**Electrical Energy Storage**

**Chapter 4**

Provisional chapter

**A Novel Highly Integrated Hybrid Energy Storage**

DOI: 10.5772/intechopen.73671

This chapter addresses potentialities and advantages of a highly integrated hybrid energy storage system (HESS) for electric propulsion and smart grids. This configuration consists of a highly integrated battery-ultracapacitor system (HIBUC) and aims to benefit from the advantages of both passive and active HESS configurations. Particularly, the integration of the ultracapacitor module (UM) within the DC-link of the DC/AC multilevel converter enables the decoupling between DC-link voltage and energy content without the need for any additional DC/DC converter. As a result, HIBUC benefits from simplicity and energy flow management capabilities very similar to those achieved by passive and active HESS configurations, respectively. This is highlighted properly by a theoretical analysis, which also accounts for a comparison between HIBUC and both passive and active HESS configurations. Some HIBUC application examples are also reported, which highlight the flexibility and potentialities of HIBUC for both electric propulsion systems and smart

Keywords: batteries, energy management, energy storage, electric vehicles, modeling,

Electric energy storage systems (ESSs) are widely recognized as one of the most promising technology for enabling the transition toward a sustainable energy system [1–3]. Particularly, transportation electrification is pushing toward progressive improvements of ESS technologies, especially for light- and heavy-road electric vehicles: these have to rely on on-board ESSs

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

A Novel Highly Integrated Hybrid Energy Storage

**System for Electric Propulsion and Smart Grid**

System for Electric Propulsion and Smart Grid

Alessandro Serpi, Mario Porru and Alfonso Damiano

Alessandro Serpi, Mario Porru and Alfonso Damiano

Additional information is available at the end of the chapter

smart grids, supercapacitors, ultracapacitors

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.73671

**Applications**

Abstract

grids.

1. Introduction

Applications

#### **A Novel Highly Integrated Hybrid Energy Storage System for Electric Propulsion and Smart Grid Applications** A Novel Highly Integrated Hybrid Energy Storage System for Electric Propulsion and Smart Grid Applications

DOI: 10.5772/intechopen.73671

Alessandro Serpi, Mario Porru and Alfonso Damiano Alessandro Serpi, Mario Porru and Alfonso Damiano

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.73671

#### Abstract

This chapter addresses potentialities and advantages of a highly integrated hybrid energy storage system (HESS) for electric propulsion and smart grids. This configuration consists of a highly integrated battery-ultracapacitor system (HIBUC) and aims to benefit from the advantages of both passive and active HESS configurations. Particularly, the integration of the ultracapacitor module (UM) within the DC-link of the DC/AC multilevel converter enables the decoupling between DC-link voltage and energy content without the need for any additional DC/DC converter. As a result, HIBUC benefits from simplicity and energy flow management capabilities very similar to those achieved by passive and active HESS configurations, respectively. This is highlighted properly by a theoretical analysis, which also accounts for a comparison between HIBUC and both passive and active HESS configurations. Some HIBUC application examples are also reported, which highlight the flexibility and potentialities of HIBUC for both electric propulsion systems and smart grids.

Keywords: batteries, energy management, energy storage, electric vehicles, modeling, smart grids, supercapacitors, ultracapacitors

#### 1. Introduction

Electric energy storage systems (ESSs) are widely recognized as one of the most promising technology for enabling the transition toward a sustainable energy system [1–3]. Particularly, transportation electrification is pushing toward progressive improvements of ESS technologies, especially for light- and heavy-road electric vehicles: these have to rely on on-board ESSs

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

for guaranteeing long mileage and short charging time. Consequently, high efficiency, low costs, small volumes, and weights are desirable. The employment of ESSs is increasingly considered also for such systems that have been propelled electrically since a long time, such as railway and ships, in order to increase system efficiency and fuel economy, as well as ensuring reliable operation of on-board power systems [4–6]. In this context, both hybrid and all-electric ships are expected to be appealing in the forthcoming future. Similarly, ESSs are a key point for designing more electric aircrafts, in which pneumatic and hydraulic actuators are being replaced with electrical ones [7]. Therefore, on-board ESSs should start the engines, maintain DC-link voltage constant over dynamic operations, and, above all, guarantee emergency power supply. ESSs can be employed successfully also for addressing several issues affecting modern power systems, such as reduced level of power quality, massive growth of distributed generation, and high penetration of renewable energy sources [8–10]. Particularly, integrating the massive and increasing share of photovoltaic and wind power plants installed all over the world is one of the main challenges for future power systems, which will be faced resorting to smart grid and microgrid concepts. In this context, ESSs are the ideal solution for mitigating power fluctuations, storing overproduction, and releasing it when required, improving overall reliability and power quality.

An ESS consists of two main stages, i.e., the power conversion system and the energy storage unit, as shown in Figure 1. The power conversion system is generally represented by a power electronic converter, which has to regulate ESS voltage and current levels in order to match application requirements. Whereas, the energy conversion occurs within the energy storage unit, which exchange electrical energy only, storing it into different forms (mechanical, chemical, magnetic, etc.).

batteries (Li-ion) are surely the best solution for modern electric vehicles due to their very highenergy density and specific energy. However, sodium-based batteries may be preferred when high capacity is required, namely for load leveling and renewable energy sources integration. Despite their advantages, electrochemical batteries generally suffer from low power capabilities; thus, even if research is focused on improving power density, they are not yet the best

Figure 2. Energy/power density (left) and specific energy/power (right) of ESSs on Ragone plots: pumped hydroelectric

A Novel Highly Integrated Hybrid Energy Storage System for Electric Propulsion and Smart Grid Applications

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81

storage system (PHS), compressed air energy storage system (CAES), and fuel cell (FC).

Differently from electrochemical batteries, high-power density ESSs can provide much little amount of energy but in very short times, which is the case of flywheel energy storage system (FESS), superconducting magnetic energy storage system (SMES), and ultracapacitors (UCs). Particularly, FESS is characterized by very high efficiency, long life expectancy, and low environmental impact. However, it presents a quite high self-discharge rate and also suffers from safety issues as far as high speeds are concerned [15–17]. Regarding SMES, a cryogenic system ensures the superconducting state of coils. Consequently, losses are due to power converters only, leading to a very high overall efficiency. Other advantages consist of fast response and wide power range, as well as long lifespan. Nevertheless, SMES is very expensive due to the high costs of both superconductors and cryogenic system; thus, although it has been recently tested for power quality and voltage stabilization in both transmission and distribution systems, it is still employed in military applications mostly [18–20]. The main advantages of UCs are high-power capability, quite long life cycle and no memory effect, but they cannot store great amount of energy unless big and costly UC modules are used. Thus, UCs have been used for power quality applications and for handling small regenerative braking on electric propul-

Based on the previous considerations, it can be stated that a single ESS technology hardly matches both energy and power application requirements. Particularly, electrochemical batteries are very suitable for providing energy services, in which high-energy storage capability is mandatory. On the other hand, FESS, SMES, and UCs are more appropriate for power services,

solution for high-power applications [13, 14].

sion systems [21–23].

There are several ESSs available on the market, which are generally classified in accordance with their energy/power density (Wh/l, W/l) or specific energy/power (Wh/Kg, W/Kg), as highlighted in Figure 2 [11, 12]. High-energy density ESSs are able to provide large amount of energy but over long time periods, as occurring for the majority of electrochemical batteries. These are the first ESS introduced on the market and still represent the most widespread. Electrochemical batteries can be further classified based on the chemical reaction they exploit. Lead acid (PbA) is probably the most-known technology; it is being widely used on vehicles for starting, lighting, and ignition purposes. PbA batteries are currently employed when sizes and weights are not an issue, such as isolated power systems and UPSs, whereas, lithium-ion

Figure 1. ESS schematic representation.

A Novel Highly Integrated Hybrid Energy Storage System for Electric Propulsion and Smart Grid Applications http://dx.doi.org/10.5772/intechopen.73671 81

for guaranteeing long mileage and short charging time. Consequently, high efficiency, low costs, small volumes, and weights are desirable. The employment of ESSs is increasingly considered also for such systems that have been propelled electrically since a long time, such as railway and ships, in order to increase system efficiency and fuel economy, as well as ensuring reliable operation of on-board power systems [4–6]. In this context, both hybrid and all-electric ships are expected to be appealing in the forthcoming future. Similarly, ESSs are a key point for designing more electric aircrafts, in which pneumatic and hydraulic actuators are being replaced with electrical ones [7]. Therefore, on-board ESSs should start the engines, maintain DC-link voltage constant over dynamic operations, and, above all, guarantee emergency power supply. ESSs can be employed successfully also for addressing several issues affecting modern power systems, such as reduced level of power quality, massive growth of distributed generation, and high penetration of renewable energy sources [8–10]. Particularly, integrating the massive and increasing share of photovoltaic and wind power plants installed all over the world is one of the main challenges for future power systems, which will be faced resorting to smart grid and microgrid concepts. In this context, ESSs are the ideal solution for mitigating power fluctuations, storing overproduction, and releasing it when required,

An ESS consists of two main stages, i.e., the power conversion system and the energy storage unit, as shown in Figure 1. The power conversion system is generally represented by a power electronic converter, which has to regulate ESS voltage and current levels in order to match application requirements. Whereas, the energy conversion occurs within the energy storage unit, which exchange electrical energy only, storing it into different forms (mechanical, chem-

There are several ESSs available on the market, which are generally classified in accordance with their energy/power density (Wh/l, W/l) or specific energy/power (Wh/Kg, W/Kg), as highlighted in Figure 2 [11, 12]. High-energy density ESSs are able to provide large amount of energy but over long time periods, as occurring for the majority of electrochemical batteries. These are the first ESS introduced on the market and still represent the most widespread. Electrochemical batteries can be further classified based on the chemical reaction they exploit. Lead acid (PbA) is probably the most-known technology; it is being widely used on vehicles for starting, lighting, and ignition purposes. PbA batteries are currently employed when sizes and weights are not an issue, such as isolated power systems and UPSs, whereas, lithium-ion

improving overall reliability and power quality.

80 Advancements in Energy Storage Technologies

ical, magnetic, etc.).

Figure 1. ESS schematic representation.

Figure 2. Energy/power density (left) and specific energy/power (right) of ESSs on Ragone plots: pumped hydroelectric storage system (PHS), compressed air energy storage system (CAES), and fuel cell (FC).

batteries (Li-ion) are surely the best solution for modern electric vehicles due to their very highenergy density and specific energy. However, sodium-based batteries may be preferred when high capacity is required, namely for load leveling and renewable energy sources integration. Despite their advantages, electrochemical batteries generally suffer from low power capabilities; thus, even if research is focused on improving power density, they are not yet the best solution for high-power applications [13, 14].

Differently from electrochemical batteries, high-power density ESSs can provide much little amount of energy but in very short times, which is the case of flywheel energy storage system (FESS), superconducting magnetic energy storage system (SMES), and ultracapacitors (UCs). Particularly, FESS is characterized by very high efficiency, long life expectancy, and low environmental impact. However, it presents a quite high self-discharge rate and also suffers from safety issues as far as high speeds are concerned [15–17]. Regarding SMES, a cryogenic system ensures the superconducting state of coils. Consequently, losses are due to power converters only, leading to a very high overall efficiency. Other advantages consist of fast response and wide power range, as well as long lifespan. Nevertheless, SMES is very expensive due to the high costs of both superconductors and cryogenic system; thus, although it has been recently tested for power quality and voltage stabilization in both transmission and distribution systems, it is still employed in military applications mostly [18–20]. The main advantages of UCs are high-power capability, quite long life cycle and no memory effect, but they cannot store great amount of energy unless big and costly UC modules are used. Thus, UCs have been used for power quality applications and for handling small regenerative braking on electric propulsion systems [21–23].

Based on the previous considerations, it can be stated that a single ESS technology hardly matches both energy and power application requirements. Particularly, electrochemical batteries are very suitable for providing energy services, in which high-energy storage capability is mandatory. On the other hand, FESS, SMES, and UCs are more appropriate for power services, when high-power rates are required for very short times. In addition, further constraints may regard efficiency, life cycle, and cost, which might make one ESS technology unsuitable. In this regard, a viable and promising solution is the employment of a hybrid energy storage system (HESS), which consists of combining high-energy and high-power density ESSs in order to benefit from the advantages of different ESS technologies [24–27]. As a result, HESS may bring increased performances, higher efficiency, longer lifetime, reduced costs, and more appropriate design and sizing. Among all the ESS combinations, HESSs made up of electrochemical batteries and UCs are the most popular and promising solutions because of the perfect complementarity between their features [28–30].

Consequently, this chapter focuses on HESS made up of a battery pack (BP) and an ultracapacitor module (UM). Particularly, a brief overview of main HESS configurations and management approaches is provided in Section 2. Then, Section 3 focuses on a highly integrated HESS configuration [31–33], in which BP and UM are coupled only by means of a multilevel converter. This highly integrated battery-ultracapacitor system (HIBUC) is also compared to those described in Section 2, highlighting its most important advantages. In Section 3, two HIBUC application examples are also presented and discussed based on numerical simulations. Concluding remarks are reported in Section 4.

#### 2. Overview on hybrid energy storage systems

Considering a hybrid energy storage system (HESS) made up of a battery pack (BP) and an ultracapacitor module (UM), a number of configurations have been proposed in the literature, which differ from each other mainly due to the number of power electronic converters involved. Particularly, no or few power electronic converters entail simple configurations, but poor flexibility and energy management. As the number of power electronic converters increases, energy management capability, and, thus, HESS exploitation increase, but at the cost of increased complexity, costs, volumes, and weights. The following sections investigate the most important HESS configurations, as well as their management and control strategies, by highlighting their most important advantages and drawbacks.

#### 2.1. HESS configurations

HESS configurations can be roughly classified into passive and active configurations; in passive HESS, BP and UM are directly coupled to the DC-link of the DC/AC converter, as depicted in Figure 3 [34, 35]. Therefore, BP and UM share the same voltage, which generally varies with BP state-of-charge. As a result, UM voltage cannot vary independently, resulting in its poor energy exploitation. Still referring to Figure 3, the following relationships can be introduced:

$$V\_b = r\_b \dot{\imath}\_b + L \frac{d\dot{\imath}\_b}{dt} + V\_{DC} \tag{1}$$

$$\mathbf{C}\_{u}\frac{dV\_{u}}{dt} = \dot{\mathbf{i}}\_{b} - \dot{\mathbf{i}}\_{P}.\tag{2}$$

Particularly, a simple but effective BP model has been considered, which consists of a voltage source Vb series-connected with an internal resistance rb, while ib is the battery current. BP is coupled to the DC-link through an inductive filter, whose inductance L should prevent ib from unsuitable sudden variations, VDC being the DC-link voltage. Referring to Eq. (2), Cu denotes the capacitance of the UM, whereas iP denotes the power current, which is proportional to the power drawn or delivered by the DC/AC converter. The DC-link energy content EDC and its

A Novel Highly Integrated Hybrid Energy Storage System for Electric Propulsion and Smart Grid Applications

EDC <sup>¼</sup> <sup>1</sup> 2 Cu V<sup>2</sup>

The comparison between Eq. (2) and Eq. (4) reveals a significant coupling between EDC and VDC, which cannot be varied within a wide range. This results in a poor UM exploitation, as well as in an unsuitable energy management, which are the main drawbacks of this HESS configuration. Consequently, in spite of its simplicity and cheapness, passive HESS configuration is rarely used due to weak performances that make it not suitable for most applications. Much better performances can be achieved by semi-active and active HESS configurations, which exploit one or more DC/DC converters for decoupling BP and UM [36–43]. Particularly, Figure 4 shows an active parallel configuration, in which BP and UM are parallel-connected to the DC-link both through DC/DC converters. Referring to this configuration, the following

dt <sup>þ</sup> VDC , C dVDC

in which, C is the capacitance of the DC-link, which is much smaller than Cu. Whereas

Vu , ~i

ð Þ Vb � rb ib , <sup>~</sup><sup>i</sup>

dt <sup>¼</sup> <sup>~</sup><sup>i</sup>

<sup>u</sup> ¼ �kuCu

<sup>b</sup> <sup>þ</sup>~<sup>i</sup>

dVu

dEDC

DC (3)

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83

<sup>u</sup> � iP (5)

<sup>b</sup> ¼ kb ib (6)

dt : (7)

dt <sup>¼</sup> VDCð Þ ib � iP : (4)

time-variation can be expressed, respectively, as:

Figure 3. Schematic representation of passive HESS configuration.

relationships can be introduced:

<sup>V</sup>~<sup>b</sup> <sup>¼</sup> <sup>L</sup> <sup>d</sup>~ib

<sup>V</sup>~<sup>b</sup> <sup>¼</sup> <sup>1</sup> kb

VDC <sup>¼</sup> <sup>1</sup> ku

A Novel Highly Integrated Hybrid Energy Storage System for Electric Propulsion and Smart Grid Applications http://dx.doi.org/10.5772/intechopen.73671 83

Figure 3. Schematic representation of passive HESS configuration.

when high-power rates are required for very short times. In addition, further constraints may regard efficiency, life cycle, and cost, which might make one ESS technology unsuitable. In this regard, a viable and promising solution is the employment of a hybrid energy storage system (HESS), which consists of combining high-energy and high-power density ESSs in order to benefit from the advantages of different ESS technologies [24–27]. As a result, HESS may bring increased performances, higher efficiency, longer lifetime, reduced costs, and more appropriate design and sizing. Among all the ESS combinations, HESSs made up of electrochemical batteries and UCs are the most popular and promising solutions because of the perfect com-

Consequently, this chapter focuses on HESS made up of a battery pack (BP) and an ultracapacitor module (UM). Particularly, a brief overview of main HESS configurations and management approaches is provided in Section 2. Then, Section 3 focuses on a highly integrated HESS configuration [31–33], in which BP and UM are coupled only by means of a multilevel converter. This highly integrated battery-ultracapacitor system (HIBUC) is also compared to those described in Section 2, highlighting its most important advantages. In Section 3, two HIBUC application examples are also presented and discussed based on numer-

Considering a hybrid energy storage system (HESS) made up of a battery pack (BP) and an ultracapacitor module (UM), a number of configurations have been proposed in the literature, which differ from each other mainly due to the number of power electronic converters involved. Particularly, no or few power electronic converters entail simple configurations, but poor flexibility and energy management. As the number of power electronic converters increases, energy management capability, and, thus, HESS exploitation increase, but at the cost of increased complexity, costs, volumes, and weights. The following sections investigate the most important HESS configurations, as well as their management and control strategies, by

HESS configurations can be roughly classified into passive and active configurations; in passive HESS, BP and UM are directly coupled to the DC-link of the DC/AC converter, as depicted in Figure 3 [34, 35]. Therefore, BP and UM share the same voltage, which generally varies with BP state-of-charge. As a result, UM voltage cannot vary independently, resulting in its poor energy exploitation. Still referring to Figure 3, the following relationships can be introduced:

Vb <sup>¼</sup> rb ib <sup>þ</sup> <sup>L</sup> dib

Cu dVu dt <sup>þ</sup> VDC (1)

dt <sup>¼</sup> ib � iP: (2)

plementarity between their features [28–30].

82 Advancements in Energy Storage Technologies

ical simulations. Concluding remarks are reported in Section 4.

2. Overview on hybrid energy storage systems

highlighting their most important advantages and drawbacks.

2.1. HESS configurations

Particularly, a simple but effective BP model has been considered, which consists of a voltage source Vb series-connected with an internal resistance rb, while ib is the battery current. BP is coupled to the DC-link through an inductive filter, whose inductance L should prevent ib from unsuitable sudden variations, VDC being the DC-link voltage. Referring to Eq. (2), Cu denotes the capacitance of the UM, whereas iP denotes the power current, which is proportional to the power drawn or delivered by the DC/AC converter. The DC-link energy content EDC and its time-variation can be expressed, respectively, as:

$$E\_{\rm DC} = \frac{1}{2} \mathbf{C}\_{\rm u} V\_{\rm DC}^2 \tag{3}$$

$$\frac{d\mathbf{E}\_{\rm DC}}{dt} = V\_{\rm DC}(\dot{\mathbf{i}}\_{\rm b} - \dot{\mathbf{i}}\_{\rm P}).\tag{4}$$

The comparison between Eq. (2) and Eq. (4) reveals a significant coupling between EDC and VDC, which cannot be varied within a wide range. This results in a poor UM exploitation, as well as in an unsuitable energy management, which are the main drawbacks of this HESS configuration. Consequently, in spite of its simplicity and cheapness, passive HESS configuration is rarely used due to weak performances that make it not suitable for most applications.

Much better performances can be achieved by semi-active and active HESS configurations, which exploit one or more DC/DC converters for decoupling BP and UM [36–43]. Particularly, Figure 4 shows an active parallel configuration, in which BP and UM are parallel-connected to the DC-link both through DC/DC converters. Referring to this configuration, the following relationships can be introduced:

$$
\tilde{V}\_b = L\frac{d\tilde{i}\_b}{dt} + V\_{D\mathbb{C}} \quad , \quad \mathbb{C}\frac{dV\_{D\mathbb{C}}}{dt} = \tilde{i}\_b + \tilde{i}\_u - i\_{\mathbb{P}} \tag{5}
$$

in which, C is the capacitance of the DC-link, which is much smaller than Cu. Whereas

$$
\tilde{V}\_b = \frac{1}{k\_b} (V\_b - r\_b \dot{\imath}\_b) \quad , \quad \tilde{\dot{\imath}}\_b = k\_b \dot{\imath}\_b \tag{6}
$$

$$V\_{DC} = \frac{1}{k\_u} V\_u \quad , \quad \tilde{\mathbf{i}}\_u = -k\_u \mathbb{C}\_u \frac{dV\_u}{dt} . \tag{7}$$

Figure 4. Schematic representation of active parallel HESS configuration.

Particularly, Eqs. (6) and (7) have been achieved by assuming ideal DC/DC converters, which have been modeled by simple gains (kb and ku for BP and UM, respectively). Therefore, the combination of Eq. (5) with Eqs. (6) and (7) leads to the following expressions:

$$dV\_b = r\_b i\_b + k\_b^2 L \frac{di\_b}{dt} + k\_b V\_{DC} \tag{8}$$

2.2. HESS management and control

ing HESS efficiency, reliability, and durability.

As far as semi-active or active HESS configurations are concerned, the management and control system cover a fundamental role for exploiting the HESS at the maximum extent. Particularly, an appropriate selection of HESS management strategy is of paramount importance, even from the design stage, especially for sizing BP and UM properly in accordance with target performances, and technical and economic constraints. It is fundamental also for assur-

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85

Referring to Figure 5, HESS management consists mainly of a sharing criterion for splitting the overall HESS energy flow between BP and UM. Literature review reveals that several approaches have been proposed in order to exploit BP and UM inherent features to the maximum extent, preventing them from unsuitable operation as well. In this regard, UM generally handles fast power fluctuations, whereas BP copes with the average power demand. This principle is the basis of the simplest HESS management strategy known as frequency-based management (FBM); this consists of splitting the overall power demand into high- and low-frequency components, which have to be tracked by UM and BP, respectively [44, 45]. An alternative approach is the so-called rule-based management (RBM), which exploits the single ESSs in accordance with an appropriate order of priority by means of a pre-set of rules [46, 47]. In this regard, it is worth noting that FBM and RBM may be combined to each other or with fuzzy logic algorithms in

order to account for ESS constraints and to improve overall HESS performances [48–51].

Figure 5. Schematic representation of an HESS management and control system.

Although FBM and RBM are intuitive, simple, and easy to implement, they generally do not lead to optimal solutions. For this reason, another popular approach is determining BP and UM reference power profiles by minimizing suitable cost functions over a given time horizon. Hence, different optimal solving techniques can be used, such as model predictive control, mixed-integer/linear programming, nonlinear programming, and dynamic programming [52–55]. However, such solving techniques are generally complex to implement and quite time-demanding. Consequently, heuristic approaches have been also proposed, like genetic algorithms and particle swarm optimization, which achieve sub-optimal solutions but faster and with less computational efforts [56–58]. As a result, very complex and sophisticated cost functions can be considered, which can account for many system constraints and goals. The

$$\mathbb{C}\frac{dV\_{DC}}{dt} = k\_b i\_b - k\_u \mathbb{C}\_u \frac{dV\_u}{dt} - i\_P. \tag{9}$$

Regarding the DC-link energy content, it should account not only for C, but also for Cu in order to make the comparison with all HESS configurations consistent. Consequently, EDC and its time derivative can be expressed as

$$E\_{\rm DC} = \frac{1}{2} \mathbf{C} V\_{\rm DC}^2 + \frac{1}{2} \mathbf{C}\_u V\_u^2 \tag{10}$$

$$\frac{dE\_{D\mathbb{C}}}{dt} = V\_{DC}(\mathbf{k}\_b \mathbf{i}\_b - \mathbf{i}\_P). \tag{11}$$

Therefore, considering both Eqs. (9) and (11), it can be seen that VDC and EDC can be controlled independently by setting the duty cycles of the DC/DC converters properly. However, Eqs. (9) and (11) are characterized by increased complexity compared to passive HESS configurations. Furthermore, these equations highlight a significant coupling among all system variables, which makes control system design a not trivial issue. Consequently, advanced management and control systems are required in order to exploit active HESS configurations properly.

#### 2.2. HESS management and control

Particularly, Eqs. (6) and (7) have been achieved by assuming ideal DC/DC converters, which have been modeled by simple gains (kb and ku for BP and UM, respectively). Therefore, the

<sup>b</sup><sup>L</sup> dib

dVu

dt <sup>þ</sup> kbVDC (8)

dt � iP: (9)

<sup>u</sup> (10)

dt <sup>¼</sup> VDCð Þ kb ib � iP : (11)

combination of Eq. (5) with Eqs. (6) and (7) leads to the following expressions:

EDC <sup>¼</sup> <sup>1</sup> 2 CV<sup>2</sup> DC þ 1 2 Cu V<sup>2</sup>

dEDC

<sup>C</sup> dVDC

Figure 4. Schematic representation of active parallel HESS configuration.

time derivative can be expressed as

84 Advancements in Energy Storage Technologies

Vb <sup>¼</sup> rb ib <sup>þ</sup> <sup>k</sup><sup>2</sup>

dt <sup>¼</sup> kb ib � kuCu

Regarding the DC-link energy content, it should account not only for C, but also for Cu in order to make the comparison with all HESS configurations consistent. Consequently, EDC and its

Therefore, considering both Eqs. (9) and (11), it can be seen that VDC and EDC can be controlled independently by setting the duty cycles of the DC/DC converters properly. However, Eqs. (9) and (11) are characterized by increased complexity compared to passive HESS configurations. Furthermore, these equations highlight a significant coupling among all system variables, which makes control system design a not trivial issue. Consequently, advanced management and control systems are required in order to exploit active HESS configurations properly.

As far as semi-active or active HESS configurations are concerned, the management and control system cover a fundamental role for exploiting the HESS at the maximum extent. Particularly, an appropriate selection of HESS management strategy is of paramount importance, even from the design stage, especially for sizing BP and UM properly in accordance with target performances, and technical and economic constraints. It is fundamental also for assuring HESS efficiency, reliability, and durability.

Referring to Figure 5, HESS management consists mainly of a sharing criterion for splitting the overall HESS energy flow between BP and UM. Literature review reveals that several approaches have been proposed in order to exploit BP and UM inherent features to the maximum extent, preventing them from unsuitable operation as well. In this regard, UM generally handles fast power fluctuations, whereas BP copes with the average power demand. This principle is the basis of the simplest HESS management strategy known as frequency-based management (FBM); this consists of splitting the overall power demand into high- and low-frequency components, which have to be tracked by UM and BP, respectively [44, 45]. An alternative approach is the so-called rule-based management (RBM), which exploits the single ESSs in accordance with an appropriate order of priority by means of a pre-set of rules [46, 47]. In this regard, it is worth noting that FBM and RBM may be combined to each other or with fuzzy logic algorithms in order to account for ESS constraints and to improve overall HESS performances [48–51].

Although FBM and RBM are intuitive, simple, and easy to implement, they generally do not lead to optimal solutions. For this reason, another popular approach is determining BP and UM reference power profiles by minimizing suitable cost functions over a given time horizon. Hence, different optimal solving techniques can be used, such as model predictive control, mixed-integer/linear programming, nonlinear programming, and dynamic programming [52–55]. However, such solving techniques are generally complex to implement and quite time-demanding. Consequently, heuristic approaches have been also proposed, like genetic algorithms and particle swarm optimization, which achieve sub-optimal solutions but faster and with less computational efforts [56–58]. As a result, very complex and sophisticated cost functions can be considered, which can account for many system constraints and goals. The

Figure 5. Schematic representation of an HESS management and control system.

main advantage of these approaches consists of enabling HESS to provide multiple services in an optimal manner, by both economic and technical points of view; this aspect makes HESS very competitive, especially for smart grid applications.

Whereas, the overall DC-link energy content can be determined easily as

dt <sup>¼</sup> ib � iDC , CDC <sup>¼</sup> CuC

dEDC

iDC iP � �

3.1.1. Comparison with passive and active HESS configurations

<sup>¼</sup> <sup>A</sup> � iH iL

� � , A <sup>¼</sup>

Hence, if Eq. (18) is satisfied, iDC and iP can differ from each other; this does not occur in conventional NPC configurations, which are characterized by equal voltages and capacitances. Whereas, HIBUC suitably exploits DC-link capacitance and voltage unbalances, leading to a

In order to highlight the advantages of HIBUC compared to passive and active HESS configurations, reference can be made to the per unit DC-link energy content (eDC), which is defined

> eDC <sup>¼</sup> EDC 1 <sup>2</sup> CV<sup>2</sup> DC

Consequently, since A must be non-singular, the following constraint is achieved:

CDC

relationship is achieved:

relationship can be introduced:

decoupled control of VDC and EDC.

as follows:

dVDC

EDC <sup>¼</sup> <sup>1</sup> 2 Cu V<sup>2</sup> <sup>u</sup> þ 1 2 CV<sup>2</sup>

Based on both Eqs. (12) and (13), the dynamic equation of the DC-link voltage can be achieved as

A Novel Highly Integrated Hybrid Energy Storage System for Electric Propulsion and Smart Grid Applications

where CDC and iDC are defined as the equivalent DC-link capacitance and current, respectively. Furthermore, by time-differentiating Eq. (14) and combining the result with (13), the following

Hence, Eq. (16) reveals that EDC depends on both ib and iP. However, since the latter is generally imposed by the application requirements, EDC can be regulated successfully through ib only, while VDC can be driven independently by means of iDC, as pointed out by Eq. (15). This occurs as far as iDC and iP differ from each other. Otherwise, both VDC and EDC time variations would be proportional to the difference between ib and iP, thus EDC and VDC control decoupling cannot be achieved. Therefore, considering both Eqs. (15) and (16), the following

dt <sup>¼</sup> VDCð Þ ib � iP , iP <sup>¼</sup> Vu

Cu <sup>þ</sup> <sup>C</sup> , iDC <sup>¼</sup> <sup>C</sup>

VDC

C Cu þ C

Vu VDC

iH þ

V VDC

Cu Cu þ C

CV � CuVu 6¼ 0 (18)

V VDC

: (14)

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Cu

Cu <sup>þ</sup> <sup>C</sup> iL (15)

87

iL: (16)

(17)

(19)

Cu <sup>þ</sup> <sup>C</sup>iH <sup>þ</sup>

#### 3. A novel highly integrated HESS

In order to overcome the issues arising from both passive and active HESS configurations, a highly integrated solution has been proposed by the authors in [31–33], whose schematic representation is depicted in Figure 6. It consists of coupling a BP with an UM through a multilevel converter, namely a three-level neutral-point-clamped converter (NPC). The key feature of the proposed highly integrated battery-ultracapacitor system (HIBUC) is the full integration of UM within the DC-link of the NPC, which decouples the overall DC-link voltage (VDC) from its energy content (EDC). As a result, HIBUC energy flow management is quite similar to that achieved with active HESS configurations without resorting to any DC/DC converter, as detailed in the following sections.

#### 3.1. HIBUC modeling

Still referring to Figure 6, the DC-link of HIBUC is split into high-side and low-side due to the three-level configuration: high-side consists of the UM, whose overall voltage and capacitance are denoted by Vu and Cu, respectively, while the low-side is made up of conventional capacitors, Vand C being the corresponding voltage and capacitance. Hence, HIBUC main equations can be expressed as

$$V\_b = r\_b i\_b + L \frac{di\_b}{dt} + V\_{DC} \quad , \quad V\_{DC} = V\_u + V \tag{12}$$

$$\mathbf{C}\_{u}\frac{dV\_{u}}{dt} = \mathbf{i}\_{b} - \mathbf{i}\_{H} \quad , \quad \mathbf{C}\frac{dV}{dt} = \mathbf{i}\_{b} - \mathbf{i}\_{L} \,. \tag{13}$$

Figure 6. The highly integrated HESS configuration proposed in [31–33].

Whereas, the overall DC-link energy content can be determined easily as

main advantage of these approaches consists of enabling HESS to provide multiple services in an optimal manner, by both economic and technical points of view; this aspect makes HESS

In order to overcome the issues arising from both passive and active HESS configurations, a highly integrated solution has been proposed by the authors in [31–33], whose schematic representation is depicted in Figure 6. It consists of coupling a BP with an UM through a multilevel converter, namely a three-level neutral-point-clamped converter (NPC). The key feature of the proposed highly integrated battery-ultracapacitor system (HIBUC) is the full integration of UM within the DC-link of the NPC, which decouples the overall DC-link voltage (VDC) from its energy content (EDC). As a result, HIBUC energy flow management is quite similar to that achieved with active HESS configurations without resorting to any DC/DC

Still referring to Figure 6, the DC-link of HIBUC is split into high-side and low-side due to the three-level configuration: high-side consists of the UM, whose overall voltage and capacitance are denoted by Vu and Cu, respectively, while the low-side is made up of conventional capacitors, Vand C being the corresponding voltage and capacitance. Hence, HIBUC main equations

dt <sup>¼</sup> ib � iH , C dV

dt <sup>þ</sup> VDC , VDC <sup>¼</sup> Vu <sup>þ</sup> <sup>V</sup> (12)

dt <sup>¼</sup> ib � iL: (13)

Vb <sup>¼</sup> rb ib <sup>þ</sup> <sup>L</sup> dib

Cu dVu

Figure 6. The highly integrated HESS configuration proposed in [31–33].

very competitive, especially for smart grid applications.

3. A novel highly integrated HESS

86 Advancements in Energy Storage Technologies

converter, as detailed in the following sections.

3.1. HIBUC modeling

can be expressed as

$$E\_{\rm DC} = \frac{1}{2} \mathbf{C}\_{\rm u} V\_{\rm u}^2 + \frac{1}{2} \mathbf{C} V^2. \tag{14}$$

Based on both Eqs. (12) and (13), the dynamic equation of the DC-link voltage can be achieved as

$$\mathbf{C}\_{\rm DC}\frac{dV\_{\rm DC}}{dt} = \mathbf{i}\_{\rm b} - \mathbf{i}\_{\rm DC} \quad , \quad \mathbf{C}\_{\rm DC} = \frac{\mathbf{C}\_{\rm u}\mathbf{C}}{\mathbf{C}\_{\rm u} + \mathbf{C}} \quad , \quad \mathbf{i}\_{\rm DC} = \frac{\mathbf{C}}{\mathbf{C}\_{\rm u} + \mathbf{C}}\mathbf{i}\_{\rm H} + \frac{\mathbf{C}\_{\rm u}}{\mathbf{C}\_{\rm u} + \mathbf{C}}\mathbf{i}\_{\rm L} \tag{15}$$

where CDC and iDC are defined as the equivalent DC-link capacitance and current, respectively. Furthermore, by time-differentiating Eq. (14) and combining the result with (13), the following relationship is achieved:

$$\frac{dE\_{D\mathbb{C}}}{dt} = V\_{DC}(\mathbf{i}\_{\mathbb{b}} - \mathbf{i}\_{\mathbb{P}}) \quad , \quad \mathbf{i}\_{\mathbb{P}} = \frac{V\_u}{V\_{DC}}\mathbf{i}\_H + \frac{V}{V\_{DC}}\mathbf{i}\_L. \tag{16}$$

Hence, Eq. (16) reveals that EDC depends on both ib and iP. However, since the latter is generally imposed by the application requirements, EDC can be regulated successfully through ib only, while VDC can be driven independently by means of iDC, as pointed out by Eq. (15). This occurs as far as iDC and iP differ from each other. Otherwise, both VDC and EDC time variations would be proportional to the difference between ib and iP, thus EDC and VDC control decoupling cannot be achieved. Therefore, considering both Eqs. (15) and (16), the following relationship can be introduced:

$$A\begin{bmatrix}\dot{i}\_{\rm DC}\\\dot{i}\_{\rm P}\end{bmatrix} = A \cdot \begin{bmatrix}\dot{i}\_{\rm H}\\\dot{i}\_{\rm L}\end{bmatrix} \quad , \quad A = \begin{bmatrix}\mathbf{C} & \mathbf{C}\_{u}\\\overline{\mathbf{C}\_{u} + \mathbf{C}} & \overline{\mathbf{C}\_{u} + \mathbf{C}}\\\overline{V}\_{\rm D\mathbf{C}} & V\\\overline{V}\_{\rm DC} & V\_{\rm DC}\end{bmatrix} \tag{17}$$

Consequently, since A must be non-singular, the following constraint is achieved:

$$\text{CV} - \text{C}\_{u}V\_{u} \neq 0 \tag{18}$$

Hence, if Eq. (18) is satisfied, iDC and iP can differ from each other; this does not occur in conventional NPC configurations, which are characterized by equal voltages and capacitances. Whereas, HIBUC suitably exploits DC-link capacitance and voltage unbalances, leading to a decoupled control of VDC and EDC.

#### 3.1.1. Comparison with passive and active HESS configurations

In order to highlight the advantages of HIBUC compared to passive and active HESS configurations, reference can be made to the per unit DC-link energy content (eDC), which is defined as follows:

$$
\varepsilon\_{\rm DC} = \frac{E\_{\rm DC}}{\frac{1}{2}\,\mathrm{CV}\_{\rm DC}^2} \tag{19}
$$

Hence, considering Eqs. (3), (10), and (14), the following results are achieved:

$$e\_{D\mathbb{C}}^{(p)} = \alpha \quad , \quad \alpha = \frac{\mathbb{C}\_u}{\mathbb{C}} \tag{20}$$

Whereas, given that α is greater than 1, the maximum eDC value is reached for ξ = 1, as pointed

\_ð Þ H

Consequently, maximum exploitation of the DC-link energy content is achieved if ξ varies as

The evolutions of both Δξ and ΔeDC with α are depicted in Figure 8. Particularly, for low α values, active HESS configuration shows superior performances compared to HIBUC, which is characterized by limited UM voltage range, and thus, poor DC-link energy exploitation. However, HIBUC performances rapidly increase with α, they becoming very close to those achieved by active HESS configurations for relatively high α values. Since α should be quite high due to the huge capacitance of UM, e.g., hundreds or even thousands, HIBUC is a very competitive solution even versus active HESS configuration because it can assure very similar performances in terms of energy flow management. In addition, HIBUC benefits also from a multilevel converter, whose increased complexity and costs are counterbalanced by improved output voltage and current waveforms due to the availability of multiple voltage

The overall HIBUC management and control scheme is depicted in Figure 9. The comparison with Figure 5 reveals some differences, especially due to the high degree of integration among all HIBUC components. Particularly, the NPC has to account for HIBUC needs in terms of energy flow management, thus it cannot be driven only in accordance with application requirements. Regarding the HIBUC management block, it has to synthesize the most suitable

) based on the chosen HIBUC management approach; this is then tracked

<sup>1</sup> <sup>þ</sup> <sup>α</sup> ! <sup>Δ</sup><sup>e</sup>

DC ¼ max 0 ≤ ξ ≤ 1

A Novel Highly Integrated Hybrid Energy Storage System for Electric Propulsion and Smart Grid Applications

ð Þ H DC ¼ e \_ð Þ H DC � e ð Þ H DC <sup>¼</sup> <sup>α</sup><sup>2</sup>

e ð Þ H

DC n o <sup>¼</sup> <sup>α</sup> (27)

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89

<sup>1</sup> <sup>þ</sup> <sup>α</sup> (28)

out in the following:

follows:

levels.

BP current profile (ib

ξ \_

�<sup>ξ</sup> <sup>¼</sup> <sup>α</sup>

Δξ ¼ ξ \_

3.2. HIBUC management and control

\*

¼ 1 ! e

Figure 7. The eDC evolutions with ξ for α = 10: passive, active, and HIBUC configurations.

$$e\_{D\mathbb{C}}^{(A)} = 1 + a\,\xi^2\,\,,\,\,\,\xi = \frac{V\_u}{V\_{D\mathbb{C}}} = k\_u\tag{21}$$

$$
\sigma\_{D\mathbb{C}}^{\langle H\rangle} = a \cdot \xi^2 + (1 - \xi)^2 \quad , \quad \xi = \frac{V\_u}{V\_{DC}} \tag{22}
$$

where the superscripts (P), (A), and (H) denote passive, active and HIBUC configuration, respectively. In addition, α is the capacitance factor, which is much greater than 1 because Cu is much greater than C. Furthermore, ξ is the UM voltage share, which is equal to ku in the case of active HESS configuration. It is worth noting that ξ ranges from 0 to 1 for the HIBUC configuration and that the same reasonably occurs also for active HESS configurations.

Hence, considering the eDC evolutions with ξ depicted in Figure 7, different considerations can be made for each HESS configuration and for a given α value. Particularly, referring to passive HESS configuration at first, eDC does not depend on ξ, as already pointed out by Eq. (20). This is because the voltage across UM always equals VDC, thus ξ = 1 over any operating conditions. As a consequence, once α has been set, EDC can vary only with VDC. However, since VDC should be kept almost constant in order to supply the converter properly, a poor UM exploitation is achieved, as expected.

Considering now active HESS configuration, minimum and maximum eDC values are always achieved for ξ = 0 and ξ = 1, respectively, as pointed out in the following:

$$\check{\xi} = 0 \qquad \rightarrow \qquad \check{e}\_{D\mathbb{C}}^{(A)} = \min\_{0 \le \xi \le 1} \left\{ e\_{D\mathbb{C}}^{(A)} \right\} = 1 \tag{23}$$

$$
\widehat{\xi} = 1 \qquad \rightarrow \qquad \widehat{e}\_{D\mathbb{C}}^{(A)} = \max\_{0 \le \xi \le 1} \left\{ e\_{D\mathbb{C}}^{(A)} \right\} = 1 + \alpha \tag{24}
$$

Therefore, the maximum exploitation of the DC-link energy content can be achieved by varying ξ within [0,1] as

$$
\Delta \underline{\xi} = \stackrel{\frown}{\xi} - \stackrel{\arrow}{\xi} = 1 \qquad \rightarrow \qquad \Delta e^{(A)}\_{D\underline{\xi}} = \stackrel{\frown}{\dot{e}}^{(A)}\_{D\underline{\xi}} - \stackrel{\arrow}{\dot{e}}^{(A)}\_{D\underline{\xi}} = \alpha \tag{25}
$$

Hence, differently from passive HESS configuration, active configuration enables the DC-link energy content to vary with ξ without the need of changing VDC. However, it is worth noting that the operating range of ξ is much narrower than [0,1] because ξ equals ku, which is constrained by DC/DC converter maximum and minimum duty cycle capabilities.

Focusing now on the HIBUC configuration, the minimum eDC value is achieved in correspondence of the following ξ value:

$$\check{\xi} = \frac{1}{1+a} \qquad \rightarrow \qquad \check{e}\_{\mathrm{DC}}^{(H)} = \min\_{0 \le \xi \le 1} \left\{ e\_{\mathrm{DC}}^{(H)} \right\} = \frac{a}{1+a} \tag{26}$$

A Novel Highly Integrated Hybrid Energy Storage System for Electric Propulsion and Smart Grid Applications http://dx.doi.org/10.5772/intechopen.73671 89

Figure 7. The eDC evolutions with ξ for α = 10: passive, active, and HIBUC configurations.

Whereas, given that α is greater than 1, the maximum eDC value is reached for ξ = 1, as pointed out in the following:

$$\widehat{\xi} = 1 \qquad \rightarrow \qquad \widehat{\mathfrak{e}}\_{D\mathbb{C}}^{(H)} = \max\_{0 \le \xi \le 1} \left\{ \mathfrak{e}\_{D\mathbb{C}}^{(H)} \right\} = \alpha \tag{27}$$

Consequently, maximum exploitation of the DC-link energy content is achieved if ξ varies as follows:

$$
\Delta\xi = \hat{\overline{\xi}} \ - \check{\overline{\xi}} = \frac{a}{1+a} \qquad \rightarrow \qquad \Delta e^{(H)}\_{\mathrm{DC}} = \hat{e}^{(H)}\_{\mathrm{DC}} - \check{e}^{(H)}\_{\mathrm{DC}} = \frac{a^2}{1+a} \tag{28}
$$

The evolutions of both Δξ and ΔeDC with α are depicted in Figure 8. Particularly, for low α values, active HESS configuration shows superior performances compared to HIBUC, which is characterized by limited UM voltage range, and thus, poor DC-link energy exploitation. However, HIBUC performances rapidly increase with α, they becoming very close to those achieved by active HESS configurations for relatively high α values. Since α should be quite high due to the huge capacitance of UM, e.g., hundreds or even thousands, HIBUC is a very competitive solution even versus active HESS configuration because it can assure very similar performances in terms of energy flow management. In addition, HIBUC benefits also from a multilevel converter, whose increased complexity and costs are counterbalanced by improved output voltage and current waveforms due to the availability of multiple voltage levels.

#### 3.2. HIBUC management and control

Hence, considering Eqs. (3), (10), and (14), the following results are achieved:

e ð Þ P

e ð Þ A

e ð Þ H

tion is achieved, as expected.

88 Advancements in Energy Storage Technologies

varying ξ within [0,1] as

dence of the following ξ value:

DC <sup>¼</sup> <sup>α</sup> , <sup>α</sup> <sup>¼</sup> Cu

DC <sup>¼</sup> <sup>α</sup> � <sup>ξ</sup><sup>2</sup> <sup>þ</sup> ð Þ <sup>1</sup> � <sup>ξ</sup> <sup>2</sup> , <sup>ξ</sup> <sup>¼</sup> Vu

where the superscripts (P), (A), and (H) denote passive, active and HIBUC configuration, respectively. In addition, α is the capacitance factor, which is much greater than 1 because Cu is much greater than C. Furthermore, ξ is the UM voltage share, which is equal to ku in the case of active HESS configuration. It is worth noting that ξ ranges from 0 to 1 for the HIBUC configuration and that the same reasonably occurs also for active HESS configurations.

Hence, considering the eDC evolutions with ξ depicted in Figure 7, different considerations can be made for each HESS configuration and for a given α value. Particularly, referring to passive HESS configuration at first, eDC does not depend on ξ, as already pointed out by Eq. (20). This is because the voltage across UM always equals VDC, thus ξ = 1 over any operating conditions. As a consequence, once α has been set, EDC can vary only with VDC. However, since VDC should be kept almost constant in order to supply the converter properly, a poor UM exploita-

Considering now active HESS configuration, minimum and maximum eDC values are always

ð Þ A DC ¼ min 0 ≤ ξ ≤ 1

Therefore, the maximum exploitation of the DC-link energy content can be achieved by

Hence, differently from passive HESS configuration, active configuration enables the DC-link energy content to vary with ξ without the need of changing VDC. However, it is worth noting that the operating range of ξ is much narrower than [0,1] because ξ equals ku, which is

Focusing now on the HIBUC configuration, the minimum eDC value is achieved in correspon-

ð Þ H DC ¼ min 0 ≤ ξ ≤ 1

DC ¼ max 0 ≤ ξ ≤ 1

\_ð Þ A

�<sup>ξ</sup> <sup>¼</sup> <sup>1</sup> ! <sup>Δ</sup><sup>e</sup>

constrained by DC/DC converter maximum and minimum duty cycle capabilities.

<sup>1</sup> <sup>þ</sup> <sup>α</sup> ! <sup>e</sup>

e ð Þ A DC n o

e ð Þ H DC n o <sup>¼</sup> <sup>α</sup>

e ð Þ A DC n o

ð Þ A DC ¼ e \_ð Þ A DC � e ð Þ A

achieved for ξ = 0 and ξ = 1, respectively, as pointed out in the following:

<sup>ξ</sup> <sup>¼</sup> <sup>0</sup> ! <sup>e</sup>

¼ 1 ! e

ξ \_

Δξ ¼ ξ \_

<sup>ξ</sup> <sup>¼</sup> <sup>1</sup>

VDC

VDC

DC <sup>¼</sup> <sup>1</sup> <sup>þ</sup> α ξ<sup>2</sup> , <sup>ξ</sup> <sup>¼</sup> Vu

<sup>C</sup> (20)

¼ ku (21)

¼ 1 (23)

¼ 1 þ α (24)

DC ¼ α (25)

<sup>1</sup> <sup>þ</sup> <sup>α</sup> (26)

(22)

The overall HIBUC management and control scheme is depicted in Figure 9. The comparison with Figure 5 reveals some differences, especially due to the high degree of integration among all HIBUC components. Particularly, the NPC has to account for HIBUC needs in terms of energy flow management, thus it cannot be driven only in accordance with application requirements. Regarding the HIBUC management block, it has to synthesize the most suitable BP current profile (ib \* ) based on the chosen HIBUC management approach; this is then tracked

during regenerative braking. In this regard, different maps can be chosen depending on UM sizing and on the kind of assistance it has to provide to BP in supplying the traction motor over

A Novel Highly Integrated Hybrid Energy Storage System for Electric Propulsion and Smart Grid Applications

Once EDC\* has been set, its tracking can be accomplished through a PI regulator (RE), which

Then, in order to overcome the dependence of Pb from VDC, it is possible to multiply (12) by ib

1 2 L di2 b

in which, the magnetic energy variation related to the inductive filter can be neglected safely due to the relative low inductance value. As a result, the reference ib value can be computed as

s

Regarding the HIBUC control block, it consists of two nested control loops: the external loop regulates ib through VDC by means of a PI regulator, which can be designed easily based on Eq. (12). As a result, a reference DC-link voltage profile is achieved, whose tracking is demanded to the internal loop; this determines the most suitable reference iDC profile by

In order to highlight the effectiveness of the proposed configuration, a simulation study has been performed in MATLAB-Simulink, whose main parameters are reported in Table 1. While simulations results are depicted in Figures 11–15. Particularly, each variable is shown in per unit with reference to the corresponding base value shown in Table 1. Focusing on Figure 11 at first, different speed profiles have been considered for simulating a start and stop of the vehicle, which are characterized by decreasing ramp times (5 s for case 1, 4 s for case 2, and 3 s for case 3). While the corresponding motor torque evolutions are depicted in Figure 12. The latter reveals higher torque demands as soon as faster speed variations are required, namely from case 1 to case 3, as expected. Considering the current evolutions shown in Figure 13, it

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Vb 2rb � �<sup>2</sup>

� P∗ b rb

dt <sup>¼</sup> Pb � PP , Pb <sup>¼</sup> VDCib , PP <sup>¼</sup> VDCiP: (29)

dt <sup>≃</sup>ibð Þ Vb � rb ib (30)

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91

: (31)

these operating conditions, as pointed out in [32].

dEDC

follows:

can be designed in accordance with Eq. (16), but expressed as

Figure 10. The HIBUC management and control scheme for an electric propulsion system.

and substituting the result in Eq. (29), leading to the following expression:

Pb ¼ ibð Þ� Vb � rb ib

i ∗ <sup>b</sup> <sup>¼</sup> Vb 2rb �

means of another PI regulator, which is designed in accordance with Eq. (15).

Figure 8. The evolutions of both Δξ (dashed lines) and ΔeDC (solid lines) with α: active and HIBUC configurations.

Figure 9. General overview of the HIBUC management and control scheme.

by means of the HIBUC control system, which can be designed in accordance with both Eqs. (12) and (15). As a result, a suitable iDC\* is achieved, whose implementation is guaranteed by means of advanced PWM patterns that account for both HIBUC and application requirements, as well detailed in [33, 59, 60].

In conclusion, the structure of both HIBUC management and control blocks generally depends on the specific application, as well as on the kind of power and energy services HIBUC has to provide. For this reason, two application examples are reported in the following, for each of which a much in-depth analysis of these blocks is presented, as well as some simulation results.

#### 3.2.1. Electric propulsion system

The HIBUC management and control blocks for the highly integrated electric propulsion system proposed in [31–33] are depicted in Figure 10. Focusing on the HIBUC energy management at first, the idea is to exploit the UM over acceleration and regenerative braking mainly. Consequently, the reference EDC profile is set in accordance with the actual motor/ vehicle speed (ωm), namely EDC should decrease properly as ω<sup>m</sup> increases in order to enable UM to release its energy content gradually during acceleration, as well as storing it back

A Novel Highly Integrated Hybrid Energy Storage System for Electric Propulsion and Smart Grid Applications http://dx.doi.org/10.5772/intechopen.73671 91

Figure 10. The HIBUC management and control scheme for an electric propulsion system.

by means of the HIBUC control system, which can be designed in accordance with both Eqs. (12) and (15). As a result, a suitable iDC\* is achieved, whose implementation is guaranteed by means of advanced PWM patterns that account for both HIBUC and application require-

Figure 8. The evolutions of both Δξ (dashed lines) and ΔeDC (solid lines) with α: active and HIBUC configurations.

In conclusion, the structure of both HIBUC management and control blocks generally depends on the specific application, as well as on the kind of power and energy services HIBUC has to provide. For this reason, two application examples are reported in the following, for each of which a much in-depth analysis of these blocks is presented, as well as some simulation

The HIBUC management and control blocks for the highly integrated electric propulsion system proposed in [31–33] are depicted in Figure 10. Focusing on the HIBUC energy management at first, the idea is to exploit the UM over acceleration and regenerative braking mainly. Consequently, the reference EDC profile is set in accordance with the actual motor/ vehicle speed (ωm), namely EDC should decrease properly as ω<sup>m</sup> increases in order to enable UM to release its energy content gradually during acceleration, as well as storing it back

ments, as well detailed in [33, 59, 60].

90 Advancements in Energy Storage Technologies

Figure 9. General overview of the HIBUC management and control scheme.

3.2.1. Electric propulsion system

results.

during regenerative braking. In this regard, different maps can be chosen depending on UM sizing and on the kind of assistance it has to provide to BP in supplying the traction motor over these operating conditions, as pointed out in [32].

Once EDC\* has been set, its tracking can be accomplished through a PI regulator (RE), which can be designed in accordance with Eq. (16), but expressed as

$$\frac{d E\_{\rm DC}}{dt} = P\_b - P\_{\rm P} \quad , \quad P\_b = V\_{\rm DC} \dot{\mathbf{i}}\_b \quad , \quad P\_{\rm P} = V\_{\rm DC} \dot{\mathbf{i}}\_{\rm P} \,. \tag{29}$$

Then, in order to overcome the dependence of Pb from VDC, it is possible to multiply (12) by ib and substituting the result in Eq. (29), leading to the following expression:

$$P\_b = \dot{\mathbf{i}}\_b (V\_b - r\_b \ \dot{\mathbf{i}}\_b) - \frac{1}{2} \mathbf{L} \frac{d\dot{\mathbf{i}}\_b^2}{dt} \approx \dot{\mathbf{i}}\_b (V\_b - r\_b \ \dot{\mathbf{i}}\_b) \tag{30}$$

in which, the magnetic energy variation related to the inductive filter can be neglected safely due to the relative low inductance value. As a result, the reference ib value can be computed as follows:

$$
\dot{q}\_b^\* = \frac{V\_b}{2r\_b} - \sqrt{\left(\frac{V\_b}{2r\_b}\right)^2 - \frac{P\_b^\*}{r\_b}}.\tag{31}
$$

Regarding the HIBUC control block, it consists of two nested control loops: the external loop regulates ib through VDC by means of a PI regulator, which can be designed easily based on Eq. (12). As a result, a reference DC-link voltage profile is achieved, whose tracking is demanded to the internal loop; this determines the most suitable reference iDC profile by means of another PI regulator, which is designed in accordance with Eq. (15).

In order to highlight the effectiveness of the proposed configuration, a simulation study has been performed in MATLAB-Simulink, whose main parameters are reported in Table 1. While simulations results are depicted in Figures 11–15. Particularly, each variable is shown in per unit with reference to the corresponding base value shown in Table 1. Focusing on Figure 11 at first, different speed profiles have been considered for simulating a start and stop of the vehicle, which are characterized by decreasing ramp times (5 s for case 1, 4 s for case 2, and 3 s for case 3). While the corresponding motor torque evolutions are depicted in Figure 12. The latter reveals higher torque demands as soon as faster speed variations are required, namely from case 1 to case 3, as expected. Considering the current evolutions shown in Figure 13, it


Table 1. Parameters and rated values of the electric propulsion system.

Figure 11. Motor speed evolution achieved over vehicle start and stop.

Figure 12. Motor torque evolution achieved over vehicle start and stop.

can be seen that high power demands occur during both vehicle acceleration and braking, especially in comparison with steady state vehicle power requirements. However, BP is prevented from coping with such high and fast power variations, as proved by the low battery current profiles achieved in all cases and still shown in Figure 13. This is due to UM, which is discharged and charged appropriately in accordance with the (ωm, EDC\* ) map, as highlighted in Figure 14. In addition, Figure 15 shows that Vu is reduced during vehicle acceleration, but V is increased simultaneously. Consequently, VDC can be kept sufficiently high in order to prevent unsuitable and fast BP current variations. Similar considerations can be made during regenerative braking, in correspondence of which UM is recharged, while BP current is slowly driven to zero. As a result, HIBUC enables the UM to supply the motor on its own mostly during both vehicle acceleration and regenerative braking, thus preventing BP from an unsuitable exploitation.

3.2.2. Smart grid

Figure 14. DC-link energy variations over vehicle start and stop.

tracking of both ib

The HIBUC management and control blocks for a smart grid are depicted in Figure 16. Particularly, it differs from Figure 10 only in terms of HIBUC management because the

Figure 15. DC-link voltage evolutions over vehicle start and stop: VDC (gray), Vu (red), and V (blue).

Figure 13. Power and battery current evolutions over vehicle start and stop: iP (green) and ib (gold).

A Novel Highly Integrated Hybrid Energy Storage System for Electric Propulsion and Smart Grid Applications

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on the HIBUC management only, it can be seen that a frequency-based management approach has been followed [61], namely the smart grid reference active power profile (Pe\*) is processed by an appropriate low-pass filter in order to extract low-frequency components only. These are further processed by the energy management block, which has to synthesize the reference BP

\* and VDC\* does not depend on the specific application. Therefore, focusing

A Novel Highly Integrated Hybrid Energy Storage System for Electric Propulsion and Smart Grid Applications http://dx.doi.org/10.5772/intechopen.73671 93

Figure 13. Power and battery current evolutions over vehicle start and stop: iP (green) and ib (gold).

Figure 14. DC-link energy variations over vehicle start and stop.

Figure 15. DC-link voltage evolutions over vehicle start and stop: VDC (gray), Vu (red), and V (blue).

#### 3.2.2. Smart grid

can be seen that high power demands occur during both vehicle acceleration and braking, especially in comparison with steady state vehicle power requirements. However, BP is prevented from coping with such high and fast power variations, as proved by the low battery current profiles achieved in all cases and still shown in Figure 13. This is due to UM, which is

Te,n ωm,n Pm,n Vb rb Lb Ib Cu C

Value 110 3500 40.3 450 0.4 15 89.6 1.7 1.6 Units Nm rpm kW V Ω mH A F mF

in Figure 14. In addition, Figure 15 shows that Vu is reduced during vehicle acceleration, but V is increased simultaneously. Consequently, VDC can be kept sufficiently high in order to prevent unsuitable and fast BP current variations. Similar considerations can be made during regenerative braking, in correspondence of which UM is recharged, while BP current is slowly driven to zero. As a result, HIBUC enables the UM to supply the motor on its own mostly during both vehicle acceleration and regenerative braking, thus preventing BP from an

) map, as highlighted

discharged and charged appropriately in accordance with the (ωm, EDC\*

unsuitable exploitation.

EPS parameters and rated values

92 Advancements in Energy Storage Technologies

Table 1. Parameters and rated values of the electric propulsion system.

Figure 11. Motor speed evolution achieved over vehicle start and stop.

Figure 12. Motor torque evolution achieved over vehicle start and stop.

The HIBUC management and control blocks for a smart grid are depicted in Figure 16. Particularly, it differs from Figure 10 only in terms of HIBUC management because the tracking of both ib \* and VDC\* does not depend on the specific application. Therefore, focusing on the HIBUC management only, it can be seen that a frequency-based management approach has been followed [61], namely the smart grid reference active power profile (Pe\*) is processed by an appropriate low-pass filter in order to extract low-frequency components only. These are further processed by the energy management block, which has to synthesize the reference BP

Figure 16. The HIBUC management and control scheme for a smart grid.

power profile in accordance with BP energy and power constraints. As a result, the UM has to cope with high-frequency power components only, which are generally characterized by poor energy content. However, since UM has to compensate for system losses, forecasting errors, and relatively low BP dynamic performances, the energy management block accounts also for the DC-link and, thus, the UM energy content in defining the reference BP power profile. Hence, if UM energy level is too low, additional power is delivered by BP with the aim of restoring an intermediate UM energy content. The opposite occurs when UM is almost fully charged, namely BP should draw energy from UM in order to preserve its continuous operation. As a result, the HESS configuration is exploited properly, not only by differentiating the kind of services BP and UM have to provide, but also by enabling a mutual support between the single ESSs.

A simulation study has been carried out in MATLAB-Simulink with reference to the main parameters shown in Table 2. Particularly, HIBUC has been sized differently from the previous application (electric propulsion system), especially in terms of voltage and capacitance ratings, in order to better match the new application requirements. Simulation results are depicted in Figures 17–21; all the results are expressed in per unit with reference to the corresponding base values shown in Table 2. Simulations refer to several active and/or reactive smart grid power variations, as highlighted in Figure 17. In addition, after 3 s, a noise signal has been added to the reference active power in order to test HIBUC performances in preventing BP from coping with such high-frequency power fluctuations. Still focusing on Figure 17, it can be seen that a very fast and suitable reference power tracking is achieved. This is mainly due to the fast UM dynamic response, while a much slower BP power response is achieved due to the employment of a low-pass filter, as highlighted in Figure 18. Particularly, the low-pass filter prevents BP from quickly reacting to reference active power variations, as well as from coping with the noise signal added to the reference active power; this is handled by UM on its own mostly, as still highlighted in Figure 18.

Focusing now on the BP reference power profile, it is made up of two contributions, as pointed

is provided by a DC-link energy loop similar to that shown in Figure 10. Particularly, δPb\* enables BP to slowly drive the UM energy to a suitable intermediate reference value, as shown in Figure 20. This occurs by varying Vu and V suitably, as highlighted in Figure 21. It is worth noting that UM may be fully charged or discharged if this control loop is not employed and, thus, unable to cope with fast active power decrease or increase, respectively. In addition, δPb

accounts also for system losses that, if ignored, would force UM to be fully discharged. In conclusion, it is also worthy of note that reactive power variations do not affect both BP and UM power profiles significantly, as highlighted by the comparison between Figure 17 and

\*

\* (red), δPb

A Novel Highly Integrated Hybrid Energy Storage System for Electric Propulsion and Smart Grid Applications

\* (gold), and Pb

), whereas the other contribution (δPb

http://dx.doi.org/10.5772/intechopen.73671

95

̅ \* (pink).

> \* )

> > \*

out in Figure 19: one comes from the low-pass filter (P̅<sup>b</sup>

Figure 19. Reference active power components provided by BP: Pb

Figure 17. Active and reactive power profiles of the smart grid.

Figure 18. Active powers: PP (green), Pb (red), and PDC (pink).

Figure 18.


Table 2. Parameters and rated values of the smart grid scenario.

A Novel Highly Integrated Hybrid Energy Storage System for Electric Propulsion and Smart Grid Applications http://dx.doi.org/10.5772/intechopen.73671 95

Figure 17. Active and reactive power profiles of the smart grid.

power profile in accordance with BP energy and power constraints. As a result, the UM has to cope with high-frequency power components only, which are generally characterized by poor energy content. However, since UM has to compensate for system losses, forecasting errors, and relatively low BP dynamic performances, the energy management block accounts also for the DC-link and, thus, the UM energy content in defining the reference BP power profile. Hence, if UM energy level is too low, additional power is delivered by BP with the aim of restoring an intermediate UM energy content. The opposite occurs when UM is almost fully charged, namely BP should draw energy from UM in order to preserve its continuous operation. As a result, the HESS configuration is exploited properly, not only by differentiating the kind of services BP and UM have to provide, but also by enabling a mutual support between

Figure 16. The HIBUC management and control scheme for a smart grid.

94 Advancements in Energy Storage Technologies

A simulation study has been carried out in MATLAB-Simulink with reference to the main parameters shown in Table 2. Particularly, HIBUC has been sized differently from the previous application (electric propulsion system), especially in terms of voltage and capacitance ratings, in order to better match the new application requirements. Simulation results are depicted in Figures 17–21; all the results are expressed in per unit with reference to the corresponding base values shown in Table 2. Simulations refer to several active and/or reactive smart grid power variations, as highlighted in Figure 17. In addition, after 3 s, a noise signal has been added to the reference active power in order to test HIBUC performances in preventing BP from coping with such high-frequency power fluctuations. Still focusing on Figure 17, it can be seen that a very fast and suitable reference power tracking is achieved. This is mainly due to the fast UM dynamic response, while a much slower BP power response is achieved due to the employment of a low-pass filter, as highlighted in Figure 18. Particularly, the low-pass filter prevents BP from quickly reacting to reference active power variations, as well as from coping with the noise signal added to the reference active power; this is handled by UM on its own

Pe,n Vline fline Vb rb Lb Ib Cu C

Value 40 230 50 1000 0.4 15 40 141 0.94 Units kW Vrms Hz V Ω mH A mF mF

the single ESSs.

mostly, as still highlighted in Figure 18.

Table 2. Parameters and rated values of the smart grid scenario.

MG parameters and rated values

Figure 18. Active powers: PP (green), Pb (red), and PDC (pink).

Figure 19. Reference active power components provided by BP: Pb \* (red), δPb \* (gold), and Pb ̅ \* (pink).

Focusing now on the BP reference power profile, it is made up of two contributions, as pointed out in Figure 19: one comes from the low-pass filter (P̅<sup>b</sup> \* ), whereas the other contribution (δPb \* ) is provided by a DC-link energy loop similar to that shown in Figure 10. Particularly, δPb\* enables BP to slowly drive the UM energy to a suitable intermediate reference value, as shown in Figure 20. This occurs by varying Vu and V suitably, as highlighted in Figure 21. It is worth noting that UM may be fully charged or discharged if this control loop is not employed and, thus, unable to cope with fast active power decrease or increase, respectively. In addition, δPb \* accounts also for system losses that, if ignored, would force UM to be fully discharged. In conclusion, it is also worthy of note that reactive power variations do not affect both BP and UM power profiles significantly, as highlighted by the comparison between Figure 17 and Figure 18.

Author details

References

2 NEPSY srl, Cagliari, Italy

Press; 2011. 254p

2015. 200p

Alessandro Serpi1,2\*, Mario Porru1,2 and Alfonso Damiano<sup>1</sup>

\*Address all correspondence to: alessandro.serpi@diee.unica.it

[1] Huggins RA. Energy Storage. Boston, MA: Springer US; 2010

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1 Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy

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[2] Barnes FS, Levine JG. Large Energy Storage Systems Handbook. Boca Raton, FL: CRC

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[9] Gao DW. Energy Storage for Sustainable Microgrid. Oxford, UK: Academic Press; 2015. 153p [10] Tan X, Li Q, Wang H. Advances and trends of energy storage technology in microgrid. International Journal of Electrical Power & Energy Systems. 2013 Jan;44(1):179-191 [11] Zakeri B, Syri S. Electrical energy storage systems: A comparative life cycle cost analysis.

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Figure 20. DC-link energy variations.

Figure 21. DC-link voltages: VDC (gray), Vu (red), and V (blue).

#### 4. Conclusion

This chapter has addressed the potentialities and advantages of a highly integrated batteryultracapacitor system (HIBUC). Particularly, HIBUC benefits from the advantages of both passive and active hybrid energy storage system (HESS) configurations, namely a relative simple structure and an effective energy flow management of both the battery pack (BP) and the ultracapacitor module (UM). This has been proved by the analytical comparison among all the above-mentioned configurations, which reveals HIBUC as a very suitable and competitive solution, especially when high UM capacitance is concerned. In addition, HIBUC is also very flexible, as proved by the two application examples, namely an electric propulsion system and a smart grid. For each of these, HIBUC management and control approaches have been presented and discussed, providing some simulation results as well. These highlight the high level of integration achieved in each application, especially in the design of the HIBUC management stage, which should be done in accordance with the specific application requirements.

#### Acknowledgements

This work has been developed within the project "Design and Implementation of a Novel Hybrid Energy Storage System for Microgrids," which is funded by the Sardinian Regional Government (Regional Law no. 7, August 7, 2007) under the Grant Agreement n68 (Annuity 2015).

#### Author details

Alessandro Serpi1,2\*, Mario Porru1,2 and Alfonso Damiano<sup>1</sup>


#### References

4. Conclusion

Figure 20. DC-link energy variations.

96 Advancements in Energy Storage Technologies

Figure 21. DC-link voltages: VDC (gray), Vu (red), and V (blue).

Acknowledgements

This chapter has addressed the potentialities and advantages of a highly integrated batteryultracapacitor system (HIBUC). Particularly, HIBUC benefits from the advantages of both passive and active hybrid energy storage system (HESS) configurations, namely a relative simple structure and an effective energy flow management of both the battery pack (BP) and the ultracapacitor module (UM). This has been proved by the analytical comparison among all the above-mentioned configurations, which reveals HIBUC as a very suitable and competitive solution, especially when high UM capacitance is concerned. In addition, HIBUC is also very flexible, as proved by the two application examples, namely an electric propulsion system and a smart grid. For each of these, HIBUC management and control approaches have been presented and discussed, providing some simulation results as well. These highlight the high level of integration achieved in each application, especially in the design of the HIBUC management stage, which should be done in accordance with the specific application requirements.

This work has been developed within the project "Design and Implementation of a Novel Hybrid Energy Storage System for Microgrids," which is funded by the Sardinian Regional Government

(Regional Law no. 7, August 7, 2007) under the Grant Agreement n68 (Annuity 2015).


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**Chapter 5**

Provisional chapter

**Synergetic Control of a Hybrid Battery-Ultracapacitor**

DOI: 10.5772/intechopen.73673

This chapter presents a synergy-based cascade control scheme for a hybrid batteryultracapacitor (UC) energy storage system. The purpose is to improve the dynamic response of the battery-based energy storage system using an ultracapacitor module as an auxiliary energy storage unit. A bidirectional DC-DC converter is designed to interface between the ultracapacitor module and the main DC-bus. The control scheme is based on a fast inner current control loop using sliding mode control and an outer loop for DC-bus voltage regulation using synergy-based control. The improvement in performance is demonstrated through simulation and experiments. The results show that the DC-bus voltage is well regulated under external load disturbances with fast dynamic transients. The ultracapacitor module is able to absorb the sudden load variations and limit the battery power requirements by maintaining an optimal power balance between the two embedded storage units. The performance of the proposed synergy-based controller is compared with the standard PI controller, and its ability to achieve optimal transient

Keywords: DC-DC converter, hybrid energy storage system, synergetic control,

The rapid development of the automotive industry has resulted in a variety of technological enhancements in electric vehicles (EV), which have significantly improved fuel consumption and reduced emissions. However, EV technology still faces many challenges such as long drive

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

Synergetic Control of a Hybrid Battery-Ultracapacitor

**Energy Storage System**

Energy Storage System

http://dx.doi.org/10.5772/intechopen.73673

performance is verified.

ultracapacitor

1. Introduction

Abstract

Rached Dhaouadi, Kamyar Khosravi and Yoichi Hori

Rached Dhaouadi, Kamyar Khosravi and Yoichi Hori

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

Provisional chapter

#### **Synergetic Control of a Hybrid Battery-Ultracapacitor Energy Storage System** Synergetic Control of a Hybrid Battery-Ultracapacitor Energy Storage System

DOI: 10.5772/intechopen.73673

Rached Dhaouadi, Kamyar Khosravi and Yoichi Hori Rached Dhaouadi, Kamyar Khosravi and Yoichi Hori

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.73673

#### Abstract

This chapter presents a synergy-based cascade control scheme for a hybrid batteryultracapacitor (UC) energy storage system. The purpose is to improve the dynamic response of the battery-based energy storage system using an ultracapacitor module as an auxiliary energy storage unit. A bidirectional DC-DC converter is designed to interface between the ultracapacitor module and the main DC-bus. The control scheme is based on a fast inner current control loop using sliding mode control and an outer loop for DC-bus voltage regulation using synergy-based control. The improvement in performance is demonstrated through simulation and experiments. The results show that the DC-bus voltage is well regulated under external load disturbances with fast dynamic transients. The ultracapacitor module is able to absorb the sudden load variations and limit the battery power requirements by maintaining an optimal power balance between the two embedded storage units. The performance of the proposed synergy-based controller is compared with the standard PI controller, and its ability to achieve optimal transient performance is verified.

Keywords: DC-DC converter, hybrid energy storage system, synergetic control, ultracapacitor

#### 1. Introduction

The rapid development of the automotive industry has resulted in a variety of technological enhancements in electric vehicles (EV), which have significantly improved fuel consumption and reduced emissions. However, EV technology still faces many challenges such as long drive

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

distribution, and eproduction in any medium, provided the original work is properly cited.

range, long battery operating life, and high charge–discharge cycle rate in order to recover as much of the vehicle's kinetic energy as possible and supply high peak energies on demand [1–4].

This chapter presents a new control scheme to improve the dynamic response of a batterybased energy storage system using an UC module as an auxiliary energy storage unit. This chapter represents a preliminary study for EV applications. The primary objective is to improve both the vehicle range and the battery cycle life through optimal management of the onboard power and energy, and realize full utilization of the installed storage capacities.

Synergetic Control of a Hybrid Battery-Ultracapacitor Energy Storage System

http://dx.doi.org/10.5772/intechopen.73673

105

The originality of the proposed technique is the procedure to develop the synergy-based cascade control scheme and to devise the link between the system variables to have an accurate control of the DC-bus voltage and an optimal management of the power flow between the battery, UC module, and load. Additionally, our contribution extends the analysis of the synergy-based cascade control scheme by providing a proof of the controller stability using

A prototype hybrid battery/UC system is developed to perform experimental analysis and validate the proposed controller. Experimental results and a comparison with the standard PI controller are given to validate the optimal transient performance of the synergy-based controller. The proposed synergy-based control scheme is shown to have the following characteristics:

• Synergetic control improves the dynamic response of the UC energy storage system.

• The control scheme maintains an optimal power balance between the storage units.

• Synergy-based control is robust to external load disturbances and UC voltage variation.

Figure 1 shows the proposed topology used for electric vehicles. The system has a DC-coupled structure where a UC module is used as an auxiliary power source and connected to the

• UC absorbs sudden load variations and limits battery power requirements.

Lyapunov theory.

2. Hybrid energy storage system

Figure 1. Topology of the hybrid energy storage system.

For the EV operation, it is important to predict the battery energy demand for a specific trip. However, the stochastic driving cycles and unpredictable power demand may lead to a fast discharge action of batteries, resulting in an energy shortage to complete the given trip. A backup energy storage unit is therefore necessary to supply a stable and reliable power to the vehicle and improve the steady-state and dynamic behavior under different operating conditions [5, 6].

Ultracapacitors (UCs) are nowadays recognized as a viable auxiliary power source with outstanding power characteristics. They have been integrated successfully with energy storage systems for many industrial applications such as electric vehicles and photovoltaic energy systems [7–13]. The inclusion of UC can be very useful to maintain stability in electrical power systems with distributed generation by enhancing the output from lead-acid batteries and intermittent renewable resources.

In electric vehicles, the main power source is usually a lithium-ion battery, or a fuel cell, and the mechanical load is coupled to a permanent-magnet synchronous machine (PMSM) through an inverter. To extend the driving range of the vehicle and enable more efficient use of the batteries, a UC module is used as an auxiliary power source connected to the DC-bus through a bidirectional DC-DC converter. This configuration allows obtaining an optimized charge/ discharge operation to smooth the power fluctuations and reinforce the DC-bus during the load transients [14–17].

During the last decade, different control techniques based on adaptive control theory, sliding mode control, fuzzy logic, and neural networks have been proposed for the control of DC-DC power converters [18, 19]. The main objective of such nonlinear controllers is to provide the control support for boost-type converters to improve their controllability and performance for large operating ranges.

Recently, the synergetic control appears to be a novel effective approach to deal with many nonlinear control problems due to its optimality property and its inherent robustness to disturbances. The synergetic control was developed by Kolesnikov et al. [20] on the basis of the standard variable structure control. The method was later applied to a number of industrial processes, including problems in energy conversion [21–25]. In [24], the authors presented the optimization characteristic of the synergetic control method and showed that the control law can be derived using the analytical design of aggregated regulators (ADAR) method and calculus of variation principles.

The main features of synergetic control are that it is well-suited for digital implementation; it gives constant switching frequency operation and gives better control of the off-manifold dynamics. Switching converters have intrinsic nonlinear and time-varying characteristics, which make the synergetic controller to also be a well-suited control scheme. The other important advantages of this control approach are order reduction, decoupling design procedure, and insensitivity to parameter variation.

This chapter presents a new control scheme to improve the dynamic response of a batterybased energy storage system using an UC module as an auxiliary energy storage unit. This chapter represents a preliminary study for EV applications. The primary objective is to improve both the vehicle range and the battery cycle life through optimal management of the onboard power and energy, and realize full utilization of the installed storage capacities.

The originality of the proposed technique is the procedure to develop the synergy-based cascade control scheme and to devise the link between the system variables to have an accurate control of the DC-bus voltage and an optimal management of the power flow between the battery, UC module, and load. Additionally, our contribution extends the analysis of the synergy-based cascade control scheme by providing a proof of the controller stability using Lyapunov theory.

A prototype hybrid battery/UC system is developed to perform experimental analysis and validate the proposed controller. Experimental results and a comparison with the standard PI controller are given to validate the optimal transient performance of the synergy-based controller.

The proposed synergy-based control scheme is shown to have the following characteristics:


#### 2. Hybrid energy storage system

range, long battery operating life, and high charge–discharge cycle rate in order to recover as much of the vehicle's kinetic energy as possible and supply high peak energies on demand [1–4]. For the EV operation, it is important to predict the battery energy demand for a specific trip. However, the stochastic driving cycles and unpredictable power demand may lead to a fast discharge action of batteries, resulting in an energy shortage to complete the given trip. A backup energy storage unit is therefore necessary to supply a stable and reliable power to the vehicle and improve the steady-state and dynamic behavior under different operating condi-

Ultracapacitors (UCs) are nowadays recognized as a viable auxiliary power source with outstanding power characteristics. They have been integrated successfully with energy storage systems for many industrial applications such as electric vehicles and photovoltaic energy systems [7–13]. The inclusion of UC can be very useful to maintain stability in electrical power systems with distributed generation by enhancing the output from lead-acid batteries and

In electric vehicles, the main power source is usually a lithium-ion battery, or a fuel cell, and the mechanical load is coupled to a permanent-magnet synchronous machine (PMSM) through an inverter. To extend the driving range of the vehicle and enable more efficient use of the batteries, a UC module is used as an auxiliary power source connected to the DC-bus through a bidirectional DC-DC converter. This configuration allows obtaining an optimized charge/ discharge operation to smooth the power fluctuations and reinforce the DC-bus during the

During the last decade, different control techniques based on adaptive control theory, sliding mode control, fuzzy logic, and neural networks have been proposed for the control of DC-DC power converters [18, 19]. The main objective of such nonlinear controllers is to provide the control support for boost-type converters to improve their controllability and performance for

Recently, the synergetic control appears to be a novel effective approach to deal with many nonlinear control problems due to its optimality property and its inherent robustness to disturbances. The synergetic control was developed by Kolesnikov et al. [20] on the basis of the standard variable structure control. The method was later applied to a number of industrial processes, including problems in energy conversion [21–25]. In [24], the authors presented the optimization characteristic of the synergetic control method and showed that the control law can be derived using the analytical design of aggregated regulators (ADAR) method and

The main features of synergetic control are that it is well-suited for digital implementation; it gives constant switching frequency operation and gives better control of the off-manifold dynamics. Switching converters have intrinsic nonlinear and time-varying characteristics, which make the synergetic controller to also be a well-suited control scheme. The other important advantages of this control approach are order reduction, decoupling design proce-

tions [5, 6].

intermittent renewable resources.

104 Advancements in Energy Storage Technologies

load transients [14–17].

large operating ranges.

calculus of variation principles.

dure, and insensitivity to parameter variation.

Figure 1 shows the proposed topology used for electric vehicles. The system has a DC-coupled structure where a UC module is used as an auxiliary power source and connected to the

Figure 1. Topology of the hybrid energy storage system.

DC-bus through a bidirectional DC-DC converter. The proposed hybrid energy storage system is designed to have high efficiency and regenerative energy capture capability. These two features represent the key elements with respect to energy saving in electric vehicles. The battery is the main DC power source that forms the DC-bus. Various loads including the AC drive motors and auxiliary electrical loads are fed from the DC-bus through DC-AC and DC-DC converters. The AC drive motors represent the main load. A UC is interfaced to the DC-bus through a bidirectional DC-DC converter to control the energy transfer between the battery and the UC module. The power converter circuit consists of two MOSFET switches in a bridge configuration combined with an inductor and a capacitor as shown in Figure 2. The converter is connected to the UC module on the low-voltage side and to the lead-acid battery on the high-voltage side. The circuit is controlled through a PWM signal generated by the hysteresis current controller.

The power converter regulates the energy flow to and from the UC in two modes of operation: buck and boost, depending on the direction of the inductor current. The converter operates in the boost mode when energy is transferred from the UC to the battery.

On the other hand, the converter operates in the buck mode when energy is transferred from the battery to the UC, or if energy is recovered from the load (regenerative breaking). The power converter is assumed to operate in continuous conduction PWM mode while switching between two states depending on the status of the switches Q1; Q<sup>2</sup> ð Þ.

In the first PWM state, Q<sup>1</sup> is ON and Q<sup>2</sup> is OFF, while in the second PWM state, Q<sup>1</sup> is OFF and Q<sup>2</sup> is ON. In the boost mode of operation, the converter circuit is described by the following equations:

$$L\frac{d\dot{\mathbf{i}}\_L}{dt} = \upsilon\_c - (\mathcal{R}\_s + \mathcal{R}\_L)\dot{\mathbf{i}}\_L - \upsilon\_0(1-\mu),\tag{1}$$

$$C\_f \frac{dv\_0}{dt} = \dot{\mathbf{i}}\_L(1 - \mu) - \dot{\mathbf{i}}\_0 + \dot{\mathbf{i}}\_{b\prime} \tag{2}$$

$$\mathbf{C}\_{s}\frac{dv\_{c}}{dt} = -\mathbf{i}\_{L} \tag{3}$$

The battery is modeled by an equivalent RC circuit with a series–parallel branch as given by Eq. (5). Cb represents the charge storage capacity of the battery, Rb is the internal series resistance (ESR), and Rd is used to model the long-term storage performance of the battery [13].

> νb Rd

The state space equations of the energy storage system can be obtained by taking iL, v0, vc, and

00 0

<sup>0</sup> � <sup>1</sup> Cb

The DC-bus voltage regulation is achieved by using a cascade control structure with a fast inner current control loop and an outer synergy-based voltage control loop. The current control loop is implemented using a sliding mode scheme to achieve a fast-response and robust performance. As a result, the inductor current is controlled to follow the reference

<sup>L</sup> <sup>0</sup>

<sup>0</sup> <sup>1</sup>

RbCf

Synergetic Control of a Hybrid Battery-Ultracapacitor Energy Storage System

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107

iL v0 vs vb

1 Rb þ 1 Rd � �

v<sup>0</sup> ¼ vb � Rbib

1

þ ib ¼ 0

: (5)

i0, (6)

Cb dvb dt þ

Figure 2. Block diagram of the proposed synergy-based hybrid energy storage system.

vb as state variables, and considering the load current i<sup>0</sup> as the system input:

L

� <sup>1</sup> RbCf

RbCb

3. Cascade control scheme with sliding mode current control

<sup>L</sup> � <sup>u</sup>

<sup>0</sup> <sup>1</sup>

d dt

iL v0 vc vb

� ð Þ Rs <sup>þ</sup> RL

u Cf

� 1 Cs

where u ¼ 1 � u is the switch control signal.

where u is the average control factor of the switch Q2, L is the inductance, Cs is the ultracapacitance, Cf is the filter capacitance, RL is the internal inductor resistance, ib is the battery current, iL is the inductor current, i<sup>0</sup> is the load current, v<sup>0</sup> is the output voltage, vc is the ultracapacitor voltage, and Rs is the internal resistance (ESR) of the ultracapacitor.

In the literature, different electro-circuit models for UC behavior simulation are available. These models have different degrees of complexity and simulation qualities [26–27]. In this chapter, the focus is on the validation of the synergy-based controller concept, the UC is modeled as a pure supercapacitance in series with the equivalent ESR. The measured UC voltage is given by:

$$\begin{aligned} \mathbf{v}\_{\text{s}} &= \mathbf{v}\_{\text{c}} + \mathbf{R}\_{\text{s}} \mathbf{i}\_{\text{sc}} \\ \mathbf{i}\_{\text{sc}} &= -\mathbf{i}\_{L} \end{aligned} \tag{4}$$

where isc is defined as the UC current. A positive isc means that the UC is charging, while a negative isc means that the UC is discharging.

Synergetic Control of a Hybrid Battery-Ultracapacitor Energy Storage System http://dx.doi.org/10.5772/intechopen.73673 107

Figure 2. Block diagram of the proposed synergy-based hybrid energy storage system.

DC-bus through a bidirectional DC-DC converter. The proposed hybrid energy storage system is designed to have high efficiency and regenerative energy capture capability. These two features represent the key elements with respect to energy saving in electric vehicles. The battery is the main DC power source that forms the DC-bus. Various loads including the AC drive motors and auxiliary electrical loads are fed from the DC-bus through DC-AC and DC-DC converters. The AC drive motors represent the main load. A UC is interfaced to the DC-bus through a bidirectional DC-DC converter to control the energy transfer between the battery and the UC module. The power converter circuit consists of two MOSFET switches in a bridge configuration combined with an inductor and a capacitor as shown in Figure 2. The converter is connected to the UC module on the low-voltage side and to the lead-acid battery on the high-voltage side. The circuit is controlled through a PWM signal generated by the hysteresis current controller.

The power converter regulates the energy flow to and from the UC in two modes of operation: buck and boost, depending on the direction of the inductor current. The converter operates in

On the other hand, the converter operates in the buck mode when energy is transferred from the battery to the UC, or if energy is recovered from the load (regenerative breaking). The power converter is assumed to operate in continuous conduction PWM mode while switching

In the first PWM state, Q<sup>1</sup> is ON and Q<sup>2</sup> is OFF, while in the second PWM state, Q<sup>1</sup> is OFF and Q<sup>2</sup> is ON. In the boost mode of operation, the converter circuit is described by the following

where u is the average control factor of the switch Q2, L is the inductance, Cs is the ultracapacitance, Cf is the filter capacitance, RL is the internal inductor resistance, ib is the battery current, iL is the inductor current, i<sup>0</sup> is the load current, v<sup>0</sup> is the output voltage, vc is the ultracapacitor voltage, and Rs is the internal resistance (ESR) of the ultracapacitor.

In the literature, different electro-circuit models for UC behavior simulation are available. These models have different degrees of complexity and simulation qualities [26–27]. In this chapter, the focus is on the validation of the synergy-based controller concept, the UC is modeled as a pure supercapacitance in series with the equivalent ESR. The measured UC

> vs ¼ vc þ Rsisc isc ¼ �iL

where isc is defined as the UC current. A positive isc means that the UC is charging, while a

dt <sup>¼</sup> vc � ð Þ Rs <sup>þ</sup> RL iL � <sup>v</sup>0ð Þ <sup>1</sup> � <sup>u</sup> , (1)

dt <sup>¼</sup> iLð Þ� <sup>1</sup> � <sup>u</sup> <sup>i</sup><sup>0</sup> <sup>þ</sup> ib, (2)

dt ¼ �iL, (3)

, (4)

the boost mode when energy is transferred from the UC to the battery.

between two states depending on the status of the switches Q1; Q<sup>2</sup> ð Þ.

Cf dv<sup>0</sup>

> Cs dvc

<sup>L</sup> diL

equations:

106 Advancements in Energy Storage Technologies

voltage is given by:

negative isc means that the UC is discharging.

The battery is modeled by an equivalent RC circuit with a series–parallel branch as given by Eq. (5). Cb represents the charge storage capacity of the battery, Rb is the internal series resistance (ESR), and Rd is used to model the long-term storage performance of the battery [13].

$$\begin{aligned} \mathbf{C}\_b \frac{d\upsilon\_b}{dt} + \frac{\nu\_b}{R\_d} + i\_b &= \mathbf{0} \\ \upsilon\_0 = \upsilon\_b - R\_b i\_b \end{aligned} \tag{5}$$

The state space equations of the energy storage system can be obtained by taking iL, v0, vc, and vb as state variables, and considering the load current i<sup>0</sup> as the system input:

$$
\frac{d}{dt} \begin{bmatrix} \dot{u} \\ \upsilon\_0 \\ \upsilon\_c \\ \upsilon\_b \end{bmatrix} = \begin{bmatrix} -\frac{(\mathsf{R}\_s + \mathsf{R}\_L)}{L} & -\frac{\overline{u}}{L} & \frac{1}{L} & 0 \\ \overline{u} & -\frac{1}{\mathsf{R}\_b \mathsf{C}\_f} & 0 & \frac{1}{\mathsf{R}\_b \mathsf{C}\_f} \\ -\frac{1}{\mathsf{C}\_s} & 0 & 0 & 0 \\ 0 & \frac{1}{\mathsf{R}\_b \mathsf{C}\_b} & 0 & -\frac{1}{\mathsf{C}\_b} \left(\frac{1}{\mathsf{R}\_b} + \frac{1}{\mathsf{R}\_d}\right) \end{bmatrix} \begin{bmatrix} \dot{u} \\ \upsilon\_0 \\ \upsilon\_b \\ \upsilon\_b \end{bmatrix} + \begin{bmatrix} 0 \\ -\frac{1}{\mathsf{C}\_f} \\ 0 \\ 0 \end{bmatrix} \dot{\imath\_{0\prime}} \tag{6}
$$

where u ¼ 1 � u is the switch control signal.

#### 3. Cascade control scheme with sliding mode current control

The DC-bus voltage regulation is achieved by using a cascade control structure with a fast inner current control loop and an outer synergy-based voltage control loop. The current control loop is implemented using a sliding mode scheme to achieve a fast-response and robust performance. As a result, the inductor current is controlled to follow the reference

current Ir, within a given tolerance band, in order to charge or discharge the ultracapacitor and keep a regulated output DC-bus voltage v0. The main objective is to keep the output voltage at the desired value even under external disturbances and load variations. Figure 2 shows the overall control scheme of the energy management system.

First, a current switching line is defined

$$S(\mathbf{x}) = I\_r - \mathbf{i}\_L = \mathbf{0},\tag{7}$$

Next, a Lyapunov function is defined as

<sup>F</sup>\_ <sup>¼</sup> <sup>1</sup>

which can be rewritten in the following form:

<sup>F</sup>\_ ¼ � <sup>1</sup> v0

The roots of the function f vð Þ¼ <sup>0</sup> 0 are

4. Synergetic control

be written in the following form:

<sup>b</sup> <sup>v</sup><sup>0</sup> �

v<sup>0</sup> �

8 >>><

>>>:

� � r

ffiffi a b

ffiffi a b

<sup>v</sup><sup>01</sup> <sup>¼</sup> <sup>1</sup> 2

<sup>v</sup><sup>02</sup> <sup>¼</sup> <sup>1</sup>

v<sup>0</sup> > 1 <sup>2</sup> <sup>R</sup>0ib <sup>þ</sup>

regulation v<sup>0</sup> ¼ v0<sup>r</sup> under different operating conditions.

for t<sup>0</sup> ≤ t ≤ tf and satisfy boundary conditions σð Þ¼ t<sup>0</sup> σ<sup>0</sup> and σ tf

� dv<sup>0</sup> dt <sup>¼</sup> <sup>1</sup>

> v0 <sup>2</sup> � <sup>d</sup> b <sup>v</sup><sup>0</sup> � <sup>a</sup> b

R0ib �

<sup>2</sup> <sup>R</sup>0ib <sup>þ</sup>

� �

� � r

Then

<sup>F</sup> <sup>¼</sup> <sup>1</sup>

<sup>2</sup><sup>b</sup> <sup>v</sup><sup>0</sup> �

ffiffi a b

ffiffi a b � � r a

> ¼ � <sup>1</sup> v0

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

<sup>b</sup> þ 4R0Irvs

<sup>b</sup> þ 4R0Irvs

<sup>b</sup> þ 4R0Irvs

v0

Synergetic Control of a Hybrid Battery-Ultracapacitor Energy Storage System

v<sup>0</sup> �

� � r

x\_ ¼ f xð Þ ; w; t , (17)

� bv<sup>0</sup> þ d

ffiffi a b

: (12)

http://dx.doi.org/10.5772/intechopen.73673

� �, (13)

f vð Þ<sup>0</sup> : (14)

: (16)

� � <sup>¼</sup> <sup>σ</sup><sup>f</sup> , for which the functional

(15)

109

� � r <sup>2</sup>

<sup>b</sup> <sup>v</sup><sup>0</sup> �

R0 2 i 2

R0 2 i 2

The stability condition of the system Eq. (10) is guaranteed if F\_ < 0. This condition is satisfied if

R0 2 i 2

The synergetic control scheme is next developed by analyzing the reduced system voltage equations with sliding mode current control as described by (10). The nonlinear system can

where <sup>x</sup> <sup>¼</sup> <sup>v</sup><sup>0</sup> vs ½ �<sup>t</sup> is the state vector, <sup>v</sup><sup>0</sup> is the system output, and <sup>w</sup> <sup>¼</sup> Ir is the system input.

The objective is to devise a control law Ir ¼ f vð Þ <sup>0</sup>; vs that optimizes the required voltage

Let L tð Þ ; σ; σ\_ be a function with continuous first and second derivatives with respect to all of its arguments. The objective is to find the function σð Þ x tð Þ that is continuously differentiable

� � q

� � q

� � <sup>q</sup> :

where Ir > 0 is the reference current. Sliding motion exists in the region where vs < v0. The condition dS dt ¼ 0 is obtained by using Eq. (1):

$$\frac{v\_0}{L}(1-u) - \frac{v\_s}{L} = 0.\tag{8}$$

The equivalent control is

$$0 < \mu\_{t\eta} = 1 - \frac{\upsilon\_s}{\upsilon\_0} < 1. \tag{9}$$

The hysteresis current controller is a very high gain controller permitting the measured current to properly track the reference signal with high accuracy. Therefore, if the tolerance band is very small, the current control loop can be approximated by a unity block. Hence, the converter equations reduce to iL ≈ Ir and u ¼ ueq.

In EV applications, a sudden acceleration or deceleration is equivalent to a step load torque change. Therefore, a variable load current can be used to represent the nonlinear DC-AC converter characteristics together with the AC motors.

The energy storage system model is next modified to include the load as a variable resistance <sup>R</sup>0. The load current can then be expressed as: <sup>i</sup><sup>0</sup> <sup>¼</sup> <sup>v</sup><sup>0</sup> R0 .

The resulting converter equations are nonlinear in terms of the output voltage v0.

$$\begin{cases} \begin{aligned} \frac{d\boldsymbol{v}\_{0}}{dt} &= -\frac{1}{\mathsf{C}\_{f}} \left( \frac{1}{\mathsf{R}\_{b}} + \frac{1}{\mathsf{R}\_{b}} \right) \boldsymbol{v}\_{0} + \frac{1}{\mathsf{R}\_{b}\mathsf{C}\_{f}} \boldsymbol{v}\_{b} + \frac{1}{\mathsf{C}\_{f}} \left( \frac{\boldsymbol{v}\_{s}}{\boldsymbol{v}\_{0}} \right) \boldsymbol{I}\_{r} \\\ \frac{d\boldsymbol{v}\_{b}}{dt} &= \frac{1}{\mathsf{R}\_{b}\mathsf{C}\_{b}} \boldsymbol{v}\_{0} - \frac{1}{\mathsf{C}\_{b}} \left( \frac{1}{\mathsf{R}\_{b}} + \frac{1}{\mathsf{R}\_{d}} \right) \boldsymbol{v}\_{b} \\\ \frac{d\boldsymbol{v}\_{c}}{dt} &= -\frac{1}{\mathsf{C}\_{s}} \boldsymbol{I}\_{r} \end{aligned} \end{cases} \tag{10}$$

The Lyapunov stability method is next used to analyze the voltage Eqs. (10). The output voltage equation is the main nonlinear equation and can be written in the following form:

$$\frac{dv\_0}{dt} = \frac{a}{v\_0} - bv\_0 + d,\tag{11}$$

where <sup>a</sup> <sup>¼</sup> <sup>1</sup> Cf Irvs, <sup>b</sup> <sup>¼</sup> <sup>1</sup> R0Cf , and <sup>d</sup> <sup>¼</sup> <sup>1</sup> Cf ib. Next, a Lyapunov function is defined as

$$F = \frac{1}{2b} \left( v\_0 - \sqrt{\frac{a}{b}} \right)^2. \tag{12}$$

Then

current Ir, within a given tolerance band, in order to charge or discharge the ultracapacitor and keep a regulated output DC-bus voltage v0. The main objective is to keep the output voltage at the desired value even under external disturbances and load variations. Figure 2 shows the

where Ir > 0 is the reference current. Sliding motion exists in the region where vs < v0. The

vs

v0

<sup>L</sup> ð Þ� <sup>1</sup> � <sup>u</sup>

<sup>0</sup> <sup>&</sup>lt; ueq <sup>¼</sup> <sup>1</sup> � vs

The hysteresis current controller is a very high gain controller permitting the measured current to properly track the reference signal with high accuracy. Therefore, if the tolerance band is very small, the current control loop can be approximated by a unity block. Hence, the con-

In EV applications, a sudden acceleration or deceleration is equivalent to a step load torque change. Therefore, a variable load current can be used to represent the nonlinear DC-AC

The energy storage system model is next modified to include the load as a variable resistance

v<sup>0</sup> þ

1 Rb þ 1 Rd � �

The Lyapunov stability method is next used to analyze the voltage Eqs. (10). The output voltage equation is the main nonlinear equation and can be written in the following form:

The resulting converter equations are nonlinear in terms of the output voltage v0.

1 Rb þ 1 R0 � �

<sup>v</sup><sup>0</sup> � <sup>1</sup> Cb

> dv<sup>0</sup> dt <sup>¼</sup> <sup>a</sup> v0

R0 .

> 1 RbCf

> > vb

vb þ 1 Cf

vs v0 � �

Ir

� bv<sup>0</sup> þ d, (11)

:

(10)

v0

S xð Þ¼ Ir � iL ¼ 0, (7)

<sup>L</sup> <sup>¼</sup> <sup>0</sup>: (8)

< 1: (9)

overall control scheme of the energy management system.

dt ¼ 0 is obtained by using Eq. (1):

verter equations reduce to iL ≈ Ir and u ¼ ueq.

converter characteristics together with the AC motors.

<sup>R</sup>0. The load current can then be expressed as: <sup>i</sup><sup>0</sup> <sup>¼</sup> <sup>v</sup><sup>0</sup>

dv<sup>0</sup> dt ¼ � <sup>1</sup> Cf

8 >>>>>>><

>>>>>>>:

dvb dt <sup>¼</sup> <sup>1</sup> RbCb

dvc dt ¼ � <sup>1</sup> Cs Ir

First, a current switching line is defined

108 Advancements in Energy Storage Technologies

condition dS

where <sup>a</sup> <sup>¼</sup> <sup>1</sup>

Cf

Irvs, <sup>b</sup> <sup>¼</sup> <sup>1</sup>

R0Cf

, and <sup>d</sup> <sup>¼</sup> <sup>1</sup>

Cf ib.

The equivalent control is

$$\dot{F} = \frac{1}{b} \left( v\_0 - \sqrt{\frac{a}{b}} \right) \times \frac{dv\_0}{dt} = \frac{1}{b} \left( v\_0 - \sqrt{\frac{a}{b}} \right) \left( \frac{a}{v\_0} - bv\_0 + d \right), \tag{13}$$

which can be rewritten in the following form:

$$\dot{F} = -\frac{1}{v\_0} \left( v\_0 - \sqrt{\frac{a}{b}} \right) \left( v\_0^2 - \frac{d}{b} v\_0 - \frac{a}{b} \right) = -\frac{1}{v\_0} \left( v\_0 - \sqrt{\frac{a}{b}} \right) f(v\_0). \tag{14}$$

The roots of the function f vð Þ¼ <sup>0</sup> 0 are

$$\begin{cases} \upsilon\_{01} = \frac{1}{2} \left( R\_0 \dot{\iota}\_b - \sqrt{R\_0^2 \dot{\iota}\_b^2 + 4R\_0 I\_r \upsilon\_s} \right) \\\\ \upsilon\_{02} = \frac{1}{2} \left( R\_0 \dot{\iota}\_b + \sqrt{R\_0^2 \dot{\iota}\_b^2 + 4R\_0 I\_r \upsilon\_s} \right) \end{cases} \tag{15}$$

The stability condition of the system Eq. (10) is guaranteed if F\_ < 0. This condition is satisfied if

$$
\sigma\_0 > \frac{1}{2} \left( R\_0 \dot{\imath}\_b + \sqrt{R\_0^2 \dot{\imath}\_b^2 + 4R\_0 I\_r \upsilon\_s} \right) \tag{16}
$$

#### 4. Synergetic control

The synergetic control scheme is next developed by analyzing the reduced system voltage equations with sliding mode current control as described by (10). The nonlinear system can be written in the following form:

$$
\dot{\mathbf{x}} = f(\mathbf{x}, \mathbf{w}, t),
\tag{17}
$$

where <sup>x</sup> <sup>¼</sup> <sup>v</sup><sup>0</sup> vs ½ �<sup>t</sup> is the state vector, <sup>v</sup><sup>0</sup> is the system output, and <sup>w</sup> <sup>¼</sup> Ir is the system input.

The objective is to devise a control law Ir ¼ f vð Þ <sup>0</sup>; vs that optimizes the required voltage regulation v<sup>0</sup> ¼ v0<sup>r</sup> under different operating conditions.

Let L tð Þ ; σ; σ\_ be a function with continuous first and second derivatives with respect to all of its arguments. The objective is to find the function σð Þ x tð Þ that is continuously differentiable for t<sup>0</sup> ≤ t ≤ tf and satisfy boundary conditions σð Þ¼ t<sup>0</sup> σ<sup>0</sup> and σ tf � � <sup>¼</sup> <sup>σ</sup><sup>f</sup> , for which the functional

$$J = \int\_{t\_0}^{t\_f} L(t, \sigma(t), \dot{\sigma}(t)) dt = \int\_{t\_0}^{t\_f} \left( T^2 \dot{\sigma}^2 + \sigma^2 \right) dt,\tag{18}$$

is minimum, where <sup>T</sup> <sup>¼</sup> <sup>T</sup><sup>T</sup> <sup>&</sup>gt; 0 is a symmetric positive parameter to be designed. Then, <sup>σ</sup>ð Þ<sup>t</sup> is a minimizer of the functional J if it is a solution of the following linear differential equation

$$T\dot{\sigma} + \sigma = 0,\tag{19}$$

controlled by a faster inner loop. This strategy is shown to give a good transient performance. However, it is very sensitive to the UC voltage vs, since this voltage is continuously changing due to charging and discharging actions. To overcome this drawback, an optimized control law is adopted by filtering the UC voltage vs to detect and use the low-frequency variation

where vsf is the filtered UC voltage. A first order low-pass filter is used with a cut-off frequency

The proposed synergetic control law is validated first using computer simulation. The closed loop system behavior is evaluated by checking the system robustness to step load disturbances. The simulation results are next validated on an experimental prototype system with the same

Figure 3 shows the system performance when starting at no load, and then the load current is changed from 0 to 2.6 A. It can be observed that initially the DC-bus voltage v<sup>0</sup> is stabilized at the desired reference value of 32.7 V. Next, due to the sudden variation in load, the voltage is disturbed and goes through a fast transient. The synergetic controller is able to minimize the

Figure 3. System response to a step load current with optimized control law. (a) Load current i0; (b) DC-bus voltage v0; (c)

battery current ib and SC current isc ; and (d) SC voltage vs.

ωf s þ ω<sup>f</sup>

Gfð Þ¼ s

ðt 0

Synergetic Control of a Hybrid Battery-Ultracapacitor Energy Storage System

ð Þ v<sup>0</sup> � v0<sup>r</sup> dt, (25)

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: (26)

Ψ ¼ v<sup>0</sup> � v0<sup>r</sup> þ k1vsf þ k<sup>2</sup>

instead of the actual voltage.

ω<sup>f</sup> and a transfer function

6. Simulation results

input parameters.

The new manifold is then updated as

where T is a design parameter that sets the speed of convergence to the desired manifold.

Next, define the macro-variable σ as

$$
\sigma = \upsilon\_0 - \upsilon\_{0r} + k\_1(\upsilon\_s - \upsilon\_{sr}) + k\_2 \int\_0^t (\upsilon\_0 - \upsilon\_{0r}) dt. \tag{20}
$$

The reference voltage v0<sup>r</sup> can be selected to be the nominal DC-bus voltage with no load when the UC is in the charging mode. On the other hand, the reference voltage vsr is selected as the rated voltage of the UC. Next, the macro-variable derivative is obtained using the chain rule of differentiation.

$$
\dot{\sigma} = \frac{d\sigma}{d\mathbf{x}} \ \dot{\mathbf{x}}.\tag{21}
$$

Using (18)–(21) and solving for the reference current Ir yields

$$I\_{r} = \frac{1}{\frac{k\_{1}}{C\_{s}} - \frac{v\_{s}}{C\_{f}v\_{0}}} \times \left[ \frac{i\_{b} - i\_{0}}{C\_{f}} + \left(k\_{2} + \frac{1}{T}\right)(v\_{0} - v\_{0r}) + \frac{k\_{1}}{T}(v\_{s} - v\_{sr}) + \frac{k\_{2}}{T} \right](v\_{0} - v\_{0r})dt\right]. \tag{22}$$

This synergetic control law will force the system to operate on the manifold σð Þ¼ x 0. In addition, the control law will impose a well-controlled dynamic behavior off the manifold.

Next, the control law designed earlier is shown to be globally asymptotically stable. Consider the positive definite candidate Lyapunov function V is defined by

$$V(\mathbf{x}) = \frac{1}{2}\sigma^2(\mathbf{x}).\tag{23}$$

Then, the total time derivative of V along the trajectories of σð Þx is given by

$$
\dot{V} = \sigma(\mathbf{x})\dot{\sigma}(\mathbf{x}) = -\frac{1}{T}\sigma^2(\mathbf{x}) < 0 \qquad \forall \sigma \neq 0,\tag{24}
$$

which shows that the system (10) will converge to the manifold σð Þ¼ x 0 asymptotically.

#### 5. Optimized control law

The synergy-based control strategy presented earlier uses a cascade control structure where the output voltage is regulated by the outer loop via the inductor current which is tightly controlled by a faster inner loop. This strategy is shown to give a good transient performance. However, it is very sensitive to the UC voltage vs, since this voltage is continuously changing due to charging and discharging actions. To overcome this drawback, an optimized control law is adopted by filtering the UC voltage vs to detect and use the low-frequency variation instead of the actual voltage.

The new manifold is then updated as

J ¼ ðtf t0

Next, define the macro-variable σ as

110 Advancements in Energy Storage Technologies

differentiation.

Ir <sup>¼</sup> <sup>1</sup> k1 Cs � vs Cf v<sup>0</sup> �

5. Optimized control law

L tð Þ ; σð Þt ; σ\_ð Þt dt ¼

ðtf t0 T2

> ðt 0

is minimum, where <sup>T</sup> <sup>¼</sup> <sup>T</sup><sup>T</sup> <sup>&</sup>gt; 0 is a symmetric positive parameter to be designed. Then, <sup>σ</sup>ð Þ<sup>t</sup> is a minimizer of the functional J if it is a solution of the following linear differential equation

where T is a design parameter that sets the speed of convergence to the desired manifold.

The reference voltage v0<sup>r</sup> can be selected to be the nominal DC-bus voltage with no load when the UC is in the charging mode. On the other hand, the reference voltage vsr is selected as the rated voltage of the UC. Next, the macro-variable derivative is obtained using the chain rule of

<sup>σ</sup>\_ <sup>¼</sup> <sup>d</sup><sup>σ</sup>

ð Þþ v<sup>0</sup> � v0<sup>r</sup>

This synergetic control law will force the system to operate on the manifold σð Þ¼ x 0. In addition, the control law will impose a well-controlled dynamic behavior off the manifold.

Next, the control law designed earlier is shown to be globally asymptotically stable. Consider

V xð Þ¼ <sup>1</sup> 2 σ2

> 1 T σ2

which shows that the system (10) will converge to the manifold σð Þ¼ x 0 asymptotically.

The synergy-based control strategy presented earlier uses a cascade control structure where the output voltage is regulated by the outer loop via the inductor current which is tightly

k1

<sup>T</sup> ð Þþ vs � vsr

σ ¼ v<sup>0</sup> � v0<sup>r</sup> þ k1ð Þþ vs � vsr k<sup>2</sup>

Using (18)–(21) and solving for the reference current Ir yields

þ k<sup>2</sup> þ

the positive definite candidate Lyapunov function V is defined by

Then, the total time derivative of V along the trajectories of σð Þx is given by

<sup>V</sup>\_ <sup>¼</sup> <sup>σ</sup>ð Þ<sup>x</sup> <sup>σ</sup>\_ð Þ¼� <sup>x</sup>

1 T � �

ib � i<sup>0</sup> Cf

�

<sup>σ</sup>\_ <sup>2</sup> <sup>þ</sup> <sup>σ</sup><sup>2</sup> � �dt, (18)

ð Þ v<sup>0</sup> � v0<sup>r</sup> dt: (20)

d x <sup>x</sup>\_: (21)

k2 T ð

ð Þx : (23)

ð Þx < 0 ∀σ 6¼ 0, (24)

ð Þ v<sup>0</sup> � v0<sup>r</sup> dt

� :

(22)

T σ\_ þ σ ¼ 0, (19)

$$
\Psi = \upsilon\_0 - \upsilon\_{0r} + k\_1 \upsilon\_{\circ f} + k\_2 \int\_0^t (\upsilon\_0 - \upsilon\_{0r}) dt,\tag{25}
$$

where vsf is the filtered UC voltage. A first order low-pass filter is used with a cut-off frequency ω<sup>f</sup> and a transfer function

$$\mathcal{G}\_f(\mathbf{s}) = \frac{\omega\_f}{\mathbf{s} + \omega\_f}. \tag{26}$$

#### 6. Simulation results

The proposed synergetic control law is validated first using computer simulation. The closed loop system behavior is evaluated by checking the system robustness to step load disturbances. The simulation results are next validated on an experimental prototype system with the same input parameters.

Figure 3 shows the system performance when starting at no load, and then the load current is changed from 0 to 2.6 A. It can be observed that initially the DC-bus voltage v<sup>0</sup> is stabilized at the desired reference value of 32.7 V. Next, due to the sudden variation in load, the voltage is disturbed and goes through a fast transient. The synergetic controller is able to minimize the

Figure 3. System response to a step load current with optimized control law. (a) Load current i0; (b) DC-bus voltage v0; (c) battery current ib and SC current isc ; and (d) SC voltage vs.

voltage fluctuation, and the voltage is regulated back to the reference value. The peak-to-peak voltage variation is

$$
\Delta \upsilon\_0 = \frac{\upsilon\_{0\text{max}} - \upsilon\_{0\text{min}}}{\upsilon\_{0r}} = 0.56\%. \tag{27}
$$

Figure 4. Hardware prototype of the proposed UC-based energy storage system.

Table 1. Prototype system parameters.

Symbol Parameter Value R0 Resistive load 25 Ω L Inductance 1.35 H RL Inductor Internal Resistance 0.2 Ω Cs UC Bank 383.3 F Rs UC Bank Internal Resistance 0.2 Ω Cf Output capacitor 4700 μF vsn Nominal UC voltage 15 V vbn Nominal battery voltage 42 V Cb Battery storage capacitor 900 F Rb Battery internal series resistance (ESR) 0.4 Ω Rd Battery storage resistance 470 Ω Ts Outer Loop Sampling time 12.5 μs ts Current Control Loop Sampling Time 5 μs Δi Current controller Hysteresis band 0.5 A T Synergetic Controller Time Constant 10 ms k1 Synergetic Controller Gain 0.01 k2 Synergetic Controller Gain 100

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The sudden load increase at t ¼ 0:1s is quickly supplied by the UC, which is discharged through the inductor. The battery shows only a small variation in current which contains only a low-frequency component needed for voltage regulation. This response is a good feature of the proposed control system that would help extend the battery operating life. The results clearly illustrate that the optimized control system has a very good robust performance to load uncertainty. The incorporation of the low-pass filter allows tracking the voltage changes with fast transient response and very small steady-state error.

#### 7. Experimental results and discussion

In this section, experimental results of the proposed synergy-based control scheme are provided to validate the theoretical design. Figure 4 shows a general view of the actual hardware. The synergetic controller block is implemented by Eq. (22) as illustrated in Figure 2. The system parameters are given in Table 1.

The hybrid energy storage system prototype was developed using a bidirectional DC-DC converter module, a 36-V battery pack, and a 15-V UC bank formed by the series connection of six UCs with 2300 F each. The variable load is implemented by using two 25 Ω power resistors in parallel connected to the DC-bus. The control algorithms are developed on the eZdsp board from Texas Instruments based on the TMS320F28335 DSP and the dSPACE1104 development system. The TI DSP is solely dedicated to the current control loop, while the dSPACE1104 system is for the outer voltage control loop. The control code is developed by the operator on a laptop using Code Composer Studio and then downloaded on the TI-DSP for real-time operation.

The closed loop system behavior is analyzed by evaluating the transient response and steadystate response to step load disturbances. The first test examines the case where the DC-bus voltage is maintained at a constant value with no load and the battery is charging only the UC with a constant current.

The DC-bus voltage reference is set to a value v0<sup>r</sup> ¼ 32:85V lower than the nominal value of the battery. The synergetic controller would then automatically set the charging current reference for the UC to maintain this DC-bus voltage level. The load is next changed abruptly as shown in the profile of Figure 5. Normally, this would require an abrupt change of the battery current to supply this sudden load disturbance. However, the system shows a very good robustness to this load disturbance, as the UC current changes rapidly from the charging mode to the discharging mode to supply the required additional load with the minimum effect on the battery current.

Synergetic Control of a Hybrid Battery-Ultracapacitor Energy Storage System http://dx.doi.org/10.5772/intechopen.73673 113

Figure 4. Hardware prototype of the proposed UC-based energy storage system.


Table 1. Prototype system parameters.

voltage fluctuation, and the voltage is regulated back to the reference value. The peak-to-peak

The sudden load increase at t ¼ 0:1s is quickly supplied by the UC, which is discharged through the inductor. The battery shows only a small variation in current which contains only a low-frequency component needed for voltage regulation. This response is a good feature of the proposed control system that would help extend the battery operating life. The results clearly illustrate that the optimized control system has a very good robust performance to load uncertainty. The incorporation of the low-pass filter allows tracking the voltage changes with

In this section, experimental results of the proposed synergy-based control scheme are provided to validate the theoretical design. Figure 4 shows a general view of the actual hardware. The synergetic controller block is implemented by Eq. (22) as illustrated in Figure 2. The

The hybrid energy storage system prototype was developed using a bidirectional DC-DC converter module, a 36-V battery pack, and a 15-V UC bank formed by the series connection of six UCs with 2300 F each. The variable load is implemented by using two 25 Ω power resistors in parallel connected to the DC-bus. The control algorithms are developed on the eZdsp board from Texas Instruments based on the TMS320F28335 DSP and the dSPACE1104 development system. The TI DSP is solely dedicated to the current control loop, while the dSPACE1104 system is for the outer voltage control loop. The control code is developed by the operator on a laptop using Code Composer Studio and then downloaded on the TI-DSP for

The closed loop system behavior is analyzed by evaluating the transient response and steadystate response to step load disturbances. The first test examines the case where the DC-bus voltage is maintained at a constant value with no load and the battery is charging only the UC

The DC-bus voltage reference is set to a value v0<sup>r</sup> ¼ 32:85V lower than the nominal value of the battery. The synergetic controller would then automatically set the charging current reference for the UC to maintain this DC-bus voltage level. The load is next changed abruptly as shown in the profile of Figure 5. Normally, this would require an abrupt change of the battery current to supply this sudden load disturbance. However, the system shows a very good robustness to this load disturbance, as the UC current changes rapidly from the charging mode to the discharging mode to supply the required additional load with the minimum effect on the

¼ 0:56%: (27)

<sup>Δ</sup>v<sup>0</sup> <sup>¼</sup> <sup>v</sup>0max � <sup>v</sup>0min v0<sup>r</sup>

fast transient response and very small steady-state error.

7. Experimental results and discussion

system parameters are given in Table 1.

real-time operation.

with a constant current.

battery current.

voltage variation is

112 Advancements in Energy Storage Technologies

Figure 5. Experimental results with synergetic controller and variable load. (a) Load current i0; (b) DC-bus voltage v0; (c) battery current ib and SC current isc ; and (d) SC voltage vs.

The output voltage also maintains its steady-state value with minimum variation, except at large load, when the ripple voltage is increased. This is mainly due to the large inductor current ripple. Despite the large variation in load, the peak-to-peak voltage variation is

$$
\Delta v\_0 = \frac{v\_{0\text{max}} - v\_{0\text{min}}}{v\_{0r}} = \frac{32.97 - 32.76}{32.85} = 0.73\%.\tag{28}
$$

controller, the voltage deviation is smaller, and the transient response is faster with a settling time of 7.1 ms as shown in Figure 7b. The battery and supercapacitor currents' behavior can be compared by referring to Figure 6c and Figure 7c. In both cases, the UC current changes rapidly from the charging mode to the discharging mode to supply the required additional load current. However, it can be observed that the battery current shows a larger variation and

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Figure 6. Experimental results with PI controller and a step load. (a) Load current ; (b) DC-bus voltage v0; (c) battery

Figure 7. Experimental results with synergetic controller and a step load. (a) Load current i0; (b) DC-bus voltage v0; (c)

current ib and SC current isc ; and (d) SC voltage vs.

battery current ib and SC current isc ; and (d) SC voltage vs.

This result shows a very good agreement with the simulation results obtained in Section 5.

The transient performance of the proposed synergetic controller is next compared with the standard PI controller, and its ability to achieve optimal transient performance is verified. The PI voltage control loop is implemented using the measured output DC-bus voltage.

$$I\_r = k\_p(\upsilon\_{0r} - \upsilon\_0) + k\_i \int\_0^t (\upsilon\_{0r} - \upsilon\_0) dt. \tag{29}$$

Figures 6 and 7 show the system response to a load step change for both controllers under the same operating conditions. The DC-bus voltage is regulated to follow a reference value v0<sup>r</sup> ¼ 32:7V. The PI controller gains were tuned to achieve a fast transient response while keeping the overshoot below 1%.

A step load change (i0 = 2.56 A) is applied at t = 0.0047 s. For the PI controller, the DC-bus voltage is reduced due to this sudden load change as shown in Figure 6b. It goes through a transient and then recovers back to the reference value within 10.7 ms. For the synergetic controller, the voltage deviation is smaller, and the transient response is faster with a settling time of 7.1 ms as shown in Figure 7b. The battery and supercapacitor currents' behavior can be compared by referring to Figure 6c and Figure 7c. In both cases, the UC current changes rapidly from the charging mode to the discharging mode to supply the required additional load current. However, it can be observed that the battery current shows a larger variation and

Figure 6. Experimental results with PI controller and a step load. (a) Load current ; (b) DC-bus voltage v0; (c) battery current ib and SC current isc ; and (d) SC voltage vs.

The output voltage also maintains its steady-state value with minimum variation, except at large load, when the ripple voltage is increased. This is mainly due to the large inductor current ripple. Despite the large variation in load, the peak-to-peak voltage variation is

Figure 5. Experimental results with synergetic controller and variable load. (a) Load current i0; (b) DC-bus voltage v0; (c)

This result shows a very good agreement with the simulation results obtained in Section 5.

PI voltage control loop is implemented using the measured output DC-bus voltage.

Ir ¼ kpð Þþ v0<sup>r</sup> � v<sup>0</sup> ki

The transient performance of the proposed synergetic controller is next compared with the standard PI controller, and its ability to achieve optimal transient performance is verified. The

Figures 6 and 7 show the system response to a load step change for both controllers under the same operating conditions. The DC-bus voltage is regulated to follow a reference value v0<sup>r</sup> ¼ 32:7V. The PI controller gains were tuned to achieve a fast transient response while

A step load change (i0 = 2.56 A) is applied at t = 0.0047 s. For the PI controller, the DC-bus voltage is reduced due to this sudden load change as shown in Figure 6b. It goes through a transient and then recovers back to the reference value within 10.7 ms. For the synergetic

<sup>¼</sup> <sup>32</sup>:<sup>97</sup> � <sup>32</sup>:<sup>76</sup>

ðt 0

<sup>32</sup>:<sup>85</sup> <sup>¼</sup> <sup>0</sup>:73%: (28)

ð Þ v0<sup>r</sup> � v<sup>0</sup> dt: (29)

<sup>Δ</sup>v<sup>0</sup> <sup>¼</sup> <sup>v</sup>0max � <sup>v</sup>0min v0<sup>r</sup>

battery current ib and SC current isc ; and (d) SC voltage vs.

114 Advancements in Energy Storage Technologies

keeping the overshoot below 1%.

Figure 7. Experimental results with synergetic controller and a step load. (a) Load current i0; (b) DC-bus voltage v0; (c) battery current ib and SC current isc ; and (d) SC voltage vs.

a slower response for the case of the PI controller compared to the synergetic controller. The same behavior is observed for the UC voltage in Figure 6d and Figure 7d.

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[2] Sant AV, Khadkikar V, Xiao W, Zeineldin HH. Four-Axis vector-controlled dual-rotor PMSM for plug-in electric vehicles. IEEE Transactions on Industrial Electronics. 2015;

[3] Huang W, Abu Qahouq JA. Energy sharing control scheme for state-of-charge balancing of distributed battery energy storage system. IEEE Transactions on Industrial Electronics.

[4] Hu KW, Yi PH, Liaw CM. An EV SRM drive powered by battery/Supercapacitor with G2V and V2H/V2G capabilities. IEEE Transactions on Industrial Electronics. 2015;62(8):

[5] Shuai L, Corzine KA, Ferdowsi M. Unique ultracapacitor direct integration scheme in multilevel motor drives for large vehicle propulsion. IEEE Transactions on Vehicular

[6] Dixon JW, Ortuzar ME. Ultracapacitors + DC-DC converters in regenerative braking

[7] Delille G, François B. A review of some technical and economic features of energy storage technologies for distribution system integration. Ecological Engineering and Environ-

[8] Zhang Z, Zhang X, Chen W, Rasim Y, Salman W, Pan H, Yuan Y, Wang C. A highefficiency energy regenerative shock absorber using supercapacitors for renewable energy applications in range extended electric vehicle. Applied Energy. 2016;178:177-188

[9] Herrera V, Milo A, Gaztañaga H, Etxeberria-Otadui I, Villarreal I, Camblong H. Adaptive energy management strategy and optimal sizing applied on a battery-supercapacitor

[10] Ma T, Yang H, Lu L. Development of hybrid battery–supercapacitor energy storage for

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remote area renewable energy systems. Applied Energy. 2015;153:56-62

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Table 2 gives the peak-to-peak variations of the battery current and the DC-bus voltage for both controllers. It can be seen that the synergy-based controller has a much better transient performance and a higher robustness to disturbances than the PI controller.


Table 2. Experimental results with PI controller and synergetic controller under variable load.

#### 8. Conclusion

This chapter proposes a fast-response synergetic controller for a battery-ultracapacitor energy storage system. The synergy-based controller is developed to enhance the system robustness during the transient response of the DC-bus voltage tracking control. The ultracapacitor module is controlled to reinforce the DC-bus during the load transients and smooth the power fluctuations. The stability analysis of the nonlinear control scheme is derived using the Lyapunov theory. The effectiveness of the proposed control scheme is verified by simulations and by experiments on a prototype hybrid energy storage system and its advantages are indicated in comparison with the traditional PI control scheme. This work is intended as a preliminary study to optimize the performance of electric vehicles. It is believed that the presented technique will provide a strong foundation for the development of a range of fullfield synergy-based control techniques in electric vehicles. The added advantages of this technique is that it has a cascade control structure which can be easily adapted and implemented on existing EV control systems. Only additional current and voltage sensors are needed to implement the feedback control loops. This could be a versatile tool to improve both the vehicle range and battery cycle life through optimal management of the onboard power and energy and realize full utilization of the installed storage capacities.

#### Author details

Rached Dhaouadi<sup>1</sup> \*, Kamyar Khosravi<sup>1</sup> and Yoichi Hori<sup>2</sup>

\*Address all correspondence to: rdhaouadi@aus.edu


#### References

a slower response for the case of the PI controller compared to the synergetic controller. The

Table 2 gives the peak-to-peak variations of the battery current and the DC-bus voltage for both controllers. It can be seen that the synergy-based controller has a much better transient

Controller PI controller Synergetic controller

96.50% 37.13%

0.73% 0.37%

This chapter proposes a fast-response synergetic controller for a battery-ultracapacitor energy storage system. The synergy-based controller is developed to enhance the system robustness during the transient response of the DC-bus voltage tracking control. The ultracapacitor module is controlled to reinforce the DC-bus during the load transients and smooth the power fluctuations. The stability analysis of the nonlinear control scheme is derived using the Lyapunov theory. The effectiveness of the proposed control scheme is verified by simulations and by experiments on a prototype hybrid energy storage system and its advantages are indicated in comparison with the traditional PI control scheme. This work is intended as a preliminary study to optimize the performance of electric vehicles. It is believed that the presented technique will provide a strong foundation for the development of a range of fullfield synergy-based control techniques in electric vehicles. The added advantages of this technique is that it has a cascade control structure which can be easily adapted and implemented on existing EV control systems. Only additional current and voltage sensors are needed to implement the feedback control loops. This could be a versatile tool to improve both the vehicle range and battery cycle life through optimal management of the onboard power

same behavior is observed for the UC voltage in Figure 6d and Figure 7d.

performance and a higher robustness to disturbances than the PI controller.

iss

v0<sup>r</sup> 

Table 2. Experimental results with PI controller and synergetic controller under variable load.

Settling time To reach steady state 10.7 ms 7.1 ms

 

and energy and realize full utilization of the installed storage capacities.

\*, Kamyar Khosravi<sup>1</sup> and Yoichi Hori<sup>2</sup>

1 College of Engineering, American University of Sharjah, Sharjah, UAE

2 Graduate School of Frontier Science, The University of Tokyo, Chiba, Japan

\*Address all correspondence to: rdhaouadi@aus.edu

8. Conclusion

Battery current variation <sup>Δ</sup>ib <sup>¼</sup> ibmax�ibmin

116 Advancements in Energy Storage Technologies

DC-bus voltage variation <sup>Δ</sup>v<sup>0</sup> <sup>¼</sup> <sup>v</sup>0max�v0min

Author details

Rached Dhaouadi<sup>1</sup>


[14] Capasso C, Sepe V, Veneri O, Montanari M, Poletti L. Experimentation with a ZEBRA plus EDLC based hybrid storage system for urban means of transport. In: Proceedings of the 2015 International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles (ESARS); 3-5 March 2015; Aachen, Germany. IEEE; 2015. DOI: 10.1109/ESARS.2015.7101498

on Advanced Motion Control, (AMC2014); March 14-16, 2014; Yokohama, Japan. IEEE;

Synergetic Control of a Hybrid Battery-Ultracapacitor Energy Storage System

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[26] Buller S, Thele M, De Doncker RWAA, Karden E. Impedance-based simulation models of supercapacitors and Li-ion batteries for power electronic applications. IEEE Transactions

[27] Zubieta L, Bonert R. Characterization of double-layer capacitors for power electronics applications. IEEE Transactions on Industry Applications. 2000;36(1):199-205

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on Industry Applications. 2005;41(3):742-747


on Advanced Motion Control, (AMC2014); March 14-16, 2014; Yokohama, Japan. IEEE; 2014. DOI: 10.1109/AMC.2014.6823275

[26] Buller S, Thele M, De Doncker RWAA, Karden E. Impedance-based simulation models of supercapacitors and Li-ion batteries for power electronic applications. IEEE Transactions on Industry Applications. 2005;41(3):742-747

[14] Capasso C, Sepe V, Veneri O, Montanari M, Poletti L. Experimentation with a ZEBRA plus EDLC based hybrid storage system for urban means of transport. In: Proceedings of the 2015 International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles (ESARS); 3-5 March 2015; Aachen, Germany. IEEE; 2015. DOI:

[15] Sanfélix J, Messagie M, Omar N, Mierlo JV, Hennige V. Environmental performance of advanced hybrid energy storage systems for electric vehicle applications. Applied Energy.

[16] Capasso C, Veneri O, Patalano S. Experimental study on the performance of a ZEBRA battery based propulsion system for urban commercial vehicles. Applied Energy. 2017;

[17] Krause A, Kossyrev P, Oljaca M, Passerini S, Winter M, Balducci A. Electrochemical double layer capacitor and lithium-ion capacitor based on carbon black. Journal of Power

[18] Tan SC, Lai YM, Tse CK, Salamero LM, Wu CK. A fast-response sliding-mode controller for boost-type converters with a wide range of operating conditions. IEEE Transactions

[19] Wai RJ, Chen MW, Liu YK. Design of adaptive control and fuzzy neural network control for single-stage boost inverter. IEEE Transactions on Industrial Electronics. 2015;62(9):

[20] Kolesnikov A. Synergetic control for electromechanical systems. In: Proceedings of the 15th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2002);

[21] Kondratiev I, Santi E, Dougal R, Veselov G. Synergetic control for m-parallel connected DC-DC Buck converters. In: Proceedings of the 35th Annual IEEE Power Electronics Specialists Conference (PESC 04); 20-25 June 2004; Aachen, Germany: IEEE; 2004. p. 182-188. DOI: 10.

[22] Santi E, Monti A, Li D. Synergetic control for power electronics applications: A comparison with the sliding mode approach. Journal of Circuits, Systems, and Computers. 2004;

[23] Guangfu MA, Jing H, Gang L, Chuanjiang L. Adaptive synergetic optimal control for attitude tracking of rigid spacecraft. In: Proceedings of the 30th Chinese Control Confer-

[24] Nusawardhana SAK, Crossley W. Nonlinear synergetic optimal control. Journal of Guid-

[25] Dhaouadi R, Hori Y, Huang X. Robust control of an ultracapacitor-based hybrid energy storage system for electric vehicles. In: Proceedings of the 13th IEEE International Workshop

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Sources. 2011;196(20):8836-8842

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**Section 3**

**Integration of Energy Storage into Applications**

**Integration of Energy Storage into Applications**

**Chapter 6**

Provisional chapter

**Analysis of Various Energy Storage Systems for**

Analysis of Various Energy Storage Systems for Variable

DOI: 10.5772/intechopen.72294

During the high penetration of wind power, wind turbines can affect power quality directly due to an unstable and intermittency source. Voltage fluctuations, harmonics, and voltage drops might be factors in this environment. Energy storage systems (ESSs) with variable speed wind turbines (VSWTs) as a permanent magnetic synchronous generator (PMSG) and a doubly fed induction generator (DFIG) could be a solution to improve the power quality from the "variability" of wind power. This chapter investigates the proposed system, which comprises a hybrid ESS for the VSWT. It analyzes the ability of various ESSs (B, SC, and EDLC) based on VSWTs with various ESSs for power quality in terms of average THD (%) specified in reference to IEEE std-519-1992 and IEC 61400-21-Ed.2.0. In addition, this chapter investigates the DFIG with hybrid energy storage systems (Li ion battery and super capacitor ESS) for the economic evaluation in terms of payback time. The simulation results have been verified by a power system computer-aided design/electromagnetic transients direct current (PSCAD/EMTDC) to demonstrate the system performance under different scenarios.

Keywords: VSWT, hybrid ESS, total harmonic distortion, power quality, payback time,

The DOE's 2009 Annual Energy Outlook projected energy accumulation in the USA. The report describes that the future growth of domestic electric energy will increase by 26% for electricity sector by 2030. This might not only require additional generation capacity of 259 GW but also forecast to increase as 1.7% of total installed capacity until 2030 [1, 2]. Variable speed wind turbine prefers to the conventional operation to extract the maximum power from the unstable wind generation. Since speed of wind turbine is nonconstant, the generator should

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

**Variable Speed Wind Turbines**

Speed Wind Turbines

Youngil Kim and Robert J. Harrington

Youngil Kim and Robert J. Harrington

http://dx.doi.org/10.5772/intechopen.72294

Abstract

PSCAD/EMTDC

1. Introduction

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

#### **Analysis of Various Energy Storage Systems for Variable Speed Wind Turbines** Analysis of Various Energy Storage Systems for Variable Speed Wind Turbines

DOI: 10.5772/intechopen.72294

Youngil Kim and Robert J. Harrington Youngil Kim and Robert J. Harrington

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.72294

#### Abstract

During the high penetration of wind power, wind turbines can affect power quality directly due to an unstable and intermittency source. Voltage fluctuations, harmonics, and voltage drops might be factors in this environment. Energy storage systems (ESSs) with variable speed wind turbines (VSWTs) as a permanent magnetic synchronous generator (PMSG) and a doubly fed induction generator (DFIG) could be a solution to improve the power quality from the "variability" of wind power. This chapter investigates the proposed system, which comprises a hybrid ESS for the VSWT. It analyzes the ability of various ESSs (B, SC, and EDLC) based on VSWTs with various ESSs for power quality in terms of average THD (%) specified in reference to IEEE std-519-1992 and IEC 61400-21-Ed.2.0. In addition, this chapter investigates the DFIG with hybrid energy storage systems (Li ion battery and super capacitor ESS) for the economic evaluation in terms of payback time. The simulation results have been verified by a power system computer-aided design/electromagnetic transients direct current (PSCAD/EMTDC) to demonstrate the system performance under different scenarios.

Keywords: VSWT, hybrid ESS, total harmonic distortion, power quality, payback time, PSCAD/EMTDC

#### 1. Introduction

The DOE's 2009 Annual Energy Outlook projected energy accumulation in the USA. The report describes that the future growth of domestic electric energy will increase by 26% for electricity sector by 2030. This might not only require additional generation capacity of 259 GW but also forecast to increase as 1.7% of total installed capacity until 2030 [1, 2]. Variable speed wind turbine prefers to the conventional operation to extract the maximum power from the unstable wind generation. Since speed of wind turbine is nonconstant, the generator should

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

be controlled by the power electronic circuit. Two variable speed wind turbines (VSWTs) on a principle of power electronics could be classified as DFIGs and PMSGs [3, 4].

DFIG-based wind turbine is a wound rotor induction generator. It consists of three-phase windings on the rotor and stator. The stator winding of the wind turbine is directly connected to the grid winding, while the rotor winding of the generator is fed by variable frequency bidirectional back-to-back PWM based on the voltage source converter, which consists of rotor side converter (RSC) and grid side converter (GSC). It has typically about 30% of nominal generator power [3, 4]. The synchronous machine has the ability to provide its own excitation on the rotor. Such excitation may be obtained by means of either a current carrying winding or permanent magnets (PMs) [5]. PM excitation avoids the field current supply or reactive power compensation facilities [2, 6–8]. PMSG is a permanent magnet synchronous machine with its stator windings connected to the grid through a frequency converter. Signal frequency is generated via the pulse width modulation (PWM) with the DC link of back-to-back voltage source converters (VSCs) consisting of machine side converter and grid side converter [9, 10].

There are several state-of-the-art technologies on wind power and energy storage to improve power quality and stability by optimal control of ESS [11–17], hybrid ESS [18, 19], and renewable forecasting modeling [20, 21]. It is widely popularized that an elaborately optimal control strategy for ESS is to smooth wind power fluctuations for the renewable energy [11–14]. Two configurations of DFIG-BESS for internal and external controllers have improved the capability integrated to the grid [15]. In [16], an optimal control scheduling was considered on the variable smoothing time constant and charging/discharging of power limits, which is to mitigate the wind power fluctuations, while extending the battery life cycle of the BESS. A control strategy of the ESS in the wind farm focused an open-loop optimal control scheme is to incorporate the operating limits of BESS based on the forecasted wind condition [17]. The author [18] introduced the hybrid energy storage system (HESS) to overcome the fast PVwind power generation fluctuations by smart scheduling, which has a statistical approach for the capacity distribution of the HESS. However, they did not demonstrate real-time simulation with few limited cases. The control and energy management of the hybrid ESS (battery and super capacitor) with DFIG has coordinated the power flows and load demand [19]. Prediction scheme of wind power generation connected to battery ESS developed by using numeric weather prediction model which required to the input as detailed wind information [20]. However, they did not smooth to power variation scheme in this chapter. Energy management system of flywheel ESS used to fuzzy logic for two optimized models as constraint condition and determine optimization objective to control of FESS [21].

RSC controller is to regulate active power (Ps) and reactive power (Qs) of the stator side independently, while the GSC controller is to keep the constant DC voltage (Vdc) to adjust reactive power (Qg) of the GSC, which transfers from the grid side [22]. Figure 1(b) describes that PMSG which is a variable speed wind turbine with a direct-drive generator connected to the grid through a full-scale power converter [2, 6]. Three different ESS such as battery (B), super capacitor (SC), and electrical dual layer capacitor (EDLC) were introduced in Figure 1. Each ESS controller comprised bank, inductance and two-quadrant DC/DC converter connected to the DC link as illustrated in Figure 1. Two DC/DC converter design includes two insulated gate Bi-polar junction transistors (IGBTs) diode switches as S1 and S2. To compare two different VSWTs, that is, DFIG and PMSG, three-phase voltage sources are employed at 20 kV, 60 Hz, 0.04 H, and 2.5 Ω, and PSCAD parameter of the wind turbine given in Table 1.

Table 1. PSCAD/EMTDC parameters of the wind turbine for DFIG, PMSG in the proposed systems [23].

Figure 1. Block diagram of the hybrid energy storage system connected to the configuration variable speed wind

]

Analysis of Various Energy Storage Systems for Variable Speed Wind Turbines

http://dx.doi.org/10.5772/intechopen.72294

125

]

Generated Rated MVA 2 [MVA] Machine rated angular mechanical speed 125.667 [rad/s]

Rotor radius 44 [m] Rotor area 2124 [m2

Air density 1.229 [kg/m<sup>3</sup>

Gear box efficiency 0.97 [P.U] Gear ratio-machine/Turbine 60 Equation for power coefficient Mode 2

turbines: (a) DFIG (left) and (b) PMSG (right).

#### 2. Configuration of the hybrid ESS of the variable speed wind turbines

The configuration of the HYESS-DFIG is illustrated in Figure 1(a). DFIG is a variable speed wind turbine with a partial scale power converter in the rotor circuit. The main objective of the Analysis of Various Energy Storage Systems for Variable Speed Wind Turbines http://dx.doi.org/10.5772/intechopen.72294 125

be controlled by the power electronic circuit. Two variable speed wind turbines (VSWTs) on a

DFIG-based wind turbine is a wound rotor induction generator. It consists of three-phase windings on the rotor and stator. The stator winding of the wind turbine is directly connected to the grid winding, while the rotor winding of the generator is fed by variable frequency bidirectional back-to-back PWM based on the voltage source converter, which consists of rotor side converter (RSC) and grid side converter (GSC). It has typically about 30% of nominal generator power [3, 4]. The synchronous machine has the ability to provide its own excitation on the rotor. Such excitation may be obtained by means of either a current carrying winding or permanent magnets (PMs) [5]. PM excitation avoids the field current supply or reactive power compensation facilities [2, 6–8]. PMSG is a permanent magnet synchronous machine with its stator windings connected to the grid through a frequency converter. Signal frequency is generated via the pulse width modulation (PWM) with the DC link of back-to-back voltage source converters (VSCs) consisting of machine side converter and grid side converter [9, 10]. There are several state-of-the-art technologies on wind power and energy storage to improve power quality and stability by optimal control of ESS [11–17], hybrid ESS [18, 19], and renewable forecasting modeling [20, 21]. It is widely popularized that an elaborately optimal control strategy for ESS is to smooth wind power fluctuations for the renewable energy [11–14]. Two configurations of DFIG-BESS for internal and external controllers have improved the capability integrated to the grid [15]. In [16], an optimal control scheduling was considered on the variable smoothing time constant and charging/discharging of power limits, which is to mitigate the wind power fluctuations, while extending the battery life cycle of the BESS. A control strategy of the ESS in the wind farm focused an open-loop optimal control scheme is to incorporate the operating limits of BESS based on the forecasted wind condition [17]. The author [18] introduced the hybrid energy storage system (HESS) to overcome the fast PVwind power generation fluctuations by smart scheduling, which has a statistical approach for the capacity distribution of the HESS. However, they did not demonstrate real-time simulation with few limited cases. The control and energy management of the hybrid ESS (battery and super capacitor) with DFIG has coordinated the power flows and load demand [19]. Prediction scheme of wind power generation connected to battery ESS developed by using numeric weather prediction model which required to the input as detailed wind information [20]. However, they did not smooth to power variation scheme in this chapter. Energy management system of flywheel ESS used to fuzzy logic for two optimized models as constraint condition

principle of power electronics could be classified as DFIGs and PMSGs [3, 4].

124 Advancements in Energy Storage Technologies

and determine optimization objective to control of FESS [21].

2. Configuration of the hybrid ESS of the variable speed wind turbines

The configuration of the HYESS-DFIG is illustrated in Figure 1(a). DFIG is a variable speed wind turbine with a partial scale power converter in the rotor circuit. The main objective of the

Figure 1. Block diagram of the hybrid energy storage system connected to the configuration variable speed wind turbines: (a) DFIG (left) and (b) PMSG (right).


Table 1. PSCAD/EMTDC parameters of the wind turbine for DFIG, PMSG in the proposed systems [23].

RSC controller is to regulate active power (Ps) and reactive power (Qs) of the stator side independently, while the GSC controller is to keep the constant DC voltage (Vdc) to adjust reactive power (Qg) of the GSC, which transfers from the grid side [22]. Figure 1(b) describes that PMSG which is a variable speed wind turbine with a direct-drive generator connected to the grid through a full-scale power converter [2, 6]. Three different ESS such as battery (B), super capacitor (SC), and electrical dual layer capacitor (EDLC) were introduced in Figure 1. Each ESS controller comprised bank, inductance and two-quadrant DC/DC converter connected to the DC link as illustrated in Figure 1. Two DC/DC converter design includes two insulated gate Bi-polar junction transistors (IGBTs) diode switches as S1 and S2. To compare two different VSWTs, that is, DFIG and PMSG, three-phase voltage sources are employed at 20 kV, 60 Hz, 0.04 H, and 2.5 Ω, and PSCAD parameter of the wind turbine given in Table 1.

#### 2.1. Model of the wind turbines

Variable speed wind turbine could be mathematically described by the follows (1), (2), (3). A wind turbine extracts kinetic energy (Pwind(kin)) from the swept area (Area) of the blades. The power of the airflow (PairflowÞ is given by [3, 24]:

$$P\_{\text{air}} = \frac{1}{2} \cdot \rho \cdot \text{Area} \cdot \text{v}^3 \tag{1}$$

model is used to explain about the control scheme of an induction machine. The equation of

dϕ

dϕ

The equivalent two-phase model of the symmetrical variable speed wind turbine (dq frame)

dϕsd

dϕsq

dϕrd

dϕrq

The stator and rotor fluxes using the synchronously rotating reference frame (d,q frame) are

ϕsd ¼ Lsisd þ Lmird <sup>ϕ</sup>sq <sup>¼</sup> Lsisq <sup>þ</sup> Lmirq (

ϕrd ¼ Lrird þ Lmisd <sup>ϕ</sup>rq <sup>¼</sup> Lrirq <sup>þ</sup> Lmisq (

where Rs, Rr indicate equivalent resistance of stator and rotor windings. Ls, Lr, Lm indicate self and mutual inductances of stator and rotor windings, respectively. Ignoring the power losses in the stator and rotor resistances, the active and reactive powers from the stator are

PS <sup>¼</sup> <sup>3</sup>

QS <sup>¼</sup> <sup>3</sup>

dt <sup>þ</sup> <sup>j</sup>ωs∅<sup>s</sup> (8)

http://dx.doi.org/10.5772/intechopen.72294

127

dt <sup>þ</sup> <sup>j</sup>ωr∅<sup>r</sup> (9)

dt � <sup>ω</sup>sϕsq (12)

dt <sup>þ</sup> <sup>ω</sup>sϕsd (13)

dt � <sup>ω</sup>rϕrq (14)

dt <sup>þ</sup> <sup>ω</sup>rϕrq (15)

<sup>2</sup> vsd∙isd <sup>þ</sup> vsqisq � � (18)

<sup>2</sup> vsd∙isd � vsqisq � � (19)

(16)

(17)

∅<sup>s</sup> ¼ Lsis þ Lmir (10)

Analysis of Various Energy Storage Systems for Variable Speed Wind Turbines

∅<sup>r</sup> ¼ Lmis þ Lrir (11)

Vs ¼ Rsis þ

Vr ¼ Rrir þ

used to synchronously rotating reference frame (dq frame) as follows [7]:

vsd ¼ Rs∙isd þ

vsq ¼ Rs∙isq þ

vrd ¼ Rr∙ird þ

vrq ¼ Rr∙irq þ

given as follows [5, 8, 9, 22]:

given by

voltage vector in the DFIGs is expressed as follows [4, 5, 22, 26]:

where ρ, Area, v is the symbol of air density (approximately 1.225 kg=m3), swept area of rotor m<sup>2</sup> � �, upwind free wind speed (m/s), respectively.

The wind power (Pwind kin ð ÞÞ transferred to the rotor of VSWT could be expressed by

$$\mathbf{P}\_{\rm wind(kin)} = \mathbf{C}\_{\rm pt}(\lambda, \beta) \cdot \frac{1}{2} \cdot \rho \cdot \text{Area} \cdot \mathbf{v}^3 \tag{2}$$

where Cpt λ; β � � is a power coefficient, λ is a function of tip speed ratio , and β is pitch angle. Wind turbines are characterized by their aerodynamic torque or turbine torque as Ta [3, 24, 25]:

$$\text{Ta} = \frac{\mathbb{C}\_{\text{pt}}(\lambda, \beta) \frac{1}{2} \cdot \rho \cdot \text{Area} \cdot \text{v}^3}{\Omega\_t} \tag{3}$$

where A is the maximum value of Cpt defined by the Betz limit, which a turbine can never extract more than 59.3% of the power from an air stream [21, 25]. In reality, wind turbine rotors have maximum Cp values in the range 25–45% [7].

#### 2.2. Park's model of the VSWT

For our proposed system, the Park's model was used in VSWT (DFIG and PMSG) [3]. The voltage equations in the d, q axes-frame are given as follows [4, 5, 22, 26]:

$$V\_d = \frac{d\phi\_d}{dt} - \omega\_r \phi\_q + R\_d I\_d \tag{4}$$

$$V\_q = \frac{d\phi\_q}{dt} + \omega\_r \phi\_d + R\_d I\_q \tag{5}$$

$$
\omega\_r = \frac{d\theta}{dt} \tag{6}
$$

$$
\theta = \int\_0^t \omega\_r dt + \theta\_0 \tag{7}
$$

ϕd, ϕq, Id, Iq, Vd, Vq are the stator fluxes, current and voltage of the d and q components; Ra is the stator resistance; ω<sup>r</sup> is the rotor speed in electrical [radians/s]. The general Park's model is used to explain about the control scheme of an induction machine. The equation of voltage vector in the DFIGs is expressed as follows [4, 5, 22, 26]:

2.1. Model of the wind turbines

126 Advancements in Energy Storage Technologies

power of the airflow (PairflowÞ is given by [3, 24]:

m<sup>2</sup> � �, upwind free wind speed (m/s), respectively.

have maximum Cp values in the range 25–45% [7].

2.2. Park's model of the VSWT

Variable speed wind turbine could be mathematically described by the follows (1), (2), (3). A wind turbine extracts kinetic energy (Pwind(kin)) from the swept area (Area) of the blades. The

where ρ, Area, v is the symbol of air density (approximately 1.225 kg=m3), swept area of rotor

where Cpt λ; β � � is a power coefficient, λ is a function of tip speed ratio , and β is pitch angle. Wind turbines are characterized by their aerodynamic torque or turbine torque as Ta [3, 24, 25]:

where A is the maximum value of Cpt defined by the Betz limit, which a turbine can never extract more than 59.3% of the power from an air stream [21, 25]. In reality, wind turbine rotors

For our proposed system, the Park's model was used in VSWT (DFIG and PMSG) [3]. The

<sup>ω</sup><sup>r</sup> <sup>¼</sup> <sup>d</sup><sup>θ</sup>

θ ¼ ðt 0

ϕd, ϕq, Id, Iq, Vd, Vq are the stator fluxes, current and voltage of the d and q components; Ra is the stator resistance; ω<sup>r</sup> is the rotor speed in electrical [radians/s]. The general Park's

voltage equations in the d, q axes-frame are given as follows [4, 5, 22, 26]:

Vd <sup>¼</sup> <sup>d</sup>ϕ<sup>d</sup>

Vq <sup>¼</sup> <sup>d</sup>ϕ<sup>q</sup>

Ωt

1 2

<sup>2</sup> ∙ρ∙Area∙v3

∙ρ∙Area∙v3 (1)

∙ρ∙Area∙v3 (2)

dt � <sup>ω</sup>rϕ<sup>q</sup> <sup>þ</sup> RaId (4)

dt <sup>þ</sup> <sup>ω</sup>rϕ<sup>d</sup> <sup>þ</sup> RaIq (5)

dt (6)

ωrdt þ θ<sup>0</sup> (7)

(3)

Pair <sup>¼</sup> <sup>1</sup> 2

The wind power (Pwind kin ð ÞÞ transferred to the rotor of VSWT could be expressed by

Pwind kin ð Þ <sup>¼</sup> Cpt <sup>λ</sup>; <sup>β</sup> � �<sup>∙</sup>

Ta <sup>¼</sup> Cpt <sup>λ</sup>; <sup>β</sup> � � <sup>1</sup>

$$V\_s = R\_s i\_s + \frac{d\phi}{dt} + j\omega\_s \mathcal{Q}\_s \tag{8}$$

$$V\_r = R\_r i\_r + \frac{d\phi}{dt} + j\omega\_r \mathcal{O}\_r \tag{9}$$

$$\mathbf{Q}\_{\rm s} = \mathbf{L}\_{\rm s} \mathbf{i}\_{\rm s} + \mathbf{L}\_{\rm m} \mathbf{i}\_{\rm r} \tag{10}$$

$$\mathcal{Q}\_r = L\_m i\_s + L\_r i\_r \tag{11}$$

The equivalent two-phase model of the symmetrical variable speed wind turbine (dq frame) used to synchronously rotating reference frame (dq frame) as follows [7]:

$$
\omega\_{sd} = R\_s \cdot i\_{sd} + \frac{d\phi\_{sd}}{dt} - \omega\_s \phi\_{sq} \tag{12}
$$

$$
\omega\_{sq} = R\_s \cdot i\_{sq} + \frac{d\phi\_{sq}}{dt} + \omega\_s \phi\_{sd} \tag{13}
$$

$$
\omega\_{rd} = R\_r \cdot i\_{rd} + \frac{d\phi\_{rd}}{dt} - \omega\_r \phi\_{rq} \tag{14}
$$

$$
\omega\_{r\eta} = R\_r \cdot i\_{r\eta} + \frac{d\phi\_{r\eta}}{dt} + \omega\_r \phi\_{r\eta} \tag{15}
$$

The stator and rotor fluxes using the synchronously rotating reference frame (d,q frame) are given as follows [5, 8, 9, 22]:

$$\begin{cases} \phi\_{\rm sd} = \mathbf{L}\_{\rm s} \mathbf{i}\_{\rm sd} + \mathbf{L}\_{\rm m} \mathbf{i}\_{\rm rd} \\\\ \phi\_{\rm sd} = \mathbf{L}\_{\rm s} \mathbf{i}\_{\rm sd} + \mathbf{L}\_{\rm m} \mathbf{i}\_{\rm rd} \end{cases} \tag{16}$$

$$\begin{cases} \phi\_{rd} = L\_r \dot{\imath}\_{rd} + L\_m \dot{\imath}\_{sd} \\ \phi\_{rq} = L\_r \dot{\imath}\_{rq} + L\_m \dot{\imath}\_{sq} \end{cases} \tag{17}$$

where Rs, Rr indicate equivalent resistance of stator and rotor windings. Ls, Lr, Lm indicate self and mutual inductances of stator and rotor windings, respectively. Ignoring the power losses in the stator and rotor resistances, the active and reactive powers from the stator are given by

$$P\_S = \frac{\mathfrak{Z}}{2} \left( \upsilon\_{sd} \cdot \dot{\mathfrak{z}}\_{sd} + \upsilon\_{sq} \dot{\mathfrak{z}}\_{sq} \right) \tag{18}$$

$$Q\_S = \frac{3}{2} \left( \upsilon\_{sd} \cdot \dot{\mathbf{i}}\_{sd} - \upsilon\_{sq} \dot{\mathbf{i}}\_{sq} \right) \tag{19}$$

$$P\_r = \frac{3}{2} \left( \upsilon\_{rd} \cdot i\_{rd} + \upsilon\_{r\eta} i\_{r\eta} \right) \tag{20}$$

$$Q\_r = \frac{\mathfrak{Z}}{2} \left( \upsilon\_{rd} \cdot i\_{rd} + \upsilon\_{r\eta} i\_{r\eta} \right) \tag{21}$$

The electromagnetic torque is given by [4, 5, 22, 26]:

$$T\_e = \frac{3}{2} p \phi\_s i\_{s\eta} = -\frac{3}{2} p \phi\_s \frac{L\_m}{L\_s} i\_{r\eta} = -\frac{3}{2} p \frac{V\_{s\eta}}{\omega\_s} \frac{L\_m}{L\_s} i\_{r\eta} \tag{22}$$

The synchronous model is also expressed in the (d,q) synchronous Park's model, and the voltage equations of the PMSG are represented as follows [7]:

$$\frac{d\dot{\mathbf{u}}\_{sd}}{dt} = \frac{1}{L\_d} \left( -R\_s \dot{\mathbf{i}}\_{sd} + \omega L\_q \dot{\mathbf{i}}\_{sq} - V\_{sd} \right) \tag{23}$$

$$\frac{d\dot{\mathbf{u}}\_{sq}}{dt} = \frac{1}{L\_{\eta}} \left( -R\_{s}\dot{\mathbf{i}}\_{sq} - \omega\_{c}L\_{d}\dot{\mathbf{i}}\_{sd} - V\_{s\eta} + \omega\_{\text{PM}}\mathfrak{D}\_{f} \right) \tag{24}$$

where Vsd, Vsq, isd and isq are voltage and current for the d,q axis of the stator side. ∅<sup>f</sup> is the magnitude of the flux linkages by using the permanent magnetic flux [Web]; ωPM is the value as rotating speed [rad/s] of the PMSG. The electromagnetic (EM) torque in the rotor could be expressed as follows [7]:

$$T\_e = \frac{3}{2} \text{ p } \mathfrak{D}\_f \mathfrak{i}\_{sq} \tag{25}$$

The reference value of the iess can be made by using a voltage feedback control between Vdc-ref

Figure 2. Block diagram of the ESS in PSCAD/EMTDC: modeling of ESS (upper) [14] and controller of ESS (lower) [29].

kvi s

The inner current control loop using a PI controller of current can be produced by using the

kbi s

Finally, the gate signal is generated by comparing the difference between the duty ratio and the

The HYESS which is applied to the VSWT system uses DC-DC converters with controller of the ESS illustrated in Figure 2. When active power of DFIGs and PMSGs in the rotor side (Pr) or

vdc₋ref � vdc (26)

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iess₋ref � iess (27)

and Vdc with PI controller of the voltage. It can be expressed as follows [2, 25, 28]:

iess₋ref ¼ kvp þ

duty ratio to generate the gate-signal iess₋ref , as given by Eq. (27) [2, 25, 28]:

carrier frequency.

Table 2. Properties of three different ESSs [23].

D ¼ kbp þ

3.2. Energy flow and Total harmonic distortion of various energy storage systems

p is the number of pole pairs in the PMSG.

#### 3. Modeling and control of the grid connected to various ESSs

Energy storage systems (ESSs) have a function of converting electrical energy from a power system network into a form that can be stored for converting back to electrical energy when needed [8, 9]. ESS has numerous applications including portable devices, smart grid, building integration, energy efficiency, transport vehicles, and stationary renewable energy resources [27]. In this chapter, only three different distributed ESSs for renewable generation systems were introduced as battery, super capacitor, and electrical dual layer capacitor [23]. Table 2 summarizes the characteristics of three different ESSs.

#### 3.1. Modeling and control of energy storage systems

Figure 2 shows that each E-ES, for this study, consists of an energy source bank, an inductance and a two-quadrant DC/DC converter connected to the DC link. It also describes how the controller of the DC/DC buck-boost mode generates the gate signals for gate 1 and gate 2 [23].


Table 2. Properties of three different ESSs [23].

Pr <sup>¼</sup> <sup>3</sup>

Qr <sup>¼</sup> <sup>3</sup>

<sup>p</sup>ϕsisq ¼ � <sup>3</sup>

2 pϕ<sup>s</sup> Lm Ls

The synchronous model is also expressed in the (d,q) synchronous Park's model, and the

where Vsd, Vsq, isd and isq are voltage and current for the d,q axis of the stator side. ∅<sup>f</sup> is the magnitude of the flux linkages by using the permanent magnetic flux [Web]; ωPM is the value as rotating speed [rad/s] of the PMSG. The electromagnetic (EM) torque in the rotor could be

Energy storage systems (ESSs) have a function of converting electrical energy from a power system network into a form that can be stored for converting back to electrical energy when needed [8, 9]. ESS has numerous applications including portable devices, smart grid, building integration, energy efficiency, transport vehicles, and stationary renewable energy resources [27]. In this chapter, only three different distributed ESSs for renewable generation systems were introduced as battery, super capacitor, and electrical dual layer capacitor [23]. Table 2

Figure 2 shows that each E-ES, for this study, consists of an energy source bank, an inductance and a two-quadrant DC/DC converter connected to the DC link. It also describes how the controller of the DC/DC buck-boost mode generates the gate signals for gate 1 and gate 2 [23].

Te <sup>¼</sup> <sup>3</sup>

3. Modeling and control of the grid connected to various ESSs

�Rsisd þ ωLqisq � Vsd

�Rsisq � ωeLdisd � Vsq þ ωPM∅<sup>f</sup>

The electromagnetic torque is given by [4, 5, 22, 26]:

128 Advancements in Energy Storage Technologies

Te <sup>¼</sup> <sup>3</sup> 2

> disq dt <sup>¼</sup> <sup>1</sup> Lq

expressed as follows [7]:

p is the number of pole pairs in the PMSG.

summarizes the characteristics of three different ESSs.

3.1. Modeling and control of energy storage systems

voltage equations of the PMSG are represented as follows [7]:

disd dt <sup>¼</sup> <sup>1</sup> Ld <sup>2</sup> vrd∙ird <sup>þ</sup> vrqirq

<sup>2</sup> vrd∙ird <sup>þ</sup> vrqirq

irq ¼ � <sup>3</sup> 2 p Vsq ωs Lm Ls

(20)

(21)

(23)

<sup>2</sup> <sup>p</sup> <sup>∅</sup><sup>f</sup> isq (25)

(24)

irq (22)

Figure 2. Block diagram of the ESS in PSCAD/EMTDC: modeling of ESS (upper) [14] and controller of ESS (lower) [29].

The reference value of the iess can be made by using a voltage feedback control between Vdc-ref and Vdc with PI controller of the voltage. It can be expressed as follows [2, 25, 28]:

$$i\_{es.ref} = \left[k\_{vp} + \frac{k\_{vi}}{s}\right] \left(v\_{dc.ref} - v\_{dc}\right) \tag{26}$$

The inner current control loop using a PI controller of current can be produced by using the duty ratio to generate the gate-signal iess₋ref , as given by Eq. (27) [2, 25, 28]:

$$D = \left[k\_{bp} + \frac{k\_{bi}}{s}\right] \left(i\_{\text{ess-ref}} - i\_{\text{ess}}\right) \tag{27}$$

Finally, the gate signal is generated by comparing the difference between the duty ratio and the carrier frequency.

#### 3.2. Energy flow and Total harmonic distortion of various energy storage systems

The HYESS which is applied to the VSWT system uses DC-DC converters with controller of the ESS illustrated in Figure 2. When active power of DFIGs and PMSGs in the rotor side (Pr) or stator side (Ps) is greater than active power of DFIG and PMSG on the grid side (Pg), the ESS charges the energy flow from the DC bus to the ESS Bank through the S1 switch and S2 diode [26, 29, 30].

Therefore, the source of ESS works to absorb active power from the DC voltage, while acts as a step-down converter when Vess boosts. The DC-DC converter design used as a buck converter circuit, and then D1 (duty ratio) of S1 in the buck mode can be computed by (28) [31]:

$$\mathbf{D1} = \left[ \mathbf{V\_{ess}} / \mathbf{V\_{dc}} \right] \tag{28}$$

When Pg is bigger than Pr, the E-ES energy discharges through S1 and S2, and energy flows to the DC bus (Vdc) [27–29]. In this case, the converter acts as a boost converter mode [27–29]. The ESS bank serves as a source to supply active power, which results in the decrease of the voltage Vess. The duty ratio D2 of S2 in the boost mode can be expressed as (29) [25]:

$$\mathbf{D}\_2 = \mathbf{1} - \mathbf{D}\_1 \tag{29}$$

Total harmonic distortion (THD) is usually expressed as a percentage of fundamental voltage

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

� 100%

vuut (31)

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E ¼ IVh (32)

Vn V1 � �<sup>2</sup>

where V1 = fundamental frequency voltage component, Vn = nth harmonic voltage component.

Batteries have been widely used in many fields, which are referred to as electrical energy storage system, which accumulate electric energy in electrochemical form and delivers direct current (DC). The promising battery which is used as energy storage devices is lithium ion (Liion) battery. Due to high electrical potential and energy density of battery, Li-ion batteries are one of the promising solutions for the storage compared to other battery options. In addition, Li-ion batteries do not have poisonous metals (lead, mercury, or cadmium) and memory effect. The main disadvantage of Li-ion battery is the required high production cost [33]. The energy

where I (A) and V(V) are current and voltage of battery and h (hours) is the charging time. For this study, a Panasonic LJ-SK84A Li-ion storage battery system [34] is used to design about the

modeling for the PSCAD battery as 8 KWh from 110 to 165 V in Figure 4.

Xn¼7 n¼2

Figure 3. PSCAD modeling of 7th order 60 Hz for THD of active power, current, voltage at the PCC.

by the expression as (31):

3.3. Battery energy storage systems (B-ESS)

of battery could be calculated by (32):

Table 3 summarizes the energy flow in the two modes, that is, buck and boost modes.

The harmonic distortion of the voltage and current waveforms is generally expressed in terms of the fundamental frequency [32]. Power injection from the HY ESS affects the power quality, which is described as voltage and current THD. In Ref. [30], the practices and requirements for harmonic control in electrical power systems are established. Figure 3 shows the PSCAD/ EMTDC modeling for the voltage (Vg), current (Ig), and active power (Pg) of the THD measurement at the point of common connection (PCC) in the proposed system. With the increasing use of the nonlinear devices, harmonic distortion of the voltage waveform is a problem which is receiving the considerable attention. Any periodic waveform of nonsinusoidal form can be synthesized by expressing it as the sum of a series of harmonics of the fundamental frequency by using Fourier analysis (30) [31]:

$$\mathbf{f}(t) = a\_0 + a\_1 \cos \omega t + a\_2 \cos 2\omega t + a\_3 \cos 3\omega t + \dots \\
+ b\_1 \cos \omega t + b\_2 \cos 2\omega t + b\_3 \cos 3\omega t + \dots \\
\tag{30}$$

where

$$a\_0 = \frac{1}{T} \int\_0^T f(t)dt, \quad a\_n = \frac{2}{T} \int\_0^T f(t) \cos n \cdot \omega t \cdot dt, \quad b\_n = \frac{2}{T} \cdot \int\_0^T f(t) \sin n \cdot \omega t \cdot dt$$


Table 3. Energy flow of the energy storage systems.

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Figure 3. PSCAD modeling of 7th order 60 Hz for THD of active power, current, voltage at the PCC.

Total harmonic distortion (THD) is usually expressed as a percentage of fundamental voltage by the expression as (31):

$$\sqrt{\sum\_{n=2}^{n=7} \left(\frac{V\_n}{V\_1}\right)^2 \times 100\%} \tag{31}$$

where V1 = fundamental frequency voltage component, Vn = nth harmonic voltage component.

#### 3.3. Battery energy storage systems (B-ESS)

stator side (Ps) is greater than active power of DFIG and PMSG on the grid side (Pg), the ESS charges the energy flow from the DC bus to the ESS Bank through the S1 switch and S2 diode

Therefore, the source of ESS works to absorb active power from the DC voltage, while acts as a step-down converter when Vess boosts. The DC-DC converter design used as a buck converter

When Pg is bigger than Pr, the E-ES energy discharges through S1 and S2, and energy flows to the DC bus (Vdc) [27–29]. In this case, the converter acts as a boost converter mode [27–29]. The ESS bank serves as a source to supply active power, which results in the decrease of the voltage

D1 ¼ Vess=Vdc ½ � (28)

D2 ¼ 1 � D1 (29)

circuit, and then D1 (duty ratio) of S1 in the buck mode can be computed by (28) [31]:

Table 3 summarizes the energy flow in the two modes, that is, buck and boost modes.

The harmonic distortion of the voltage and current waveforms is generally expressed in terms of the fundamental frequency [32]. Power injection from the HY ESS affects the power quality, which is described as voltage and current THD. In Ref. [30], the practices and requirements for harmonic control in electrical power systems are established. Figure 3 shows the PSCAD/ EMTDC modeling for the voltage (Vg), current (Ig), and active power (Pg) of the THD measurement at the point of common connection (PCC) in the proposed system. With the increasing use of the nonlinear devices, harmonic distortion of the voltage waveform is a problem which is receiving the considerable attention. Any periodic waveform of nonsinusoidal form can be synthesized by expressing it as the sum of a series of harmonics of the fundamental

fð Þt ¼a<sup>0</sup> þ a<sup>1</sup> cos ωt þ a<sup>2</sup> cos 2ωt þ a<sup>3</sup> cos 3ωt þ … þ b<sup>1</sup> cos ωt þ b<sup>2</sup> cos 2ωt þ b<sup>3</sup> cos 3ωt þ … (30)

f tð Þ cos <sup>n</sup>∙ωt∙dt, bn <sup>¼</sup> <sup>2</sup>

S1 switch and S2 diode S2 switch and S2 Diode

T ∙ ðT 0

to DC Bus

f tð Þ sin n∙ωt∙dt

Discharge (Pr or Ps < Pg): From ESS

Vess. The duty ratio D2 of S2 in the boost mode can be expressed as (29) [25]:

frequency by using Fourier analysis (30) [31]:

f tð Þdt, an <sup>¼</sup> <sup>2</sup>

Energy flow Charge (Pr or Ps > Pg): From DC bus

T ðT 0

to the ESS

Vess Increase Reduce

Bank Sink as absorb active power Source as supply active power

Converter act as Buck converter as Vdc!reduce Boost converter as Vdc!increase

<sup>a</sup><sup>0</sup> <sup>¼</sup> <sup>1</sup> T ðT 0

Switch direction of two quadratic

Table 3. Energy flow of the energy storage systems.

[26, 29, 30].

130 Advancements in Energy Storage Technologies

where

converters

Batteries have been widely used in many fields, which are referred to as electrical energy storage system, which accumulate electric energy in electrochemical form and delivers direct current (DC). The promising battery which is used as energy storage devices is lithium ion (Liion) battery. Due to high electrical potential and energy density of battery, Li-ion batteries are one of the promising solutions for the storage compared to other battery options. In addition, Li-ion batteries do not have poisonous metals (lead, mercury, or cadmium) and memory effect. The main disadvantage of Li-ion battery is the required high production cost [33]. The energy of battery could be calculated by (32):

$$E = IVh \tag{32}$$

where I (A) and V(V) are current and voltage of battery and h (hours) is the charging time. For this study, a Panasonic LJ-SK84A Li-ion storage battery system [34] is used to design about the modeling for the PSCAD battery as 8 KWh from 110 to 165 V in Figure 4.

Figure 4. Modeling of PSCAD/EMTDC: BESS.

#### 3.3.1. State of charge

In the Li-ion battery, over charging or over discharging could result in reducing the battery stack life and indirectly increasing the cost. However, accurate calculation of the state of charge (SOC: %) for battery ESS is essential for the battery outputs in powered system that aims at maximizing energy storage system's performance, extending battery life, and realizing the safe operation of the systems including EV, renewable generation, and building integration.

The SOC of the battery is defined as the ratio of the remaining capacity (Qpresent) to the nominal capacity of the cell (QnominalÞ. It could be described as (33)

$$\text{SOC} \left( \% \right) = \frac{Q\_{\text{\textquotedblleft}present}}{Q\_{\text{\textquotedblleft} nominal}} \tag{33}$$

3.4. Super capacitor ESS

3.5. Electrical dual layer capacitor ESS

Figure 5 describes about the super capacitors (SCs), which have higher power density, higher round trip efficiency, longer life cycle life, and lower capital cost per cycle than batteries [9, 37]. Therefore, SC is a good candidate for short-term storage (i.e., seconds to minutes). Assuming the initial voltage across the super capacitor (Csc) is Vsc (0) after charging for a period t, the

VscðÞ¼ <sup>t</sup> Vscð Þþ <sup>0</sup> IL∙<sup>t</sup>

Capacitors composed of two conducting plates, which are separated by an insulating material [8, 29]. Conventional capacitors store little energy due to the limited charge storage areas and the separation distance between the two charged areas and the separation distance between the two charged plates [29, 38]. However, super capacitors based on the EDL mechanism can store significantly more energy because of the large interface area and the atomic range of charge separation distances [8]. A simple resistive capacitive equivalent circuit of the EDLC-

Case I—Analysis of the Variable Speed Wind Turbines with Various ESSs for Power Quality in

The simulation results in Case I verify the performance of the hybrid ESS-VSWT by using the PSCAD design tool in terms of the THD (%). The mean wind speed is to make realistic results

by using the PSCAD/EMTDC with random noise components during 60 s in Figure 6.

where IL is the load current entering the capacitor from the unregulated power supply.

Csc

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instantaneous voltage across the capacitor Vsc is given by (34)

cell is designed in PSCAD/EMTDC as shown in Figure 5.

Figure 5. PSCAD modeling of the super capacitor as 2F and EDLC [9, 39].

4. Simulation results and discussion

terms of THD (%).

Q is usually measured in the unit of Ah or mAh.

The battery state of charge (SOC) estimation could be calculated by the Coulomb counting (CC) method through the integration of measured battery current or open circuit voltage (OCV) [35–37]. However, unknown initial SOC which caused by the measurement errors and noise in real practice can affect performance, which means unreliable SOC value results in reduced performance and potential risk to the battery system. Therefore, it is critical to develop algorithms that can estimate accurate SOC to improve the performance for the battery energy storage. There are several advanced methods which can be closer to true values over time to compensate for nonlinearity such as extended Kalman filter (EKF) and sigma-point (unscented) Kalman filter (SP/UKF) [35–37].

#### 3.4. Super capacitor ESS

Figure 5 describes about the super capacitors (SCs), which have higher power density, higher round trip efficiency, longer life cycle life, and lower capital cost per cycle than batteries [9, 37]. Therefore, SC is a good candidate for short-term storage (i.e., seconds to minutes). Assuming the initial voltage across the super capacitor (Csc) is Vsc (0) after charging for a period t, the instantaneous voltage across the capacitor Vsc is given by (34)

$$\mathbf{V}\_{\rm sc}(\mathbf{t}) = \mathbf{V}\_{\rm sc}(\mathbf{0}) + \frac{I\_L \cdot \mathbf{t}}{\mathbf{C}\_{\rm sc}} \tag{34}$$

where IL is the load current entering the capacitor from the unregulated power supply.

#### 3.5. Electrical dual layer capacitor ESS

3.3.1. State of charge

Figure 4. Modeling of PSCAD/EMTDC: BESS.

132 Advancements in Energy Storage Technologies

In the Li-ion battery, over charging or over discharging could result in reducing the battery stack life and indirectly increasing the cost. However, accurate calculation of the state of charge (SOC: %) for battery ESS is essential for the battery outputs in powered system that aims at maximizing energy storage system's performance, extending battery life, and realizing the safe operation of the systems including EV, renewable generation, and building integration.

The SOC of the battery is defined as the ratio of the remaining capacity (Qpresent) to the nominal

SOC ð Þ¼ % <sup>Q</sup>₋present

The battery state of charge (SOC) estimation could be calculated by the Coulomb counting (CC) method through the integration of measured battery current or open circuit voltage (OCV) [35–37]. However, unknown initial SOC which caused by the measurement errors and noise in real practice can affect performance, which means unreliable SOC value results in reduced performance and potential risk to the battery system. Therefore, it is critical to develop algorithms that can estimate accurate SOC to improve the performance for the battery energy storage. There are several advanced methods which can be closer to true values over time to compensate for nonlinearity such as extended Kalman filter (EKF) and sigma-point

<sup>Q</sup>₋nominal (33)

capacity of the cell (QnominalÞ. It could be described as (33)

Q is usually measured in the unit of Ah or mAh.

(unscented) Kalman filter (SP/UKF) [35–37].

Capacitors composed of two conducting plates, which are separated by an insulating material [8, 29]. Conventional capacitors store little energy due to the limited charge storage areas and the separation distance between the two charged areas and the separation distance between the two charged plates [29, 38]. However, super capacitors based on the EDL mechanism can store significantly more energy because of the large interface area and the atomic range of charge separation distances [8]. A simple resistive capacitive equivalent circuit of the EDLCcell is designed in PSCAD/EMTDC as shown in Figure 5.

#### 4. Simulation results and discussion

Case I—Analysis of the Variable Speed Wind Turbines with Various ESSs for Power Quality in terms of THD (%).

The simulation results in Case I verify the performance of the hybrid ESS-VSWT by using the PSCAD design tool in terms of the THD (%). The mean wind speed is to make realistic results by using the PSCAD/EMTDC with random noise components during 60 s in Figure 6.

Figure 5. PSCAD modeling of the super capacitor as 2F and EDLC [9, 39].

4.1. The case of the DFIG-HYESS

THD (Vthd: lower).

power quality in the system, as shown in Figure 8.

Figure 7 describes average THD (%) of a DFIG at the PCC for P, I, and V during 60 s. Table 5 shows that average THD (%) of the DFIG is a greater than other DFIG-HYESS. This result showed that the proposed HY ESS-DFIG could improve the power quality by reducing the THD. SC-ESS (2.77%), B-ESS (4.4%), and B-SC ESS (4.43%) are the best choices to enhance

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Figure 7. THD (%) of only DFIG during 60 second: active power THD (Pthd: upper), current THD (ithd: middle), voltage


Figure 6. PSCAD modeling and noise component for the mean wind speed (10 m/s).


Comparison of THD (%) with the HY ESS-VSWT (2 MW, 60 Hz, 10 m/s with random noise) during 60 s

Table 4. Comparison of THD at the PCC.

Power quality issue could be verified by the THD which was monitored at the point of common coupling (PCC) in the VSWT-HYESS as illustrated in Figure 1. The data (THD: %) in Table 4 were extracted from using excel file and then taking an average value (THD: %) during 1 min.

#### 4.1. The case of the DFIG-HYESS

Power quality issue could be verified by the THD which was monitored at the point of common coupling (PCC) in the VSWT-HYESS as illustrated in Figure 1. The data (THD: %) in Table 4 were extracted from using excel file and then taking an average value (THD: %) during

Avg THD for P Avg THD for I Avg THD for V Avg THD for P Avg THD for I Avg THD for V

Figure 6. PSCAD modeling and noise component for the mean wind speed (10 m/s).

DFIG PMSG

Comparison of THD (%) with the HY ESS-VSWT (2 MW, 60 Hz, 10 m/s with random noise) during 60 s

No ESS 3.61 6.90 9.73 14.12 4.38 7.37 B- ESS 2.90 4.44 4.58 11.31 2.78 4.94 SC-ESS 2.77 5.68 4.65 10.97 2.30 3.73 E- ESS 2.83 5.65 4.7 11.14 2.78 5.00 B-SC ESS 2.90 5.74 4.43 11.18 1.82 4.96 B- E ESS 2.89 5.75 4.69 11.14 2.79 4.93 SC-E ESS 3.02 5.73 4.60 11.23 2.77 3.71 B-SC-E ESS 3.05 6.06 4.60 1.42 1.82 3.71

1 min.

Table 4. Comparison of THD at the PCC.

134 Advancements in Energy Storage Technologies

Figure 7 describes average THD (%) of a DFIG at the PCC for P, I, and V during 60 s. Table 5 shows that average THD (%) of the DFIG is a greater than other DFIG-HYESS. This result showed that the proposed HY ESS-DFIG could improve the power quality by reducing the THD. SC-ESS (2.77%), B-ESS (4.4%), and B-SC ESS (4.43%) are the best choices to enhance power quality in the system, as shown in Figure 8.

Figure 7. THD (%) of only DFIG during 60 second: active power THD (Pthd: upper), current THD (ithd: middle), voltage THD (Vthd: lower).


4.2. In the HYESS-PMSG

THD (Vthd: lower).

(1.42%), as shown in Figures 9 and 10.

Table 5 shows that the average THD of PMSGs is bigger than HY ESS-PMSG. These results indicate that an ESS which reduces THD (%) is to be improved. SC-E-B ESS is the most effective option in reducing THD for the voltage (3.71%), current (1.82%), and active power

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Figure 9. THD (%) of only PMSG during 60 s: active power THD (Pthd: upper), current THD (ithd: middle), and voltage

Case II– Financial analysis of the DFIG with two ESSs in terms of the payback time (\$)

Table 5. Cost of 1.5 MW Wind Turbine [9].

Figure 8. THD (%) of HY ESS DFIG during 60 secs: active power THD of SCESS, current THD of BESS, voltage THD of B-SC-E ESS.

#### 4.2. In the HYESS-PMSG

Item Cost (\$) Overnight cost of DFIG 2949000.00 Pb battery ESS 16862.66 Li-ion battery ESS 11992.85 Super capacitor ESS 5574125.98

Figure 8. THD (%) of HY ESS DFIG during 60 secs: active power THD of SCESS, current THD of BESS, voltage THD of B-

Table 5. Cost of 1.5 MW Wind Turbine [9].

136 Advancements in Energy Storage Technologies

SC-E ESS.

Table 5 shows that the average THD of PMSGs is bigger than HY ESS-PMSG. These results indicate that an ESS which reduces THD (%) is to be improved. SC-E-B ESS is the most effective option in reducing THD for the voltage (3.71%), current (1.82%), and active power (1.42%), as shown in Figures 9 and 10.

Case II– Financial analysis of the DFIG with two ESSs in terms of the payback time (\$)

Figure 9. THD (%) of only PMSG during 60 s: active power THD (Pthd: upper), current THD (ithd: middle), and voltage THD (Vthd: lower).

Wind energy is free. However, financial analysis can be used to assess wind project investments such as how to screen cost and the benefits of the project. Regarding the financial analysis, the payback time depends on the amount of time to evaluate the performance of different ESSs such as super capacitor ESS (SCESS), battery ESS (BESS). Payback time is the time in which investors can recover all their investment. It depends on several factors such as O&M, inflation, and depreciation. It will show whether a project is worth establishing the investment to escape the risk. Shorter payback time indicates a more economical project. A 1.5 MW wind generator, which sells the electricity generated to utilities, can serve as an example. There are several manufacturers who provide large capacity DFIG. Generally, the overnight

cost of a wind generator is \$1966/kW. The Cost of O&M per year was assumed as \$30.98/kW-Year for this work to find payback time from the [39]. A 83.33 kWh super capacitor ESS and Li Ion battery are chosen [40, 41]. The discount rate is also assumed as 9%. The capital cost of a

where capital cost is total overnight cost of DFIG (\$) + cost of Li ion or SC ESS. The payback time for DFIG with Li ion ESS is 3.47 years; however, the payback time for DFIG with supercapacitor is 15.76 year as shown in Figure 11. If a hybrid ESS (B-SC) ESS is chosen, the payback time will be between 3.47 and 15.76 years, according to the proportion of BESS and SCESS

This section analyzes the ability of various HY ESSs based on VSWTs to improve power quality in terms of THD under the same situations: three-phase voltage sources used at 20 kV, 60 Hz, 0.04 H, and 2.5 Ω. From the previous results, HYESS-VSWT has been verified to increase the power quality by reducing THD. SC-ESS (2.77%), B-ESS (4.4%), and B-SC ESS (4.43%) of the DFIG's case would be a better option to improve power quality for active power, voltage, and current, respectively, compared to other cases. In the case of the PMSGs, SC-E-B ESS indicated as the best option to reduce THD for voltage (3.71%), current (1.82%), and active power (1.42%). Compared to average active power THD of a DFIG as 3.61% and PMSG as 14.42% without ESS, a THD (%) for PMSG has lower power quality difference output as 10.81% compared to THD (%) of DFIG. This might be a converter configuration issue because

¼ Capital Cost þ NetAnnual Saving 1ð ; 032; 838:80Þ (35)

Analysis of Various Energy Storage Systems for Variable Speed Wind Turbines

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139

Payback time in terms of accumulated cash flow could be expressed as (35)

wind generator is provided in Table 5.

1 1 þ Discount Rateð Þ 0:9% year

Figure 11. Accumulated cash flow.

given in this system.

5. Conclusion

Figure 10. THD (%) of HYESS PMSG during 60 sec: active power THD of B-SC-E ESS, current THD of B-SC-E ESS, voltage THD of B-SC-E ESS.

Figure 11. Accumulated cash flow.

Wind energy is free. However, financial analysis can be used to assess wind project investments such as how to screen cost and the benefits of the project. Regarding the financial analysis, the payback time depends on the amount of time to evaluate the performance of different ESSs such as super capacitor ESS (SCESS), battery ESS (BESS). Payback time is the time in which investors can recover all their investment. It depends on several factors such as O&M, inflation, and depreciation. It will show whether a project is worth establishing the investment to escape the risk. Shorter payback time indicates a more economical project. A 1.5 MW wind generator, which sells the electricity generated to utilities, can serve as an example. There are several manufacturers who provide large capacity DFIG. Generally, the overnight

138 Advancements in Energy Storage Technologies

Figure 10. THD (%) of HYESS PMSG during 60 sec: active power THD of B-SC-E ESS, current THD of B-SC-E ESS,

voltage THD of B-SC-E ESS.

cost of a wind generator is \$1966/kW. The Cost of O&M per year was assumed as \$30.98/kW-Year for this work to find payback time from the [39]. A 83.33 kWh super capacitor ESS and Li Ion battery are chosen [40, 41]. The discount rate is also assumed as 9%. The capital cost of a wind generator is provided in Table 5.

Payback time in terms of accumulated cash flow could be expressed as (35)

$$\left(\frac{1}{1 + \text{Discount} \cdot \text{Rate}(0.9\%)}\right)^{\text{year}} = \text{Capital} \cdot \text{Cost} + \text{NetAnnual Saving} \cdot (1,032,838.80) \tag{35}$$

where capital cost is total overnight cost of DFIG (\$) + cost of Li ion or SC ESS. The payback time for DFIG with Li ion ESS is 3.47 years; however, the payback time for DFIG with supercapacitor is 15.76 year as shown in Figure 11. If a hybrid ESS (B-SC) ESS is chosen, the payback time will be between 3.47 and 15.76 years, according to the proportion of BESS and SCESS given in this system.

#### 5. Conclusion

This section analyzes the ability of various HY ESSs based on VSWTs to improve power quality in terms of THD under the same situations: three-phase voltage sources used at 20 kV, 60 Hz, 0.04 H, and 2.5 Ω. From the previous results, HYESS-VSWT has been verified to increase the power quality by reducing THD. SC-ESS (2.77%), B-ESS (4.4%), and B-SC ESS (4.43%) of the DFIG's case would be a better option to improve power quality for active power, voltage, and current, respectively, compared to other cases. In the case of the PMSGs, SC-E-B ESS indicated as the best option to reduce THD for voltage (3.71%), current (1.82%), and active power (1.42%). Compared to average active power THD of a DFIG as 3.61% and PMSG as 14.42% without ESS, a THD (%) for PMSG has lower power quality difference output as 10.81% compared to THD (%) of DFIG. This might be a converter configuration issue because DFIGs are partially controlled by the back-to-back converter such as RSC and GSC, while PMSGs are directly connected through the back-to-back converter to the grid, which make 100% power rated. Regarding average THD, the PMSG-B E SC-ESS as 1.4% has the most outstanding output results compared to other options. Furthermore, the best option's values from the simulation results satisfy the IEEE Standard requirements, which should be less than 4% for current THD and 5% for voltage THD [30].

[7] Jayalakshmi NS, Gaonkar DN, Sai Kiran Kumar K. Dynamic modeling and performance analysis of grid connected PMSG based variable speed wind turbines with simple power conditioning system. 2012 IEEE, International Conference on Power Electronics, Drives

Analysis of Various Energy Storage Systems for Variable Speed Wind Turbines

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[8] Muyeen SM, Takahashi R, Murata T, Tamura J. Integration of an energy capacitor system with a variable-speed wind generator. IEEE Transactions on Energy Conversion. Sep 2009;

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Beach, CA, 2015 IEEE, 9-9 Nov. 2015. pp. 30-34. DOI:10.1109/IGESC.2015.7359387 [10] Li S, Haskew TA, Swatloski RP, Gathings W. Optimal and direct-current vector control of direct-driven PMSG wind turbines. IEEE Transactions on Power Electronics. May 2012;

[11] Yao DL, Choi SS, Tseng KJ, Lie TT. A statistical approach to the design of a dispatchable wind power-battery energy storage system. IEEE Transactions on Energy Conversion.

[12] Bludszuweit H, Domínguez-Navarro JA. A probabilistic method for energy storage sizing based on wind power forecast uncertainty. IEEE Transactions on Power Apparatus

[13] Mercier P, Cherkaoui R, Oudalov A. Optimizing a battery energy storage system for frequency control application in an isolated power system. IEEE Transactions on Power

[14] Brekken TKA, Yokochi A, Von Jouanne A, Halamay DA. Optimal energy storage sizing and control for wind power applications. IEEE Transactions on Sustainable Energy. Jan

[15] Sarrias-Mena R, Fernandez-Ramirez LM, Garcia-Vazquez CA, Jurado F. Dynamic evaluation of two configurations for a hybrid dfig-based wind turbine integrating battery

[16] Ding M, Chen Z, Su J, et al. Optimal control of battery energy storage system based on variable smoothing time constant. Automation of Electric Power Systems. Jan 2013;37(1):

[17] Teleke S, Baran ME, Bhattacharya S, Huang AQ. Optimal control of battery energy storage for wind farm dispatching. IEEE Transactions on Energy Conversion. Sep 2010;

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In addition, a DFIG with various ESSs is a financially attractive option if a chemical battery ESS is used. The payback time is acceptable, and it still has a benefit for the environment. If the new energy storage technology as a super capacitor is considered, the payback time right now is relatively long. However, if there is a technology breakthrough, the cost of a super capacitor will probably decrease and finally be financially attractive. Presently, a hybrid system will still be relatively affordable. This financial forecast helps achieve the high-power quality and reasonable payback time during the short period.

## Author details

Youngil Kim<sup>1</sup> \* and Robert J. Harrington<sup>2</sup>

\*Address all correspondence to: yikim01@gwmail.gwu.edu


#### References


[7] Jayalakshmi NS, Gaonkar DN, Sai Kiran Kumar K. Dynamic modeling and performance analysis of grid connected PMSG based variable speed wind turbines with simple power conditioning system. 2012 IEEE, International Conference on Power Electronics, Drives and Energy Systems. December 16-19, 2012, Bengaluru, India

DFIGs are partially controlled by the back-to-back converter such as RSC and GSC, while PMSGs are directly connected through the back-to-back converter to the grid, which make 100% power rated. Regarding average THD, the PMSG-B E SC-ESS as 1.4% has the most outstanding output results compared to other options. Furthermore, the best option's values from the simulation results satisfy the IEEE Standard requirements, which should be less than

In addition, a DFIG with various ESSs is a financially attractive option if a chemical battery ESS is used. The payback time is acceptable, and it still has a benefit for the environment. If the new energy storage technology as a super capacitor is considered, the payback time right now is relatively long. However, if there is a technology breakthrough, the cost of a super capacitor will probably decrease and finally be financially attractive. Presently, a hybrid system will still be relatively affordable. This financial forecast helps achieve the high-power quality and

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[2] Mohd. Hasan Ali. Wind Energy Systems Solutions for Power Quality and Stabilization.

[3] Anaya-Lara O, Jenkins N, Ekanayake J, Cartwright P, Hughes M. Wind Energy Conversion: Modeling and Control: Wiley. ISBN: 978-0-470-71433-1; August, 2009. 288 pp

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4% for current THD and 5% for voltage THD [30].

140 Advancements in Energy Storage Technologies

reasonable payback time during the short period.

\* and Robert J. Harrington<sup>2</sup>

1 Green Technology Center, Seoul, South Korea

EIA- 282(2009). March 2009

\*Address all correspondence to: yikim01@gwmail.gwu.edu

2 The George Washington University, Washington, D.C., USA

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Author details

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142 Advancements in Energy Storage Technologies


**Chapter 7**

**Provisional chapter**

**Optimal Design and Operation Management of**

**Battery-Based Energy Storage Systems (BESS) in** 

**Optimal Design and Operation Management of** 

DOI: 10.5772/intechopen.71640

**Battery-Based Energy Storage Systems (BESS) in**

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

and reproduction in any medium, provided the original work is properly cited.

Nowadays, due to the increased operation and maintenance cost and issues related to transportation of fuels, conventional ways of power generation are no longer an optimal solution.

**Keywords:** energy storage system, microgrid, optimal design and control, renewable

Energy storage systems (ESSs) can enhance the performance of energy networks in multiple ways; they can compensate the stochastic nature of renewable energies and support their large-scale integration into the grid environment. Energy storage options can also be used for economic operation of energy systems to cut down system's operating cost. By utilizing ESSs, it is very possible to store energy in off-peak hours with lower cost and energize the grid during peak load intervals avoiding high price spikes. Application of ESSs will also enable better utilization of distributed energy sources and provide higher controllability at supply/demand side which is helpful for load leveling or peak shaving purposes. Last but not least, ESSs can provide frequency regulation services in off-grid locations where there is a strong need to meet the power balance in different operating conditions. Each of the abovementioned applications of energy storage units requires certain performance measures and constraints, which has to be well considered in design phase and embedded in control and management strategies. This chapter mainly focuses on these aspects and provides a general framework for optimal design and operation

Amjad Anvari-Moghaddam, Jeremy Dulout,

Amjad Anvari-Moghaddam, Jeremy Dulout,

Additional information is available at the end of the chapter

management of battery-based ESSs in energy networks.

energy integration, optimization

Additional information is available at the end of the chapter

Corinne Alonso, Bruno Jammes and

Corinne Alonso, Bruno Jammes and

http://dx.doi.org/10.5772/intechopen.71640

**Microgrids**

**Microgrids**

Josep M. Guerrero

**Abstract**

**1. Introduction**

Josep M. Guerrero

**Provisional chapter**

#### **Optimal Design and Operation Management of Battery-Based Energy Storage Systems (BESS) in Microgrids Battery-Based Energy Storage Systems (BESS) in Microgrids**

**Optimal Design and Operation Management of** 

DOI: 10.5772/intechopen.71640

Amjad Anvari-Moghaddam, Jeremy Dulout, Corinne Alonso, Bruno Jammes and Josep M. Guerrero Corinne Alonso, Bruno Jammes and Josep M. Guerrero Additional information is available at the end of the chapter

Amjad Anvari-Moghaddam, Jeremy Dulout,

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.71640

#### **Abstract**

Energy storage systems (ESSs) can enhance the performance of energy networks in multiple ways; they can compensate the stochastic nature of renewable energies and support their large-scale integration into the grid environment. Energy storage options can also be used for economic operation of energy systems to cut down system's operating cost. By utilizing ESSs, it is very possible to store energy in off-peak hours with lower cost and energize the grid during peak load intervals avoiding high price spikes. Application of ESSs will also enable better utilization of distributed energy sources and provide higher controllability at supply/demand side which is helpful for load leveling or peak shaving purposes. Last but not least, ESSs can provide frequency regulation services in off-grid locations where there is a strong need to meet the power balance in different operating conditions. Each of the abovementioned applications of energy storage units requires certain performance measures and constraints, which has to be well considered in design phase and embedded in control and management strategies. This chapter mainly focuses on these aspects and provides a general framework for optimal design and operation management of battery-based ESSs in energy networks.

**Keywords:** energy storage system, microgrid, optimal design and control, renewable energy integration, optimization

#### **1. Introduction**

Nowadays, due to the increased operation and maintenance cost and issues related to transportation of fuels, conventional ways of power generation are no longer an optimal solution.

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons

With more concerns about environmental footprints and global warming together with the steady progress in green technologies, renewable energy resources (RESs) are deemed to be key enablers for sustainable energy development, cost-effective operations, and pollutant emission prevention. The use of RESs in an integrated framework with different energy sources not only enhances the system efficiency at different levels (e.g., energy generation, transmission, and distribution) but also improves the energy supply reliability and allows empowering of consumers in the different locations (such as suburban districts, countrysides, and remote/islanded areas). Additionally, with the complementary characteristics of energy storage systems (ESSs) and hybridization of energy systems, it is possible to offer more affordable and reliable source of power and introduce more controllability to the generation mix. More importantly, with the application of ESSs, the issues related to unpredictable nature of RESs (mainly solar and wind energy sources) can be resolved, and a smooth-running power supply can be guaranteed. On the other hand, implementation of an integrated energy system supported with ESSs allows energy saving at different scales. By proper charging/discharging of the ESSs, we can economically benefit from dispatching cheaper energy sources during peak load hours and saving excess energy during low-demand periods. It is noteworthy that the term "ESS" could have different definitions; however, in this chapter we are talking about a "commercially available technology that is capable of absorbing energy, storing it for a period of time, and thereafter dispatching the energy" [1]. It should be also noted that the system operation can be further improved if demand response programs (DRPs) are considered in energy management portfolio. DRPs will incentivize the users to reduce their energy consumption over peak times or to shift part of their consumptions to other time intervals for matching energy supply [2]. However, a good DRP should have two primary features: the first feature is defined as the *adaptability* to different consumers with different dispositions toward the DRP, and the second one is defined as the *adjustability* to time preferences of consumers. This means that each consumer should be able to easily shift his/her demand from the high-price hours to the favorite hours according to his/her lifestyle [3]. With this introduction on advantages of renewable energy integration and reliable backup through energy storage options, this chapter discusses different battery-based ESS (BESS) technologies and presents potentials of BESS in distribution systems. Moreover, different design criteria and methodologies for ESS sizing and planning are proposed, and a general framework for optimal operation management and control of BESSs in energy networks is developed.

(oxidizer and reducer) in contact with an electrolyte and converts chemical energy into electric energy (and vice versa for rechargeable cells) [3–5]. Since the end of the eighteenth century with the development of the Volta pile, "voltaic pile," numerous designs of batteries have been invented (with different electrode materials, electrolytes, casings, separators, management systems, etc.). Hundreds of systems have been created, but almost 20 of them are currently commercialized (mainly derived from lead, zinc, nickel, or lithium materials) [6]. As presented in **Figure 1**, electrochemical cells can be classified into three main

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• Flow batteries (also called redox flow batteries) are based on two electrolytes stored in external tanks. The electrolytes are pumped into an electrochemical cell in order to produce electricity. The energy density depends on the size of the tanks, and the power density depends on the rate of chemical reactions occurring in the electrochemical cell. These batteries can be fast to recharge by changing the electrolytes. In general, the chemical reactions

• Primary batteries cannot be easily and efficiently recharged so they are usually only discharged once and discarded. They are often used in portable electric devices such as lighting, cameras, toys, and also in-home automation sensors (e.g., smoke and movement

• Secondary batteries are rechargeable and can perform a large number of cycle charge/ discharge (100–1000). The market of rechargeable batteries comprises a very wide range of applications such as powering portable electronic devices, electric vehicles, storing surplus of energy from photovoltaic systems, etc. Since 1990, the average growth rate of rechargeable battery pack market is 5% per year [7]. For decades, lead-acid batteries (such as valve regulated and sealed) have been leading, by far, the global market of rechargeable batteries. Since the end of the 1990s, lithium-ion batteries have been gradually preferred to nickel-cadmium (Ni-Cd) and nickel-metal hydride (Ni-MH) batteries

detectors). They offer a good energy density and a good shelf life.

families:

are reversible.

**Figure 1.** Main different electrochemical technologies.

#### **2. Criteria and methodologies for battery sizing and planning**

This section provides an overview of criteria and methods that should be used to optimally size and use a battery energy storage system (BESS) for different applications.

#### **2.1. Battery technologies**

A battery is constituted of electrochemical cells connected in series, parallel, or both in order to obtain the desired capacity and voltage output. A cell consists of a set of two electrodes (oxidizer and reducer) in contact with an electrolyte and converts chemical energy into electric energy (and vice versa for rechargeable cells) [3–5]. Since the end of the eighteenth century with the development of the Volta pile, "voltaic pile," numerous designs of batteries have been invented (with different electrode materials, electrolytes, casings, separators, management systems, etc.). Hundreds of systems have been created, but almost 20 of them are currently commercialized (mainly derived from lead, zinc, nickel, or lithium materials) [6]. As presented in **Figure 1**, electrochemical cells can be classified into three main families:


**Figure 1.** Main different electrochemical technologies.

With more concerns about environmental footprints and global warming together with the steady progress in green technologies, renewable energy resources (RESs) are deemed to be key enablers for sustainable energy development, cost-effective operations, and pollutant emission prevention. The use of RESs in an integrated framework with different energy sources not only enhances the system efficiency at different levels (e.g., energy generation, transmission, and distribution) but also improves the energy supply reliability and allows empowering of consumers in the different locations (such as suburban districts, countrysides, and remote/islanded areas). Additionally, with the complementary characteristics of energy storage systems (ESSs) and hybridization of energy systems, it is possible to offer more affordable and reliable source of power and introduce more controllability to the generation mix. More importantly, with the application of ESSs, the issues related to unpredictable nature of RESs (mainly solar and wind energy sources) can be resolved, and a smooth-running power supply can be guaranteed. On the other hand, implementation of an integrated energy system supported with ESSs allows energy saving at different scales. By proper charging/discharging of the ESSs, we can economically benefit from dispatching cheaper energy sources during peak load hours and saving excess energy during low-demand periods. It is noteworthy that the term "ESS" could have different definitions; however, in this chapter we are talking about a "commercially available technology that is capable of absorbing energy, storing it for a period of time, and thereafter dispatching the energy" [1]. It should be also noted that the system operation can be further improved if demand response programs (DRPs) are considered in energy management portfolio. DRPs will incentivize the users to reduce their energy consumption over peak times or to shift part of their consumptions to other time intervals for matching energy supply [2]. However, a good DRP should have two primary features: the first feature is defined as the *adaptability* to different consumers with different dispositions toward the DRP, and the second one is defined as the *adjustability* to time preferences of consumers. This means that each consumer should be able to easily shift his/her demand from the high-price hours to the favorite hours according to his/her lifestyle [3]. With this introduction on advantages of renewable energy integration and reliable backup through energy storage options, this chapter discusses different battery-based ESS (BESS) technologies and presents potentials of BESS in distribution systems. Moreover, different design criteria and methodologies for ESS sizing and planning are proposed, and a general framework for optimal operation management and control of BESSs in energy networks is developed.

**2. Criteria and methodologies for battery sizing and planning**

size and use a battery energy storage system (BESS) for different applications.

**2.1. Battery technologies**

146 Advancements in Energy Storage Technologies

This section provides an overview of criteria and methods that should be used to optimally

A battery is constituted of electrochemical cells connected in series, parallel, or both in order to obtain the desired capacity and voltage output. A cell consists of a set of two electrodes in portable devices [7]. The historical development of the main battery chemistries and the key issues to create sustainable batteries with always higher performances are well presented in Ref. [8].

• **Maturity:** a strong scientific background is behind mature technologies which benefit from numerous user experiences. Only incremental improvements are expected. In comparison,

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149

Grid-scale storage facilities through the world have been gathered in a large database from the US DOE [10]. A full description is given for most of them such as the date of creation, the location, the technology, the rated capacity, the rated power, the use cases, a picture of the project, etc. It appears that the global storage resource is small (the operational maximum power storage is around 170–180 GW, corresponding to less than 1% of our energy production). The main storage technology (in terms of rated power) is by far pumped hydro (~96%), but electrochemical projects are the most numerous (nearly 1000) and represent nearly 2% of the total rated power. As listed in [10–12], a BESS can provide numerous benefits such as: • **Environmental:** integration of renewable sources (the variability of these sources threatens the grid stability), replacement of diesel generators (in off-grid sites), pollution reduction

• **Societal:** electricity supply in remote areas, reliability improvement (possibility to maintain the grid stability or operate separately from the utility in a so-called islanded mode),

• **Economic:** energy cost decrease (due to electric energy time-shift that enables to buy cheap energy and then sell and/or use it when it is expensive), the use of expensive thermal power plant diminution (with advanced energy management strategies), electric peak demand flattening, power factor correction, transmission and distribution (T&D) investment deferral, etc.

Every actor of electricity from the end user to the utility operator may find one or more benefits to install a BESS facility [12]. Indeed, potential synergies might be achieved, for example, by charging batteries during off-peak demand and discharging during peak; energy cost may

reduced (less power in transmission lines during on-peak demand); pollution may be reduced (because in general cleaner power plants are used for the supply of baseload demand), and T&D deferral or life extension of the utility can be fulfilled because it mainly depends on the level of the peak demand. Two typical use cases are illustrated in **Figure 2**, where (a) represents the use of energy storage in order to reduce the peak demand. In this case, the power plant responsible for the baseload generation will increase its production in order to charge the BESS (in general the cost and the pollution related to this plant are the lowest compared to the other plants that are used to meet the peak demand). During peak demand, the energy comes from the BESS which replaces costly and high-pollutant power plants. Case (b) represents a typical power production from a solar photovoltaic (PV) plant during a sunny day which is not correlated with the demand profile. The BESS is charging when there is a surplus of energy in order to ensure the stability of the grid (unintentional injection of renewable power is not allowed), and it is discharging when the cost of energy is high (i.e., flattening the

R) can be

decrease (because energy is bought cheap and sold expensive); energy losses (I2

a new technology is evolving fast thanks to breakthrough advances.

(by reducing peak demand often met with harmful and costly plants), etc.

duration of outages decreased (ESS can perform a black start), etc.

energy peak demand in the morning and in late afternoon).

**2.2. Potentials of BESS in distribution systems**

In this chapter, the analysis will be focused on secondary batteries, especially on lead-acid and lithium-ion batteries, the most popular technologies (because of an attractive price for the first cited and because of high performances in terms of energy and power densities for the latter). The main useful characteristics of a BESS, when selecting a technology, are listed below:


• **Maturity:** a strong scientific background is behind mature technologies which benefit from numerous user experiences. Only incremental improvements are expected. In comparison, a new technology is evolving fast thanks to breakthrough advances.

#### **2.2. Potentials of BESS in distribution systems**

in portable devices [7]. The historical development of the main battery chemistries and the key issues to create sustainable batteries with always higher performances are well

In this chapter, the analysis will be focused on secondary batteries, especially on lead-acid and lithium-ion batteries, the most popular technologies (because of an attractive price for the first cited and because of high performances in terms of energy and power densities for the latter). The main useful characteristics of a BESS, when selecting a technology, are listed below:

• **Response time:** a BESS has to charge/discharge in a given period (e.g., fast response time from milliseconds to seconds is needed to remove power fluctuations inherited from re-

• **Capital cost:** depending on the application, different costs are useful to be considered such as the cost of rated power (€/kW), the cost of rated capacity (€/kWh), and the cost on the

• **Operation and maintenance (O&M) cost:** every BESS has its proper O&M requirements. It is difficult to find a clear trend in the literature because it is highly dependent on the loca-

• **Specific energy (Wh/kg) and specific power (W/kg):** enables to know the BESS weight that achieves power and energy requirements of the application. Energy and power densities,

• **Cycling lifetime (number of cycles):** maximum number of cycles that the BESS can

• **Cycle efficiency (%):** also named round-trip efficiency, the energy discharged by the BESS is lower than the energy initially charged into it. This parameter can be measured by calculating the ratio between energies discharged to the energy charged *Eout*/*Ein*. This calculation

• **Self-discharge:** due to parasitic chemical reactions, the charges stored in the BESS decrease. This process can be accelerated or slowed not only by external conditions (e.g., temperature, humidity) but also by operating conditions (e.g., state of charge (*SOC*) of the battery,

• **Operating temperature (°C):** some parameters such as the efficiency, the available capac-

• **Environmental impact and safety:** the extraction of the main components and manufacturing processes of batteries have different impacts on the environment from a technology to another. These impacts can be expressed as an energy consumption or a mass of GHG emissions [9]. The toxicity of some materials and the stability of the battery (e.g., thermal runaway of lithium batteries with cobalt-based cathode) can be a crucial issue depending

ity, and the lifetime depend on the operating temperature range of the BESS.

respectively, in Wh/l and W/l, are other metric representative of the volume aspect.

presented in Ref. [8].

148 Advancements in Energy Storage Technologies

newable source production).

long run (€/(cycle kWh)).

perform.

tion (labor costs) and on the age of the facility.

should not take into account self-discharge.

previous rate of charge, etc.).

on the application.

• **Calendar lifetime (years):** maximum shelf life of the BESS.

Grid-scale storage facilities through the world have been gathered in a large database from the US DOE [10]. A full description is given for most of them such as the date of creation, the location, the technology, the rated capacity, the rated power, the use cases, a picture of the project, etc. It appears that the global storage resource is small (the operational maximum power storage is around 170–180 GW, corresponding to less than 1% of our energy production). The main storage technology (in terms of rated power) is by far pumped hydro (~96%), but electrochemical projects are the most numerous (nearly 1000) and represent nearly 2% of the total rated power. As listed in [10–12], a BESS can provide numerous benefits such as:


Every actor of electricity from the end user to the utility operator may find one or more benefits to install a BESS facility [12]. Indeed, potential synergies might be achieved, for example, by charging batteries during off-peak demand and discharging during peak; energy cost may decrease (because energy is bought cheap and sold expensive); energy losses (I2 R) can be reduced (less power in transmission lines during on-peak demand); pollution may be reduced (because in general cleaner power plants are used for the supply of baseload demand), and T&D deferral or life extension of the utility can be fulfilled because it mainly depends on the level of the peak demand. Two typical use cases are illustrated in **Figure 2**, where (a) represents the use of energy storage in order to reduce the peak demand. In this case, the power plant responsible for the baseload generation will increase its production in order to charge the BESS (in general the cost and the pollution related to this plant are the lowest compared to the other plants that are used to meet the peak demand). During peak demand, the energy comes from the BESS which replaces costly and high-pollutant power plants. Case (b) represents a typical power production from a solar photovoltaic (PV) plant during a sunny day which is not correlated with the demand profile. The BESS is charging when there is a surplus of energy in order to ensure the stability of the grid (unintentional injection of renewable power is not allowed), and it is discharging when the cost of energy is high (i.e., flattening the energy peak demand in the morning and in late afternoon).

First of all, the reliability of the distribution system can be assessed by Eqs. (1) and (2):

demand for a given period [13]:

LPSP(*t*) =

(HLOL) to the total hours of operation (HTOT) [13]:

LA = 1 − \_\_\_\_

CRF <sup>=</sup> *<sup>i</sup>*

LCOE = \_\_\_\_

where *i r*

lifetime (years).

• Loss of power supply probability (LPSP) is defined as the ratio of energy deficit to the load

Optimal Design and Operation Management of Battery-Based Energy Storage Systems (BESS)…

∫

*t*0 *t <sup>E</sup>deficit*(*t*) *dt* \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

∫

• Level of autonomy (LA) is derived from the ratio of the hours that exhibit a loss of load

Concerning the economic issue, the BESS can be analyzed by calculating the annualized cost of system (ACS). The formulation (3) is derived from [14, 15] in which the annual cost of a renewable plant (PV or WT) with batteries is calculated. In these studies a replacement cost is added in the calculation of ACS because the duration of the project is often based on the

ACS = *Ccap* × *CRF* + *CO*&*<sup>M</sup>* (3)

where *Ccap* is the initial capital cost of the BESS (€), *CRF* is the capital recovery factor defined in Eq. (4) to calculate annual equal payments over the lifetime of the BESS based on the initial

> *<sup>r</sup>* (1 + *i r*)*n* \_\_\_\_\_\_\_ (1 + *i*

is the interest rate (between 5% and 10% for such projects [16]) and *n* is the BESS

*ACS Edis*

Another popular metric used in renewable plants is the levelized cost of energy (LCOE) which indicates the total cost of energy (generally per kilowatt-hour) by taking into account the cost of all equipment involved in energy production over their entire lifetime. It can be adapted to

A good criterion to take into account the environmental aspect is the PV self-consumption Eq. (6) that can be highly improved by the integration of a BESS. A high PV self-consumption implies a good use of the PV source and a local use of produced energy (transmission losses are reduced). In case of grid-connected system, some energy is exchanged with the grid, *EDU* is the energy directly used from the PV installation to the load, *EBC* is the PV energy used to

lifetime expectancy of renewable sources which is longer than battery lifetime:

capital cost, and *CO* & *<sup>M</sup>* is the annual cost of operation and maintenance (€):

BESS by using the annualized discharged energy *Edis*, as proposed in Eq. (5):

charge the BESS, and *EPV* is the total energy produced by the PV installation:

*t*0 *t*

> HLOL HTOT

*Eload*(*t*) *dt* (1)

http://dx.doi.org/10.5772/intechopen.71640

*<sup>r</sup>*)*<sup>n</sup>* <sup>−</sup> <sup>1</sup> (4)

(2)

151

(5)

**Figure 2.** Typical use cases of a BESS, (A) peak shaving and load leveling and (B) integration of renewable sources.

#### **2.3. Criteria**

The following criteria help to quantify the benefits brought by a BESS associated to renewable sources such as solar PV panels and wind turbines (WT).

First of all, the reliability of the distribution system can be assessed by Eqs. (1) and (2):

• Loss of power supply probability (LPSP) is defined as the ratio of energy deficit to the load demand for a given period [13]:

$$\text{LPSP}(t) = \frac{\int\_{t\_\*}^{t} E\_{d\phi\text{ori}}(t) \, dt}{\int\_{t\_\*}^{t} E\_{\text{load}}(t) \, dt} \tag{1}$$

• Level of autonomy (LA) is derived from the ratio of the hours that exhibit a loss of load (HLOL) to the total hours of operation (HTOT) [13]:

$$\text{LA} = 1 - \frac{\text{H}\_{\text{tot}}}{\text{H}\_{\text{tor}}} \tag{2}$$

Concerning the economic issue, the BESS can be analyzed by calculating the annualized cost of system (ACS). The formulation (3) is derived from [14, 15] in which the annual cost of a renewable plant (PV or WT) with batteries is calculated. In these studies a replacement cost is added in the calculation of ACS because the duration of the project is often based on the lifetime expectancy of renewable sources which is longer than battery lifetime:

$$\text{ACC} = \text{C}\_{ap} \times \text{CRF} + \text{C}\_{\text{OdM}} \tag{3}$$

where *Ccap* is the initial capital cost of the BESS (€), *CRF* is the capital recovery factor defined in Eq. (4) to calculate annual equal payments over the lifetime of the BESS based on the initial capital cost, and *CO* & *<sup>M</sup>* is the annual cost of operation and maintenance (€):

$$\text{CRF} = \frac{i\_r(1+i)^\*}{(1+i)^\*-1} \tag{4}$$

where *i r* is the interest rate (between 5% and 10% for such projects [16]) and *n* is the BESS lifetime (years).

Another popular metric used in renewable plants is the levelized cost of energy (LCOE) which indicates the total cost of energy (generally per kilowatt-hour) by taking into account the cost of all equipment involved in energy production over their entire lifetime. It can be adapted to BESS by using the annualized discharged energy *Edis*, as proposed in Eq. (5):

$$\text{LCOE} = \frac{\text{ACC}}{E\_{\text{dis}}} \tag{5}$$

A good criterion to take into account the environmental aspect is the PV self-consumption Eq. (6) that can be highly improved by the integration of a BESS. A high PV self-consumption implies a good use of the PV source and a local use of produced energy (transmission losses are reduced). In case of grid-connected system, some energy is exchanged with the grid, *EDU* is the energy directly used from the PV installation to the load, *EBC* is the PV energy used to charge the BESS, and *EPV* is the total energy produced by the PV installation:

**2.3. Criteria**

150 Advancements in Energy Storage Technologies

The following criteria help to quantify the benefits brought by a BESS associated to renewable

**Figure 2.** Typical use cases of a BESS, (A) peak shaving and load leveling and (B) integration of renewable sources.

sources such as solar PV panels and wind turbines (WT).

$$\mathbf{s} = \frac{E\_{\rm DU} + E\_{\rm DC}}{E\_{\rm PV}} \tag{6}$$

**3. Modeling of a BESS**

the complexity of the problem.

**3.1. Instantaneous characteristics**

**Figure 3**:

coulombic efficiency (ampere-hour efficiency):

*SOC*(*t*) = *SOC*(*t* − 1) + *ηCh*

In order to simulate the system, a model of BESS has to be defined. In the literature, BESS models developed for the sizing and the scheduling are simple with a few parameters (e.g., nominal capacity, cycle efficiency, maximum number of cycles, etc.) in order to limit

Optimal Design and Operation Management of Battery-Based Energy Storage Systems (BESS)…

The state of charge (*SOC*) of the BESS is the parameter related to the number of charges stored in the battery (a *SOC* of 100% means that the BESS is fully charged, whereas it is considered to be empty at 0%). In [21–23], the online estimation of *SOC* named "coulomb counting" is proposed. This method is based on the measurement of current and takes into account the

where *ηCh* and *ηDis* are, respectively, the charge and discharge coulombic efficiencies of the BESS (in Ref. [21], the coulombic efficiency is considered equal to 1 during the discharge and

BESS. It is to notice that the nominal capacity of the BESS is decreasing all along the lifetime

Another variable widely used in the literature is the depth of discharge (*DOD*) which describes the emptiness of battery (complement of the *SOC*). Battery manufacturers often provide the maximum number of cycles that a battery can perform for different *DODs*, as depicted in

*DOD*(*t*) = 1 − *SOC*(*t*) (8)

In order to model the effect of other operating conditions (e.g., C-rate and temperature) on the BESS behavior, the *SOC* can be formulated by introducing the concept of equivalent current. Three technologies of batteries have been tested in Ref. [24], exhibiting both the effect of the C-rate and the temperature on the available discharged capacity. Indeed, it has been empirically formulated by Peukert for lead-acid batteries at the end of the nineteenth century that the discharged capacity is related to the C-rate. The main issue is that this relation is given for a constant level of current during all the discharge conditions (not representative of real conditions). In [25], an improved method is proposed for management of lithium-ion batteries, but the model is difficult to parameterize because it needs a lot of experimental tests to be adapted to the BESS. Usually, a BESS operates at low C-rate in renewable power plants, and the temperature can be assumed to be constant. This is why the state of health (SOH) is the

smaller than 1 during the charge, due to unwanted side reactions). *I*

current level at the charge and discharge, respectively. *Cn*

of the BESS; this point will be explained in the next section.

main parameter taken into account in sizing and planning studies.

*I Ch*(*t*) <sup>∙</sup> <sup>D</sup>*<sup>t</sup>* \_\_\_\_\_\_\_ *Cn* (*t*) <sup>−</sup> *<sup>I</sup>*

*Dis*(*t*) <sup>∙</sup> <sup>D</sup>*<sup>t</sup>* \_\_\_\_\_\_\_\_ *ηDis* ∙ *Cn*

(*t*) (7)

*Dis*(*t*) are the

*Ch*(*t*) and *I*

http://dx.doi.org/10.5772/intechopen.71640

153

(*t*) is the nominal capacity of the

Other criteria can be taken into account such as the life cycle analysis (LCA) which aims at assessing the environmental impact of a device by taking into account four life stages that are manufacturing, transportation, use, and end of life. A life cycle inventory (LCI) analysis, only focused on the manufacturing of different batteries, is presented in Ref. [9]. In such studies, some data are difficult to obtain and are often estimated (especially those concerning the manufacturing processes which are fast evolving due to improvements of technologies).

#### **2.4. Optimization techniques**

Several optimization techniques are available for the sizing and the planning of renewable energy-based systems [17]. Some popular software tools such as Hybrid Optimization Model for Electric Renewables (HOMER) and Hybrid Power System Simulation Model (HYBRID2) both developed by the National Renewable Energy Laboratory (NREL), United States, and Hybrid Optimization using Genetic Algorithm (HOGA) developed in the University of Zaragoza, Spain, are presented in Ref. [17] to simulate and optimize any microgrid configuration. Nevertheless, in order to have the highest flexibility in terms of modeling and optimization, other classical tools are commonly used such as MATLAB and General Algebraic Modeling System (GAMS).

In optimization problem, the objective function can be mono-objective (e.g., cost of the entire installation during 20 years) or multi-objective (e.g., a combination of reliability, cost, and environmental impact). Very often, the cost function of a multi-objective problem is defined as a weighted sum of multiple criteria that can be expressed in different quantities. In this case, some arbitrary weighting coefficients are necessarily introduced, and the difficulty is to determine their right value. For example, if the cost function, expressed in euros per year, evaluates the yearly cost of a BESS in a microgrid, what equivalent cost (in euros per kilogram) should be associated to the greenhouse gas (GHG) emissions induced by the production, use, and end of life of batteries? This cost depends on environmental and social impacts that are not globally standardized and are fluctuating from a year to another, whereas the mass of GHG emissions is a fixed value. In this sense, the Pareto representation is very practical because each objective is expressed in the most appropriate quantity and defines its own axis.

In Ref. [18], a robust mixed-integer linear programming (RMILP) is proposed to minimize the cost of the system. In order to take into account uncertainties such as renewable production, load demand, or costs, a stochastic simulation can be achieved through the generation of multiple Monte Carlo scenarios. Heuristic and meta-heuristic optimization techniques are very popular to find the optimal solution among a large number of solutions while using the least computational resources. Two multi-objective problems combining genetic algorithms and Pareto representation are presented in [19, 20]. This method is very promising because a large number of feasible solutions are analyzed and a set of optimal solutions, best trade-off between all criteria, are obtained.

#### **3. Modeling of a BESS**

<sup>s</sup> <sup>=</sup> *EDU* <sup>+</sup> *<sup>E</sup>* \_\_\_\_\_\_\_*BC*

**2.4. Optimization techniques**

152 Advancements in Energy Storage Technologies

Modeling System (GAMS).

between all criteria, are obtained.

*EPV*

Other criteria can be taken into account such as the life cycle analysis (LCA) which aims at assessing the environmental impact of a device by taking into account four life stages that are manufacturing, transportation, use, and end of life. A life cycle inventory (LCI) analysis, only focused on the manufacturing of different batteries, is presented in Ref. [9]. In such studies, some data are difficult to obtain and are often estimated (especially those concerning the manufacturing processes which are fast evolving due to improvements of technologies).

Several optimization techniques are available for the sizing and the planning of renewable energy-based systems [17]. Some popular software tools such as Hybrid Optimization Model for Electric Renewables (HOMER) and Hybrid Power System Simulation Model (HYBRID2) both developed by the National Renewable Energy Laboratory (NREL), United States, and Hybrid Optimization using Genetic Algorithm (HOGA) developed in the University of Zaragoza, Spain, are presented in Ref. [17] to simulate and optimize any microgrid configuration. Nevertheless, in order to have the highest flexibility in terms of modeling and optimization, other classical tools are commonly used such as MATLAB and General Algebraic

In optimization problem, the objective function can be mono-objective (e.g., cost of the entire installation during 20 years) or multi-objective (e.g., a combination of reliability, cost, and environmental impact). Very often, the cost function of a multi-objective problem is defined as a weighted sum of multiple criteria that can be expressed in different quantities. In this case, some arbitrary weighting coefficients are necessarily introduced, and the difficulty is to determine their right value. For example, if the cost function, expressed in euros per year, evaluates the yearly cost of a BESS in a microgrid, what equivalent cost (in euros per kilogram) should be associated to the greenhouse gas (GHG) emissions induced by the production, use, and end of life of batteries? This cost depends on environmental and social impacts that are not globally standardized and are fluctuating from a year to another, whereas the mass of GHG emissions is a fixed value. In this sense, the Pareto representation is very practical because

each objective is expressed in the most appropriate quantity and defines its own axis.

In Ref. [18], a robust mixed-integer linear programming (RMILP) is proposed to minimize the cost of the system. In order to take into account uncertainties such as renewable production, load demand, or costs, a stochastic simulation can be achieved through the generation of multiple Monte Carlo scenarios. Heuristic and meta-heuristic optimization techniques are very popular to find the optimal solution among a large number of solutions while using the least computational resources. Two multi-objective problems combining genetic algorithms and Pareto representation are presented in [19, 20]. This method is very promising because a large number of feasible solutions are analyzed and a set of optimal solutions, best trade-off

(6)

In order to simulate the system, a model of BESS has to be defined. In the literature, BESS models developed for the sizing and the scheduling are simple with a few parameters (e.g., nominal capacity, cycle efficiency, maximum number of cycles, etc.) in order to limit the complexity of the problem.

#### **3.1. Instantaneous characteristics**

The state of charge (*SOC*) of the BESS is the parameter related to the number of charges stored in the battery (a *SOC* of 100% means that the BESS is fully charged, whereas it is considered to be empty at 0%). In [21–23], the online estimation of *SOC* named "coulomb counting" is proposed. This method is based on the measurement of current and takes into account the coulombic efficiency (ampere-hour efficiency):

$$\text{SOC(t)} = \text{SOC(t-1)} + \eta\_{\text{co}} \frac{I\_{\text{co}}(\mathbf{t}) \cdot \mathbf{Dt}}{\mathbf{C}\_{\text{n}}(\mathbf{t})} - \frac{I\_{\text{Do}}(\mathbf{t}) \cdot \mathbf{Dt}}{\eta\_{\text{Do}} \cdot \mathbf{C}\_{\text{n}}(\mathbf{t})} \tag{7}$$

where *ηCh* and *ηDis* are, respectively, the charge and discharge coulombic efficiencies of the BESS (in Ref. [21], the coulombic efficiency is considered equal to 1 during the discharge and smaller than 1 during the charge, due to unwanted side reactions). *I Ch*(*t*) and *I Dis*(*t*) are the current level at the charge and discharge, respectively. *Cn* (*t*) is the nominal capacity of the BESS. It is to notice that the nominal capacity of the BESS is decreasing all along the lifetime of the BESS; this point will be explained in the next section.

Another variable widely used in the literature is the depth of discharge (*DOD*) which describes the emptiness of battery (complement of the *SOC*). Battery manufacturers often provide the maximum number of cycles that a battery can perform for different *DODs*, as depicted in **Figure 3**:

$$DOD(t) = 1 - SOC(t) \tag{8}$$

In order to model the effect of other operating conditions (e.g., C-rate and temperature) on the BESS behavior, the *SOC* can be formulated by introducing the concept of equivalent current. Three technologies of batteries have been tested in Ref. [24], exhibiting both the effect of the C-rate and the temperature on the available discharged capacity. Indeed, it has been empirically formulated by Peukert for lead-acid batteries at the end of the nineteenth century that the discharged capacity is related to the C-rate. The main issue is that this relation is given for a constant level of current during all the discharge conditions (not representative of real conditions). In [25], an improved method is proposed for management of lithium-ion batteries, but the model is difficult to parameterize because it needs a lot of experimental tests to be adapted to the BESS. Usually, a BESS operates at low C-rate in renewable power plants, and the temperature can be assumed to be constant. This is why the state of health (SOH) is the main parameter taken into account in sizing and planning studies.

**Figure 3.** Calendar and cycling lifetime model of the BESS derived from [27].

#### **3.2. Lifetime analysis**

Due to irreversible reactions, the active material is decreasing, and the electrode interfaces are deteriorated. Thus, the capacity decreases, and the internal resistance increases (power capability fade). In order to know when to replace a BESS, a common criterion is to consider the end of life (EOL) of a battery when its capacity drops to less than 20% of the initial nominal capacity [26]. This limit of 20% has been initially set because of the behavior of lead-acid batteries: the capacity fade is quite linear until 20%, and then there was a sudden drop of capacity. Of course all the batteries do not exhibit this large decrease of capacity; this is why some projects such as the second life of batteries have been created (old batteries that do not fulfill the automotive requirements are reused in stationary projects).

Usually, the aging of batteries is monitored by measuring the nominal capacity and comparing it to the initial nominal capacity *Cn* (*t*0 ). In this case, the battery reaches its EOL when the state of health (SOH) goes below 80%:

$$SOH(t) = \frac{\mathbb{C}\_{\ast}(t)}{\mathbb{C}\_{\ast}(t\_0)}\tag{9}$$

and cycling lifetime. As presented in **Figure 3**, experimental studies performed on lithium-ion batteries [27] revealed that the maximum number of cycles performed by the BESS is higher for low cycle depths and medium *SOC* levels (close to 50%). Assuming that the BESS will perform at least 1 cycle per day, a limit can be set on the maximum number of cycles that is

Optimal Design and Operation Management of Battery-Based Energy Storage Systems (BESS)…

Two main methods are used to estimate the aging of a BESS. In Ref. [28], a simple method called "ampere hour throughput" is based on the assumption that the exchangeable energy of a battery is fixed (because nearly constant) whatever the cycle depth performed by the BESS. In this case, the maximum energy that can be exchanged is calculated as follows:

in which the initial nominal capacity is expressed in Wh. Another method is called the rainflow counting. A very popular algorithm of rainflow counting has been presented by Downing and Socie [29]. Initially developed to estimate the effect of mechanical stress in automotive and building industries, the rainflow counting is often employed to describe the aging of batteries, as in Ref. [30]. Given a battery *SOC* time series, it is possible to extract the number of cycles with their associated cycle depth and *SOC* level and then update the value of nominal capacity.

For optimal operation of an energy system equipped with BESSs in different working modes (i.e., grid-connected or islanded), it is crucial to properly design and implement energy management systems (EMSs). These system optimizers normally determine the best possible operating scheme at supply and demand sides in terms of optimized set points for controllable units such as energy storage devices and send them as the control signals into the dedicated control system of interfacing converters. Generally, there are two types of energy/power management strategies used in energy system applications. These are named as interactive schemes based on information sharing mechanisms and passive schemes based on self-autonomy [31].

In a given interactive power/energy management system (IP/EMS), local and global system information (such as line currents, nodal voltages, frequency, and powers) is communicated in the system and exchanged between corresponding nodes in order to determine operation point of each controllable ESS or distributed generation (DG) unit. These strategies also benefit from a sort of intelligence in the integration of the computing and communications technologies which help them to define and develop the communication structure based on the computation burden of each node and other related system's objectives and constraints [32]. In this regard, three different communication schemes can be realized for an IP/EMS: centralized, decentralized, and hybrid. In each of the mentioned schemes, different communication technologies such as microwave (μW), power line carrier (PLC), fiber optics, infrared, and/or

0) (10)

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155

Emax = 2 Nmax(DOD) × DOD × *Cn*(*t*

**4. BESS power/energy management schemes**

**4.1. Interactive power/energy management strategies**

defined by the calendar aging.

The lifetime of batteries is related to calendar aging (shelf life) and cycle aging. In renewable microgrids, a BESS is subjected to variable cycling conditions. The lifetime of a BESS depends on the cycle depth and the *SOC* level (mean of *SOC* during the cycle). As shown in Ref. [27], the degradation of the nominal capacity can be considered linear for both calendar and cycling lifetime. As presented in **Figure 3**, experimental studies performed on lithium-ion batteries [27] revealed that the maximum number of cycles performed by the BESS is higher for low cycle depths and medium *SOC* levels (close to 50%). Assuming that the BESS will perform at least 1 cycle per day, a limit can be set on the maximum number of cycles that is defined by the calendar aging.

Two main methods are used to estimate the aging of a BESS. In Ref. [28], a simple method called "ampere hour throughput" is based on the assumption that the exchangeable energy of a battery is fixed (because nearly constant) whatever the cycle depth performed by the BESS. In this case, the maximum energy that can be exchanged is calculated as follows:

$$\mathcal{E}\_{\text{max}} = \mathcal{Z} \,\, \mathcal{N}\_{\text{max}}(\mathbf{DOD}) \times \mathbf{DOD} \times \mathcal{C}\_{\text{n}}(t\_0) \tag{10}$$

in which the initial nominal capacity is expressed in Wh. Another method is called the rainflow counting. A very popular algorithm of rainflow counting has been presented by Downing and Socie [29]. Initially developed to estimate the effect of mechanical stress in automotive and building industries, the rainflow counting is often employed to describe the aging of batteries, as in Ref. [30]. Given a battery *SOC* time series, it is possible to extract the number of cycles with their associated cycle depth and *SOC* level and then update the value of nominal capacity.

#### **4. BESS power/energy management schemes**

**3.2. Lifetime analysis**

154 Advancements in Energy Storage Technologies

Due to irreversible reactions, the active material is decreasing, and the electrode interfaces are deteriorated. Thus, the capacity decreases, and the internal resistance increases (power capability fade). In order to know when to replace a BESS, a common criterion is to consider the end of life (EOL) of a battery when its capacity drops to less than 20% of the initial nominal capacity [26]. This limit of 20% has been initially set because of the behavior of lead-acid batteries: the capacity fade is quite linear until 20%, and then there was a sudden drop of capacity. Of course all the batteries do not exhibit this large decrease of capacity; this is why some projects such as the second life of batteries have been created (old batteries that do not fulfill

Usually, the aging of batteries is monitored by measuring the nominal capacity and compar-

The lifetime of batteries is related to calendar aging (shelf life) and cycle aging. In renewable microgrids, a BESS is subjected to variable cycling conditions. The lifetime of a BESS depends on the cycle depth and the *SOC* level (mean of *SOC* during the cycle). As shown in Ref. [27], the degradation of the nominal capacity can be considered linear for both calendar

(*t*) \_\_\_\_\_ *Cn*(*t*

). In this case, the battery reaches its EOL when the

0) (9)

(*t*0

the automotive requirements are reused in stationary projects).

**Figure 3.** Calendar and cycling lifetime model of the BESS derived from [27].

ing it to the initial nominal capacity *Cn*

state of health (SOH) goes below 80%:

*SOH*(*t*) <sup>=</sup> *Cn*

For optimal operation of an energy system equipped with BESSs in different working modes (i.e., grid-connected or islanded), it is crucial to properly design and implement energy management systems (EMSs). These system optimizers normally determine the best possible operating scheme at supply and demand sides in terms of optimized set points for controllable units such as energy storage devices and send them as the control signals into the dedicated control system of interfacing converters. Generally, there are two types of energy/power management strategies used in energy system applications. These are named as interactive schemes based on information sharing mechanisms and passive schemes based on self-autonomy [31].

#### **4.1. Interactive power/energy management strategies**

In a given interactive power/energy management system (IP/EMS), local and global system information (such as line currents, nodal voltages, frequency, and powers) is communicated in the system and exchanged between corresponding nodes in order to determine operation point of each controllable ESS or distributed generation (DG) unit. These strategies also benefit from a sort of intelligence in the integration of the computing and communications technologies which help them to define and develop the communication structure based on the computation burden of each node and other related system's objectives and constraints [32]. In this regard, three different communication schemes can be realized for an IP/EMS: centralized, decentralized, and hybrid. In each of the mentioned schemes, different communication technologies such as microwave (μW), power line carrier (PLC), fiber optics, infrared, and/or wireless radio networks (such as global system for mobile (GSM) communications and code division multiple access (CDMA)) can be effectively used and integrated into the existing infrastructures [33, 34].

with appropriate built-in redundancy and massive communication expenditure. The latter is not a challenging problem in small-scale networks, but it could be problematic for larger systems as the complexity of the centralized optimization grows exponentially with the number

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Distributed P/EMS is the second interactive scheme for management of a given system in which there is no central supervisory unit, but all the local controllers are connected and communicate with each other through a communication bus [41]. In this sense, each controller not only captures local measurements but also receives information from neighboring nodes which helps in decision-making process according to different optimization objectives [42–43]. In this scheme, intelligent algorithms are often used for better exploration/ exploitation of the environment in order to find optimal operation point. **Figure 5** shows the block diagram of a decentralized P/EMS. A distributed scheme has some advantages over a centralized one. First, it supports a scalable structure with Plug-and-Play (PnP) feature for newly added/removed energy sources or load blocks. Second, computation burden of each local controller is mitigated which in turn reduces the required communication bandwidth. Finally, a distributed P/EMS could improve the redundancy and modularity of the system where it is needed. However, there is still a problem if a communication link fails in the system. This failure would not end to a total system collapse, but the performance of the system would not be optimal any longer. Also, a distributed P/EMS suffers from degradation of performance on small/medium networks, increased use of database space, and complex use and administration. Multi-agent system (MAS) is one of the best illustrations

of units (control variables) in the system.

*4.1.2. Distributed P/EMS*

for a distributed scheme [44].

**Figure 5.** Block diagram of a decentralized P/EMS.

#### *4.1.1. Centralized P/EMS*

In a centralized P/EMS, also known as a supervisory scheme, there is a centralized entity or a control center that monitors the system's behavior, collects information from different parts of the network, makes decisions based on the observations, and accordingly updates set points for the controllable units in supply/demand sides [35–37]. In other words, a centralized P/EMS acts as a master unit, while other local controllers within the system are treated as slaves to follow the reference signals coming from the master unit as shown in **Figure 4**. To improve the effectiveness of a P/EMS, it is also very important to clearly define system's objectives and constraints. These objectives (such as operating cost minimization, emission mitigation, power loss reduction, *SOC* equalization, etc.) together with the constraints might be conflicting in some cases which in turn make the optimal decision-making process a difficult or even an impossible task. Different examples of centralized P/EMS for microgrids can be found in the literature [38–40]. The advantages of a centralized scheme mainly lie within the simplicity of implementation and globality of optimal solution; however, it brings two disadvantages: single point of failure which implies that a centralized P/EMS has to be securely designed

**Figure 4.** Block diagram of a centralized P/EMS.

with appropriate built-in redundancy and massive communication expenditure. The latter is not a challenging problem in small-scale networks, but it could be problematic for larger systems as the complexity of the centralized optimization grows exponentially with the number of units (control variables) in the system.

#### *4.1.2. Distributed P/EMS*

wireless radio networks (such as global system for mobile (GSM) communications and code division multiple access (CDMA)) can be effectively used and integrated into the existing

In a centralized P/EMS, also known as a supervisory scheme, there is a centralized entity or a control center that monitors the system's behavior, collects information from different parts of the network, makes decisions based on the observations, and accordingly updates set points for the controllable units in supply/demand sides [35–37]. In other words, a centralized P/EMS acts as a master unit, while other local controllers within the system are treated as slaves to follow the reference signals coming from the master unit as shown in **Figure 4**. To improve the effectiveness of a P/EMS, it is also very important to clearly define system's objectives and constraints. These objectives (such as operating cost minimization, emission mitigation, power loss reduction, *SOC* equalization, etc.) together with the constraints might be conflicting in some cases which in turn make the optimal decision-making process a difficult or even an impossible task. Different examples of centralized P/EMS for microgrids can be found in the literature [38–40]. The advantages of a centralized scheme mainly lie within the simplicity of implementation and globality of optimal solution; however, it brings two disadvantages: single point of failure which implies that a centralized P/EMS has to be securely designed

infrastructures [33, 34].

156 Advancements in Energy Storage Technologies

*4.1.1. Centralized P/EMS*

**Figure 4.** Block diagram of a centralized P/EMS.

Distributed P/EMS is the second interactive scheme for management of a given system in which there is no central supervisory unit, but all the local controllers are connected and communicate with each other through a communication bus [41]. In this sense, each controller not only captures local measurements but also receives information from neighboring nodes which helps in decision-making process according to different optimization objectives [42–43]. In this scheme, intelligent algorithms are often used for better exploration/ exploitation of the environment in order to find optimal operation point. **Figure 5** shows the block diagram of a decentralized P/EMS. A distributed scheme has some advantages over a centralized one. First, it supports a scalable structure with Plug-and-Play (PnP) feature for newly added/removed energy sources or load blocks. Second, computation burden of each local controller is mitigated which in turn reduces the required communication bandwidth. Finally, a distributed P/EMS could improve the redundancy and modularity of the system where it is needed. However, there is still a problem if a communication link fails in the system. This failure would not end to a total system collapse, but the performance of the system would not be optimal any longer. Also, a distributed P/EMS suffers from degradation of performance on small/medium networks, increased use of database space, and complex use and administration. Multi-agent system (MAS) is one of the best illustrations for a distributed scheme [44].

**Figure 5.** Block diagram of a decentralized P/EMS.

#### *4.1.3. Hybrid P/EMS*

Hybrid scheme for power/energy management can be realized as another interactive structure that is mainly based on a combination of centralized and distributed schemes. In a hybrid structure, local controllers which are used for operation management of different energy sources are divided into groups [45]. Within each group, a centralized scheme is used to control and optimize the performance of local controllers. On a higher level, a distributed scheme is utilized to coordinate the operation of centralized controllers in different clusters for global optimization. Such a hybrid strategy can be seen in **Figure 6**.

It is notable that a hybrid P/EMS scheme is normally implemented for large-scale networks such as interconnected energy systems or microgrids, where the optimal operation of the entire system depends on cooperation and coordination of different control layers over time. By doing this hybridization, it is very possible to improve the system reliability and resiliency for long-run operations due to the unique features that inherently exist in centralized/decentralized schemes [46].

#### **4.2. Passive power/energy management strategies**

Self-autonomy of operation for a local controller without having information from neighboring nodes is the main idea of a passive power/energy management scheme (PP/EMS). In this structure, it is assumed that making an information sharing mechanism is too costly or not viable; thus, independent operation of energy sources is required. Moreover, it is needed to clearly define the control objective of each energy source to assure reliable operation of the system. Block diagram for such a power/energy management scheme is shown in **Figure 7**.

in responding to the changes in voltage and frequency and applies similar rules in operation management of converters in ac/dc sides. The droop-based control strategy works based on the assumption that the output impedance of a controllable unit (such as a micro-source) is mainly inductive, and it utilizes droop characteristics of voltage amplitude and frequency of each controllable unit to control its output. In case of a dc microgrid, bus voltages and in case of an ac microgrid the system voltage and frequency are the information sensed by each local droop controller and used subsequently to adjust output active (and/or reactive) power of a BESS or a generation unit. **Figure 8** shows such control strategy for a given dc microgrid. As can be seen in the same figure, either output power or output current can be selected as the feedback signal in droop control. For dc microgrids with power-type load, output power can

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On the other hand, when current signal is used, as shown in Eq. (12), droop coefficient *m<sup>c</sup>*

<sup>∗</sup> = *vDC*

<sup>∗</sup> = *vDC*

DC is the rated value of dc voltage; and *mp*

power-based and current-based droop controllers, while *Poi* and *i*

<sup>∗</sup> − *m<sup>c</sup>* . *i*

DCi is the output of the droop controller, i.e., the reference value of dc output voltage

current of converter #*i*, respectively. Since there is no communication requirement to fulfill the

and *m<sup>c</sup>*

be regarded as a virtual internal resistance. In that case, the implementation and design of the parallel converter system in a dc microgrid can be simplified to some extent as the control

<sup>∗</sup> − *mp* . *Poi* (11)

*oi* (12)

are the droop coefficients in

*oi* are the output power and

can

be used as droop feedback, as shown in Eq. (11).

**Figure 7.** Block diagram of a PP/EMS.

*vDCi*

*vDCi*

law is linear:

where *v*\*

of converter #*i*; *v*\*

Among the existing methods for PP/EMS, droop-based control strategy is regarded as a dominant method [47–49]. This control methodology adopts the behavior of synchronous machines

**Figure 6.** Block diagram of a hybrid P/EMS.

Optimal Design and Operation Management of Battery-Based Energy Storage Systems (BESS)… http://dx.doi.org/10.5772/intechopen.71640 159

**Figure 7.** Block diagram of a PP/EMS.

*4.1.3. Hybrid P/EMS*

158 Advancements in Energy Storage Technologies

tralized schemes [46].

**Figure 6.** Block diagram of a hybrid P/EMS.

Hybrid scheme for power/energy management can be realized as another interactive structure that is mainly based on a combination of centralized and distributed schemes. In a hybrid structure, local controllers which are used for operation management of different energy sources are divided into groups [45]. Within each group, a centralized scheme is used to control and optimize the performance of local controllers. On a higher level, a distributed scheme is utilized to coordinate the operation of centralized controllers in different clusters for global

It is notable that a hybrid P/EMS scheme is normally implemented for large-scale networks such as interconnected energy systems or microgrids, where the optimal operation of the entire system depends on cooperation and coordination of different control layers over time. By doing this hybridization, it is very possible to improve the system reliability and resiliency for long-run operations due to the unique features that inherently exist in centralized/decen-

Self-autonomy of operation for a local controller without having information from neighboring nodes is the main idea of a passive power/energy management scheme (PP/EMS). In this structure, it is assumed that making an information sharing mechanism is too costly or not viable; thus, independent operation of energy sources is required. Moreover, it is needed to clearly define the control objective of each energy source to assure reliable operation of the system. Block diagram for such a power/energy management scheme is shown in **Figure 7**. Among the existing methods for PP/EMS, droop-based control strategy is regarded as a dominant method [47–49]. This control methodology adopts the behavior of synchronous machines

optimization. Such a hybrid strategy can be seen in **Figure 6**.

**4.2. Passive power/energy management strategies**

in responding to the changes in voltage and frequency and applies similar rules in operation management of converters in ac/dc sides. The droop-based control strategy works based on the assumption that the output impedance of a controllable unit (such as a micro-source) is mainly inductive, and it utilizes droop characteristics of voltage amplitude and frequency of each controllable unit to control its output. In case of a dc microgrid, bus voltages and in case of an ac microgrid the system voltage and frequency are the information sensed by each local droop controller and used subsequently to adjust output active (and/or reactive) power of a BESS or a generation unit. **Figure 8** shows such control strategy for a given dc microgrid. As can be seen in the same figure, either output power or output current can be selected as the feedback signal in droop control. For dc microgrids with power-type load, output power can be used as droop feedback, as shown in Eq. (11).

On the other hand, when current signal is used, as shown in Eq. (12), droop coefficient *m<sup>c</sup>* can be regarded as a virtual internal resistance. In that case, the implementation and design of the parallel converter system in a dc microgrid can be simplified to some extent as the control law is linear:

$$
\upsilon^\*\_{\rm DC} = \upsilon^\*\_{\rm DC} - m\_p \cdot P\_{ol} \tag{11}
$$

$$
\boldsymbol{\upsilon}\_{\text{DC}}^{\*} = \boldsymbol{\upsilon}\_{\text{DC}}^{\*} - \boldsymbol{m}\_{c} \cdot \mathbf{i}\_{ol} \tag{12}
$$

where *v*\* DCi is the output of the droop controller, i.e., the reference value of dc output voltage of converter #*i*; *v*\* DC is the rated value of dc voltage; and *mp* and *m<sup>c</sup>* are the droop coefficients in power-based and current-based droop controllers, while *Poi* and *i oi* are the output power and current of converter #*i*, respectively. Since there is no communication requirement to fulfill the

**Author details**

Josep M. Guerrero1

**References**

Amjad Anvari-Moghaddam1

978-953-51-4708-4

2002 ISBN: 978-0-07-135978-8

\*Address all correspondence to: aam@et.aau.dk

\*, Jeremy Dulout2

1 Department of Energy Technology, Aalborg University, Denmark

2 LAAS-CNRS, Université de Toulouse, CNRS, UPS, France

consumers. IET Gener. Trans. Dist., to appear; 2017

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, Corinne Alonso2

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, Bruno Jammes2

http://dx.doi.org/10.5772/intechopen.71640

and

161

**Figure 8.** Droop control for dc microgrids.

control objectives, this control strategy is highly reliable. Moreover, this control structure could be easily extended to different energy sources while enabling true PnP features. Apart from the benefits, there are several issues in such power/energy management strategy. First, low-voltage regulation and proportional current sharing cannot be addressed directly by this method. Instead, nonlinear and adaptive droop techniques are proposed as key solutions for achieving acceptable voltage regulation at full load and ensuring proportional current sharing. Second, low X/R line impedance ratio may result in active and reactive power coupling and instability issues in low-voltage microgrid systems and cause power sharing errors for generation units [50]. Recently, several works have been done to improve the performance of a conventional droop-based control method by implementing the droop in virtual frames [51], adding virtual impedance in control loops [52], or adjusting the output voltage bandwidth [50]. However, without a coordinating unit such as a central controller or a system optimizer, it would be a challenging task to optimally manage the operation of a microgrid system with PP/EMS.

As another type of PP/EMS, maximum power point tracking (MPPT) control methodology is also applied in microgrids to maximize power extraction from RESs (mainly WTs and PVs) under all conditions [53]. In such power management technique, unit's voltage and current are sampled frequently, and the duty ratio of the interfaced converter is adjusted accordingly. However, it should be noted that in islanded renewable-based microgrids which are controlled based on MPPT principles, ESSs must also be dispatched to provide voltage and frequency regulation services [54]. Considering the drawbacks of IP/EMS and PP/EMS, it seems that a combined P/EMS structure (e.g., a consensus-based droop framework [55] or a droopbased distributed cooperative control [56]) could not only address reliability issues but also enhance control performance of the system both in grid-connected and stand-alone modes.

#### **Author details**

Amjad Anvari-Moghaddam1 \*, Jeremy Dulout2 , Corinne Alonso2 , Bruno Jammes2 and Josep M. Guerrero1


#### **References**

control objectives, this control strategy is highly reliable. Moreover, this control structure could be easily extended to different energy sources while enabling true PnP features. Apart from the benefits, there are several issues in such power/energy management strategy. First, low-voltage regulation and proportional current sharing cannot be addressed directly by this method. Instead, nonlinear and adaptive droop techniques are proposed as key solutions for achieving acceptable voltage regulation at full load and ensuring proportional current sharing. Second, low X/R line impedance ratio may result in active and reactive power coupling and instability issues in low-voltage microgrid systems and cause power sharing errors for generation units [50]. Recently, several works have been done to improve the performance of a conventional droop-based control method by implementing the droop in virtual frames [51], adding virtual impedance in control loops [52], or adjusting the output voltage bandwidth [50]. However, without a coordinating unit such as a central controller or a system optimizer, it would be a challenging task to optimally manage the operation of a microgrid system with PP/EMS.

**Figure 8.** Droop control for dc microgrids.

160 Advancements in Energy Storage Technologies

As another type of PP/EMS, maximum power point tracking (MPPT) control methodology is also applied in microgrids to maximize power extraction from RESs (mainly WTs and PVs) under all conditions [53]. In such power management technique, unit's voltage and current are sampled frequently, and the duty ratio of the interfaced converter is adjusted accordingly. However, it should be noted that in islanded renewable-based microgrids which are controlled based on MPPT principles, ESSs must also be dispatched to provide voltage and frequency regulation services [54]. Considering the drawbacks of IP/EMS and PP/EMS, it seems that a combined P/EMS structure (e.g., a consensus-based droop framework [55] or a droopbased distributed cooperative control [56]) could not only address reliability issues but also enhance control performance of the system both in grid-connected and stand-alone modes.


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[42] Anvari-Moghaddam A, Guerrero JJM, Rahimi-Kian A, Mirian MS. Optimal real-time dispatch for integrated energy systems: An ontology-based multi-agent approach. In: 7th International Symposium on Power Electronics for Distributed Generation Systems

[43] Mokhtari G, Anvari-Moghaddam A, Nourbakhsh G. Distributed control and management of renewable electric energy resources for future grid requirements. In: Mihet L, editor. Energy Management of Distributed Generation Systems. 1st, July; InTech ed.

[44] Anvari-Moghaddam A, Rahimi-Kian A, Mirian MS, Guerrero JM. A multi-agent based energy management solution for integrated buildings and microgrid system. Applied

[45] Dou CX, Duan ZS, Liu B. Two-level hierarchical hybrid control for smart power system. IEEE Transactions on Automation Science and Engineering. 2013;**10**(4):1037-1049

[46] Lu T, Wang Z, Ai Q, Lee W-J. Interactive model for energy Management of Clustered

[47] Rodriguez E, Vasquez JC, Josep M, Rodriguez-diaz E, Anvari-moghaddam A, Vasquez JC, Guerrero JM. Multi - Level Energy Management and Optimal Control of a Residential DC Microgrid. IEEE International Conference on Consumer Electronics (ICCE), Las

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[49] Tah A, Das D. An enhanced droop control method for accurate load sharing and voltage improvement of isolated and interconnected DC microgrids. IEEE Transactions on

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Janeza Trdine, Rijeka, Croatia. 2016. p. 1-24


**Chapter 8**

**Provisional chapter**

**Fabrication and Characterization of Supercapacitors**

**Fabrication and Characterization of Supercapacitors** 

Ever increasing energy demand urges to impelled extensive research in the development of new eco-friendly energy harvesting and storage technologies. Energy harvesting technology exploiting renewable energy sources is an auspicious method for sustainable, autonomous, and everlasting operation of a variety of electronic devices. A new concept of an integrated self-powered system by combining an energy harvesting device with an energy storage device has been established to harvest renewable energy and simultaneously store it for sustainable operation of electronic devices. In this chapter, describes the fabrication of a self-powered system by integrating the supercapacitor with energy harvesting devices such as nanogenerator and solar cells to power portable electronic devices. Initially synthesis and electrochemical characterization of various electroactive materials for supercapacitors and further, fabrication of supercapacitor device were discussed. In conclusion, this chapter demonstrates self-powered system by the integration of energy harvesting, energy storage module with portable electronic devices. The various result validates the feasibility of using supercapacitors as efficient energy storage components in self-powered devices. The proposed self-powered technology based on energy conversion of renewable energy to electrical energy which stored in energy storage device and it

will be used to operate several electronic devices as a self-powered device.

**Keywords:** energy harvesting, energy storage, supercapacitors, self-powered system,

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

DOI: 10.5772/intechopen.73647

**toward Self-Powered System**

**toward Self-Powered System**

Balasubramaniam Saravanakumar and

Balasubramaniam Saravanakumar and

http://dx.doi.org/10.5772/intechopen.73647

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

Ananthakumar Ramadoss,

Ananthakumar Ramadoss,

Sang-Jae Kim

Sang-Jae Kim

**Abstract**

portable electronics

#### **Fabrication and Characterization of Supercapacitors toward Self-Powered System Fabrication and Characterization of Supercapacitors toward Self-Powered System**

DOI: 10.5772/intechopen.73647

Ananthakumar Ramadoss, Balasubramaniam Saravanakumar and Sang-Jae Kim Ananthakumar Ramadoss, Balasubramaniam Saravanakumar and Sang-Jae Kim

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.73647

#### **Abstract**

Ever increasing energy demand urges to impelled extensive research in the development of new eco-friendly energy harvesting and storage technologies. Energy harvesting technology exploiting renewable energy sources is an auspicious method for sustainable, autonomous, and everlasting operation of a variety of electronic devices. A new concept of an integrated self-powered system by combining an energy harvesting device with an energy storage device has been established to harvest renewable energy and simultaneously store it for sustainable operation of electronic devices. In this chapter, describes the fabrication of a self-powered system by integrating the supercapacitor with energy harvesting devices such as nanogenerator and solar cells to power portable electronic devices. Initially synthesis and electrochemical characterization of various electroactive materials for supercapacitors and further, fabrication of supercapacitor device were discussed. In conclusion, this chapter demonstrates self-powered system by the integration of energy harvesting, energy storage module with portable electronic devices. The various result validates the feasibility of using supercapacitors as efficient energy storage components in self-powered devices. The proposed self-powered technology based on energy conversion of renewable energy to electrical energy which stored in energy storage device and it will be used to operate several electronic devices as a self-powered device.

**Keywords:** energy harvesting, energy storage, supercapacitors, self-powered system, portable electronics

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### **1. Introduction**

Recently, a massive demand on the highly reliable energy sources with higher energy density and longer life to operate advanced electronic and optoelectronic devices, which have impelled extensive research in the development of new eco-friendly energy harvesting and storage technologies [1–5]. The advancement of portable electronics, enormous demand for electric vehicles, integration, and development of internet of things (IoT) was highly demanding the high-performance energy storage device with added functionalities like flexibility, light-weight, cost-effective, renewable, and eco-friendly features [6, 7]. At the same time, the ultimatum of our society is also looking for an advanced version of multifunctional electronic devices, which are swelling day by day towards the trend of being a portable, flexible, lightweight, wearable and self-powered devices.

different energy harvesters such as solar, vibration for self-powered device applications. Briefly, (i) fabrication and integration of supercapacitor device with vibrational energy harvesters such as piezoelectric and triboelectric nanogenerator and (ii) fabrication and integration of superca-

Fabrication and Characterization of Supercapacitors toward Self-Powered System

http://dx.doi.org/10.5772/intechopen.73647

169

pacitor device with solar energy harvesters such as a dye-sensitized solar cell (DSSC).

**2.1. Fabrication and integration of supercapacitor device with vibrational energy** 

*2.1.1. Fabrication and characterization of the thermally reduced graphene oxide* 

The fabrication of thermally reduced graphene oxide nanosheets electrode is schematically represented in **Figure 1a**. Briefly, the graphene oxide (GO) was prepared from graphite by modified hummer's method [35]. The prepared GO solution was used to coat on conducting fabric by repeated dip-coating. After each coating, the GO-coated fabric was dried at 60°C for 30 min; this process was repeated for five times. The GO-coated fabric was reduced to a graphene-coated fabric by thermal treatment at 170°C for 2 h in Ar. The surface morphology of the as-prepared film was characterized through Field emission scanning electron microscopy (FE-SEM) analysis, and results are indicating that the uniform deposition of TRGO on the fabric surface which is noted in **Figure 1b**–**c**. Further, the chemical reduction of GO deposited fabrics was confirmed through Raman, Fourier transforms infrared (FT-IR), and X-ray photoelectron spectroscopy (XPS) analysis and results are presented in **Figure 2**. From Raman spectra (**Figure 2a**), two characteristic peaks were observed at 1602 (G-band) and 1354 cm−1

Recently, a higher attention has been given to the development of two dimensional (2D) materials for various application such as electronic, catalytic, energy conversion and storage application due to their exceptional property like electrical, optical and chemical properties. Among the other 2D materials, graphene offered higher electrical conductivity, surface area (2600 m2 g−1), and excellent mechanical flexibility due to their excellent physio-chemical properties by honeycomb structured carbon atom with atomic thick [24–28]. Because of higher surface area and good conductivity of graphene attracts to energy storage application. However, the specific capacitance, energy and power density of the graphene supercapacitors are lower than the expected values, which varies with synthesis methods. Until now, a various method has been adapted for synthesis of graphene in different forms like pristine graphene, graphene, reduced graphene oxide (rGO), and graphitic oxides by micromechanical exfoliation from graphite, chemical vapor deposition, chemical reduction methods [29–34]. Herein, a flexible graphene-coated fabric electrodes were fabricated by using a simple-cost effective dip-coating technique followed by thermal reduction at 170°C in Ar for 2 h. This fabrication method allows making binder-free, highly flexible, lightweight supercapacitor device and fabrication process is very simple, cost-effective and possible to extend large scale fabrication. A thin, binder-free coating of graphene allows higher electrical conductivity, surface area,

**2. Fabrication and testing of self-powered systems**

**harvesters**

and electrochemical activity.

*(TRGO)-coated fabric electrode*

In fact, there is a increasing interest in the energy generation from environment for powering the micro/nano-systems, because it is available everywhere and abundant. However, limited by time, location, weather and other factors [8, 9]. For example, solar and wind energies are intermittent energy but renewable. But, we will not get sunshine during the night time and as well as wind on our demand, which results instability or unsustainable power supply to electronic devices [10, 11]. In order to alleviate these problems, renewable energy converters like solar and vibrational harvesters would be better choice to integrate with energy storage device, to achieve sustainable operation by storing the generated electric energy from energy harvesting devices. The nanogenerator is a device which can efficiently convert mechanical energy into electrical energy through piezoelectric and triboelectrification processes from our living/working environment. However, these mechanical energy sources are uncontrollable fluctuation which reflects in output power [11–13]. Therefore, it cannot be used directly to power electronic devices. In this circumstance, an intermediate efficient energy storage system required to store this irregular renewable energy to achieve independent power source (stable and durable output). The development of self-powered micro/nano-device by integrating energy harvesting device with electrochemical energy storage devices such as supercapacitor and battery is a promising solution for the limitation of both energy harvesting and storage devices. Among them, supercapacitors are superior than lithium-ion batteries because of its higher power capacity, more extended cyclic stability, and fast charging/discharging capability, environmental benignancy, etc. [10, 14–16].

Recently, researchers have been attempted to develop a new hybrid system by integrating the energy harvesting device (solar cell and nanogenerator) along with a storage device (lithiumion battery and supercapacitor) to perform a self-powered operation [8, 17–23]. However, the obtained results are not up to real-world application level due to low energy conversion and storage efficiency of the devices as well as power management circuit, and further research is required to improve the output performance. The performance of self-powered systems will be substantially improved with a better power management circuit and a rational design of energy harvesting and storage devices. In recent years, significant endeavors have been dedicated to building an integrated sustainable self-power system for the smart electronics with the improved architecture of energy harvesting and storage devices.

This chapter describes the fabrication and electrochemical performance of the supercapacitor device with various electrode materials and integration of supercapacitor device with different energy harvesters such as solar, vibration for self-powered device applications. Briefly, (i) fabrication and integration of supercapacitor device with vibrational energy harvesters such as piezoelectric and triboelectric nanogenerator and (ii) fabrication and integration of supercapacitor device with solar energy harvesters such as a dye-sensitized solar cell (DSSC).

#### **2. Fabrication and testing of self-powered systems**

**1. Introduction**

168 Advancements in Energy Storage Technologies

weight, wearable and self-powered devices.

Recently, a massive demand on the highly reliable energy sources with higher energy density and longer life to operate advanced electronic and optoelectronic devices, which have impelled extensive research in the development of new eco-friendly energy harvesting and storage technologies [1–5]. The advancement of portable electronics, enormous demand for electric vehicles, integration, and development of internet of things (IoT) was highly demanding the high-performance energy storage device with added functionalities like flexibility, light-weight, cost-effective, renewable, and eco-friendly features [6, 7]. At the same time, the ultimatum of our society is also looking for an advanced version of multifunctional electronic devices, which are swelling day by day towards the trend of being a portable, flexible, light-

In fact, there is a increasing interest in the energy generation from environment for powering the micro/nano-systems, because it is available everywhere and abundant. However, limited by time, location, weather and other factors [8, 9]. For example, solar and wind energies are intermittent energy but renewable. But, we will not get sunshine during the night time and as well as wind on our demand, which results instability or unsustainable power supply to electronic devices [10, 11]. In order to alleviate these problems, renewable energy converters like solar and vibrational harvesters would be better choice to integrate with energy storage device, to achieve sustainable operation by storing the generated electric energy from energy harvesting devices. The nanogenerator is a device which can efficiently convert mechanical energy into electrical energy through piezoelectric and triboelectrification processes from our living/working environment. However, these mechanical energy sources are uncontrollable fluctuation which reflects in output power [11–13]. Therefore, it cannot be used directly to power electronic devices. In this circumstance, an intermediate efficient energy storage system required to store this irregular renewable energy to achieve independent power source (stable and durable output). The development of self-powered micro/nano-device by integrating energy harvesting device with electrochemical energy storage devices such as supercapacitor and battery is a promising solution for the limitation of both energy harvesting and storage devices. Among them, supercapacitors are superior than lithium-ion batteries because of its higher power capacity, more extended cyclic stability, and fast charging/discharging capability, environmental benignancy, etc. [10, 14–16].

Recently, researchers have been attempted to develop a new hybrid system by integrating the energy harvesting device (solar cell and nanogenerator) along with a storage device (lithiumion battery and supercapacitor) to perform a self-powered operation [8, 17–23]. However, the obtained results are not up to real-world application level due to low energy conversion and storage efficiency of the devices as well as power management circuit, and further research is required to improve the output performance. The performance of self-powered systems will be substantially improved with a better power management circuit and a rational design of energy harvesting and storage devices. In recent years, significant endeavors have been dedicated to building an integrated sustainable self-power system for the smart electronics with

This chapter describes the fabrication and electrochemical performance of the supercapacitor device with various electrode materials and integration of supercapacitor device with

the improved architecture of energy harvesting and storage devices.

#### **2.1. Fabrication and integration of supercapacitor device with vibrational energy harvesters**

Recently, a higher attention has been given to the development of two dimensional (2D) materials for various application such as electronic, catalytic, energy conversion and storage application due to their exceptional property like electrical, optical and chemical properties. Among the other 2D materials, graphene offered higher electrical conductivity, surface area (2600 m2 g−1), and excellent mechanical flexibility due to their excellent physio-chemical properties by honeycomb structured carbon atom with atomic thick [24–28]. Because of higher surface area and good conductivity of graphene attracts to energy storage application. However, the specific capacitance, energy and power density of the graphene supercapacitors are lower than the expected values, which varies with synthesis methods. Until now, a various method has been adapted for synthesis of graphene in different forms like pristine graphene, graphene, reduced graphene oxide (rGO), and graphitic oxides by micromechanical exfoliation from graphite, chemical vapor deposition, chemical reduction methods [29–34]. Herein, a flexible graphene-coated fabric electrodes were fabricated by using a simple-cost effective dip-coating technique followed by thermal reduction at 170°C in Ar for 2 h. This fabrication method allows making binder-free, highly flexible, lightweight supercapacitor device and fabrication process is very simple, cost-effective and possible to extend large scale fabrication. A thin, binder-free coating of graphene allows higher electrical conductivity, surface area, and electrochemical activity.

#### *2.1.1. Fabrication and characterization of the thermally reduced graphene oxide (TRGO)-coated fabric electrode*

The fabrication of thermally reduced graphene oxide nanosheets electrode is schematically represented in **Figure 1a**. Briefly, the graphene oxide (GO) was prepared from graphite by modified hummer's method [35]. The prepared GO solution was used to coat on conducting fabric by repeated dip-coating. After each coating, the GO-coated fabric was dried at 60°C for 30 min; this process was repeated for five times. The GO-coated fabric was reduced to a graphene-coated fabric by thermal treatment at 170°C for 2 h in Ar. The surface morphology of the as-prepared film was characterized through Field emission scanning electron microscopy (FE-SEM) analysis, and results are indicating that the uniform deposition of TRGO on the fabric surface which is noted in **Figure 1b**–**c**. Further, the chemical reduction of GO deposited fabrics was confirmed through Raman, Fourier transforms infrared (FT-IR), and X-ray photoelectron spectroscopy (XPS) analysis and results are presented in **Figure 2**. From Raman spectra (**Figure 2a**), two characteristic peaks were observed at 1602 (G-band) and 1354 cm−1

**Figure 1.** (a) Schematic diagram of the formation of thermally reduced graphene oxide nanosheets. (b-c) FE-SEM images of TRGO on the fabric surface. Figures are reproduced with permission from Ref. [19]. Copyright of Elsevier.

*2.1.2. Electrochemical characterization of TRGO-coated fabric electrode*

sured in 1 M H3

Ref. [19]. Copyright of Elsevier.

PO<sup>4</sup>

The electrochemical performance of the as-prepared TRGO-coated fabric electrode was mea-

**Figure 2.** (a) Raman, (b) FT-IR and (c-d) C1s XPS spectra of GO and TRGO. Figures are reproduced with permission from

voltammetry (CV) curves of the TRGO-coated fabric electrode at various sweep rates from 5 to 125 mV s−1. The resultant CV curve shows a rectangular-like shape, which indicates the electrochemical double-layer capacitance. Further, the rectangular CV curve accompanied with redox peaks at ~0.32 V of anodic scan and ~0.29 V of the cathodic scan. The co-existence of redox peaks confirmed the Faradic reaction by oxygenated functional groups (carbonyl and quinone groups) in TRGO [36, 40–42], which significantly contributes pseudocapacitance to the system (Inset of **Figure 3a**). The calculated specific capacitance at various scan rates was shown in **Figure 3b**. At a scan rate of 5 mV s−1, the higher specific capacitance of 414 F g−1 was observed and drops with increasing scan rates [43]. Further, galvanostatic charge-discharge (GCD) was measured at different current densities and results are shown in **Figure 3c**. The resultant GCD curve shows a linear and symmetric shape with a significantly low plateau. The symmetric nature of GCD confirms the double-layer capacitive nature and small plateau

electrolyte using the three-electrode system. **Figure 3a** shows the cyclic

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(D-band) of graphitic carbon in both GO and TRGO and corresponds to the defects and disorder in the hexagonal graphitic layers and optical E2g mode in-plane vibration phonon at the Brillouin zone center, respectively [26, 36, 37]. Moreover, the chemical reduction of GO into TRGO by characteristics peak shift toward lower wave number and a slight increase in ID/IG ratio from 0.95 to 0.97, it is due to structural disorder (defects) generated during thermal reduction process [33]. The FT-IR spectra (**Figure 2b**.) also clearly indicating the reduction of GO into TRGO through the observation of reduced intensities of the absorption bands at 3340 (O▬H stretching vibration), 1728 (C〓O stretching vibration), 1623 (skeletal vibration of un-oxidized graphitic domains), 1386 (O▬H deformation of C▬OH groups), 1233 (C▬OH stretching vibration), and 1057 cm−1 (C▬O stretching vibration) [38]. In XPS spectra, three peaks were observed at 284.6, 286.4, and 288.6 eV, which correspond to C〓C/C▬C (aromatic rings), C▬O (hydroxyl and epoxy), and C〓O (carbonyl) groups, respectively [26, 36]. The peak intensities of the oxygen-containing groups in the TRGO was lower than GO due to thermal reduction (**Figure 2c**–**d**). From, these results concluded that the GO was reduced to TRGO after thermal treatment in Ar environment. Moreover, thermal treatment allowed to transform sp3 to sp2 hybridization by reduction of the oxygenating functional group [39].

Fabrication and Characterization of Supercapacitors toward Self-Powered System http://dx.doi.org/10.5772/intechopen.73647 171

**Figure 2.** (a) Raman, (b) FT-IR and (c-d) C1s XPS spectra of GO and TRGO. Figures are reproduced with permission from Ref. [19]. Copyright of Elsevier.

#### *2.1.2. Electrochemical characterization of TRGO-coated fabric electrode*

(D-band) of graphitic carbon in both GO and TRGO and corresponds to the defects and disorder in the hexagonal graphitic layers and optical E2g mode in-plane vibration phonon at the Brillouin zone center, respectively [26, 36, 37]. Moreover, the chemical reduction of GO into TRGO by characteristics peak shift toward lower wave number and a slight increase in ID/IG ratio from 0.95 to 0.97, it is due to structural disorder (defects) generated during thermal reduction process [33]. The FT-IR spectra (**Figure 2b**.) also clearly indicating the reduction of GO into TRGO through the observation of reduced intensities of the absorption bands at 3340 (O▬H stretching vibration), 1728 (C〓O stretching vibration), 1623 (skeletal vibration of un-oxidized graphitic domains), 1386 (O▬H deformation of C▬OH groups), 1233 (C▬OH stretching vibration), and 1057 cm−1 (C▬O stretching vibration) [38]. In XPS spectra, three peaks were observed at 284.6, 286.4, and 288.6 eV, which correspond to C〓C/C▬C (aromatic rings), C▬O (hydroxyl and epoxy), and C〓O (carbonyl) groups, respectively [26, 36]. The peak intensities of the oxygen-containing groups in the TRGO was lower than GO due to thermal reduction (**Figure 2c**–**d**). From, these results concluded that the GO was reduced to TRGO after thermal treatment in Ar environment. Moreover, thermal treatment allowed

**Figure 1.** (a) Schematic diagram of the formation of thermally reduced graphene oxide nanosheets. (b-c) FE-SEM images

of TRGO on the fabric surface. Figures are reproduced with permission from Ref. [19]. Copyright of Elsevier.

hybridization by reduction of the oxygenating functional group [39].

to transform sp3

to sp2

170 Advancements in Energy Storage Technologies

The electrochemical performance of the as-prepared TRGO-coated fabric electrode was measured in 1 M H3 PO<sup>4</sup> electrolyte using the three-electrode system. **Figure 3a** shows the cyclic voltammetry (CV) curves of the TRGO-coated fabric electrode at various sweep rates from 5 to 125 mV s−1. The resultant CV curve shows a rectangular-like shape, which indicates the electrochemical double-layer capacitance. Further, the rectangular CV curve accompanied with redox peaks at ~0.32 V of anodic scan and ~0.29 V of the cathodic scan. The co-existence of redox peaks confirmed the Faradic reaction by oxygenated functional groups (carbonyl and quinone groups) in TRGO [36, 40–42], which significantly contributes pseudocapacitance to the system (Inset of **Figure 3a**). The calculated specific capacitance at various scan rates was shown in **Figure 3b**. At a scan rate of 5 mV s−1, the higher specific capacitance of 414 F g−1 was observed and drops with increasing scan rates [43]. Further, galvanostatic charge-discharge (GCD) was measured at different current densities and results are shown in **Figure 3c**. The resultant GCD curve shows a linear and symmetric shape with a significantly low plateau. The symmetric nature of GCD confirms the double-layer capacitive nature and small plateau

**Figure 3.** (a) CV curves of TRGO-coated fabric electrodes (inset at 5 mV s−1). (b) Specific capacitance of TRGO-coated fabrics at different scan rates. (c) GCD curves of TRGO-coated fabric electrodes. (d) Specific capacitance of TRGO-coated fabrics at different current densities. Figures are reproduced with permission from Ref. [19]. Copyright of Elsevier.

specific capacitance as a function of scan rate is shown in **Figure 4b**. The calculated single electrode specific capacitance decreased from 281 to 54 F g−1, when scan rate increased from 5 to 125 mV s−1 and it is due to inefficient diffusion of ions at higher scan rates [48, 49]. Further, GCD of solid-state device was measured at various current density, which is shown in **Figure 4c**. The resultant GCD curve shows symmetric nature, which confirms the good capacitive nature of the device. The calculated specific capacitance was shown in **Figure 4d**. The estimated specific capacitance of the cell (flexible supercapacitor) was 70.4 F g−1 at 5 mV s−1. The highest specific capacitance (single electrode) of 169 F g−1 was achieved at a current density of 0.1 mA cm−2; this value is comparable to those previously reported results for solid-state supercapacitors [45, 50, 51]. Further, the power and energy densities are two significant parameters to evaluate the performance of the supercapacitor device. The maximum energy and power densities of the solid-state device reached 5.8 W h kg−1 at a power density of 27.7 kW kg−1 and a power density of 277.6 kW kg−1 at an energy density of 1.5 W h kg−1. The obtained values are higher and comparable to the previously reported values [44, 50, 52–60]. The excellent electrochemical performance of the solid-state SSC device mainly attributed to the following factors: (1) binder-free deposition of graphene on fabric current collector reduces the conduct resistance, which facilitates faster electrical conduction during electrochemical reaction; (2) the

**Figure 4.** (a) CV curves and (b) specific capacitance of fabric SC at different scan rates. (c) GCD profiles and (d) specific

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capacitance of fabric SC at current densities. Reproduced from Ref. [19] with permission from the Elsevier.

appeared due to the faradic reaction. The existence of redox peak in GCD curve well agrees with CV curve. The calculated specific capacitances (**Figure 3d**) were 413, 333, 218, 150, and 106 F g−1 at various current densities of 0.5, 0.75, 1, 2.5 and 5 mA cm−2, respectively. These values are comparable as well as higher than previously reported values [26, 36, 44–47]. The shorter diffusion path, higher conductivity and higher active surface area of the fabricated TRGO electrode enhances the specific capacitance.

#### *2.1.3. Fabrication and electrochemical performance of the flexible solid-state symmetric supercapacitor (SSC) device*

A solid-state SSC was fabricated by sandwiching a H3 PO<sup>4</sup> /PVA gel electrolyte and filter paper between two pieces of the TRGO-coated fabric electrodes. The electrochemical performance of the solid-state supercapacitor such as CV and GCD was measured at different scan rates and current densities and results shown in **Figure 4**. The CV curve (**Figure 4a**.) of the fabricated supercapacitor device showed rectangular-like shapes even at high scan rates, which is ideal capacitive and fast charge/discharge behavior of the supercapacitor device. The

Fabrication and Characterization of Supercapacitors toward Self-Powered System http://dx.doi.org/10.5772/intechopen.73647 173

**Figure 4.** (a) CV curves and (b) specific capacitance of fabric SC at different scan rates. (c) GCD profiles and (d) specific capacitance of fabric SC at current densities. Reproduced from Ref. [19] with permission from the Elsevier.

appeared due to the faradic reaction. The existence of redox peak in GCD curve well agrees with CV curve. The calculated specific capacitances (**Figure 3d**) were 413, 333, 218, 150, and 106 F g−1 at various current densities of 0.5, 0.75, 1, 2.5 and 5 mA cm−2, respectively. These values are comparable as well as higher than previously reported values [26, 36, 44–47]. The shorter diffusion path, higher conductivity and higher active surface area of the fabricated

**Figure 3.** (a) CV curves of TRGO-coated fabric electrodes (inset at 5 mV s−1). (b) Specific capacitance of TRGO-coated fabrics at different scan rates. (c) GCD curves of TRGO-coated fabric electrodes. (d) Specific capacitance of TRGO-coated fabrics at different current densities. Figures are reproduced with permission from Ref. [19]. Copyright of Elsevier.

between two pieces of the TRGO-coated fabric electrodes. The electrochemical performance of the solid-state supercapacitor such as CV and GCD was measured at different scan rates and current densities and results shown in **Figure 4**. The CV curve (**Figure 4a**.) of the fabricated supercapacitor device showed rectangular-like shapes even at high scan rates, which is ideal capacitive and fast charge/discharge behavior of the supercapacitor device. The

PO<sup>4</sup>

/PVA gel electrolyte and filter paper

*2.1.3. Fabrication and electrochemical performance of the flexible solid-state symmetric* 

TRGO electrode enhances the specific capacitance.

A solid-state SSC was fabricated by sandwiching a H3

*supercapacitor (SSC) device*

172 Advancements in Energy Storage Technologies

specific capacitance as a function of scan rate is shown in **Figure 4b**. The calculated single electrode specific capacitance decreased from 281 to 54 F g−1, when scan rate increased from 5 to 125 mV s−1 and it is due to inefficient diffusion of ions at higher scan rates [48, 49]. Further, GCD of solid-state device was measured at various current density, which is shown in **Figure 4c**. The resultant GCD curve shows symmetric nature, which confirms the good capacitive nature of the device. The calculated specific capacitance was shown in **Figure 4d**. The estimated specific capacitance of the cell (flexible supercapacitor) was 70.4 F g−1 at 5 mV s−1. The highest specific capacitance (single electrode) of 169 F g−1 was achieved at a current density of 0.1 mA cm−2; this value is comparable to those previously reported results for solid-state supercapacitors [45, 50, 51]. Further, the power and energy densities are two significant parameters to evaluate the performance of the supercapacitor device. The maximum energy and power densities of the solid-state device reached 5.8 W h kg−1 at a power density of 27.7 kW kg−1 and a power density of 277.6 kW kg−1 at an energy density of 1.5 W h kg−1. The obtained values are higher and comparable to the previously reported values [44, 50, 52–60]. The excellent electrochemical performance of the solid-state SSC device mainly attributed to the following factors: (1) binder-free deposition of graphene on fabric current collector reduces the conduct resistance, which facilitates faster electrical conduction during electrochemical reaction; (2) the deposition of thin TRGO nanosheets provides a large accessible surface area which allowed abundant ions access (adsorption/desorption) for electrochemical reaction; (3) direct deposition of electroactive material allows strong adhesion with fabric current collector provide higher mechanical flexibility to device.

#### *2.1.4. Integration and functional characterization of self-powered UV sensor*

To demonstrate the self-powered application, fabricated SSC device was integrated with piezoelectric nanogenerator and photosensor. Here, piezoelectric nanogenerator used as an energy harvester, which converts the mechanical vibration, environmental noises into electrical energy. The harvested alternate current (AC) electrical signal was stored in the fabricated SSC device with the help of rectifier. The stored energy was used to monitor the ultraviolet (UV) light by integrating the photosensor with this system. The detailed circuit configuration was presented in **Figure 5a**. Here, a commercial piezoelectric nanogenerator was used as the energy source; it generated an average open-circuit voltage and short-circuit current of 8 V and 20 μA, respectively, under continuous finger pressure. The serially connected supercapacitors were charged (0.3 V over 280 s) by piezoelectric nanogenerator under constant finger pressing (**Figure 5b**). To demonstrate a self-powered application, the photodetector was connected to the supercapacitor to monitor UV light (**Figure 5a**) by closing switch S2 and opening switch S1. Here, photodetector was powered by serially connected supercapacitor and photodetector act as a variable load resistance for supercapacitor. The resistance of the photodetector varied linearly with the incident light intensity. The change of load resistance considerably changes the discharge current. The stability of the self-powered device is measured by multiple ON/OFF cycles under a constant illumination intensity of 8 mW cm−2 at a wavelength of 365 nm and results showed a stable response during measurement (**Figure 5c**). Additionally, the photoresponse was measured at 0.8 mW cm−2 steps for incident light intensity ranging from 0.8 to 8 mW cm−2 (**Figure 5d**). The photoresponse current was calculated using the following relation [61]:

$$\left| I\_{\rm PR} \right| = \left( I\_{\rm OFF} - I\_{\rm ON} \right) \tag{1}$$

image is clearly indicating that the deposition of Ag NWs on the flexible PDMS substrate and Ag NWs are randomly oriented on the substrate. Further, spin-coating PEDOT:PSS/PU

**Figure 5.** (a) Electric circuit diagram of the self-powered photosensor. (b) Charging of F-SCs by piezoelectric nanogenerator; inset is the structure of F-SC. (c) Time-dependent response of multiple ON/OFF cycles at a constant illumination intensity of 8 mW cm−2 at λ = 365 nm. (d) Time-dependent photoresponse with different illumination intensity. (e) Photoresponse

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current versus light intensity. Figure is adapted with permission from the Ref. [19]. Copyright of Elsevier.

where, IPR is the photoresponse current, IOFF is the discharge current at UV light "off" condition, and ION is the discharge current at UV light "on" condition. The photoresponse current increased linearly with increasing incident intensity (**Figure 5e**). This study suggested that the self-powered device has a massive potential in wearable and portable device applications.

#### **2.2. Fabrication and electrochemical characterization of flexible transparent supercapacitor device**

First, silver nanowire (AgNW) was spin-coated on polydimethylsiloxane (PDMS) substrate and subsequently dried. Then, PEDOT: PSS/PU nanocomposite was spin-coated over the AgNW/PDMS substrate and film was dried at 150°C for 1 hr. The PEDOT:PSS/PU nanocomposite was prepared by mixing PEDOT:PSS (5–8 wt% dimethyl sulfoxide (DMSO) & 1 wt% zonyl) with 4 wt% polyurethane dispersion (PU). The morphology of the fabricated film was measured through FE-SEM image and result was shown in **Figure 6a**. The resultant FE-SEM Fabrication and Characterization of Supercapacitors toward Self-Powered System http://dx.doi.org/10.5772/intechopen.73647 175

deposition of thin TRGO nanosheets provides a large accessible surface area which allowed abundant ions access (adsorption/desorption) for electrochemical reaction; (3) direct deposition of electroactive material allows strong adhesion with fabric current collector provide

To demonstrate the self-powered application, fabricated SSC device was integrated with piezoelectric nanogenerator and photosensor. Here, piezoelectric nanogenerator used as an energy harvester, which converts the mechanical vibration, environmental noises into electrical energy. The harvested alternate current (AC) electrical signal was stored in the fabricated SSC device with the help of rectifier. The stored energy was used to monitor the ultraviolet (UV) light by integrating the photosensor with this system. The detailed circuit configuration was presented in **Figure 5a**. Here, a commercial piezoelectric nanogenerator was used as the energy source; it generated an average open-circuit voltage and short-circuit current of 8 V and 20 μA, respectively, under continuous finger pressure. The serially connected supercapacitors were charged (0.3 V over 280 s) by piezoelectric nanogenerator under constant finger pressing (**Figure 5b**). To demonstrate a self-powered application, the photodetector was connected to the supercapacitor to monitor UV light (**Figure 5a**) by closing switch S2 and opening switch S1. Here, photodetector was powered by serially connected supercapacitor and photodetector act as a variable load resistance for supercapacitor. The resistance of the photodetector varied linearly with the incident light intensity. The change of load resistance considerably changes the discharge current. The stability of the self-powered device is measured by multiple ON/OFF cycles under a constant illumination intensity of 8 mW cm−2 at a wavelength of 365 nm and results showed a stable response during measurement (**Figure 5c**). Additionally, the photoresponse was measured at 0.8 mW cm−2 steps for incident light intensity ranging from 0.8 to 8 mW cm−2 (**Figure 5d**). The photoresponse current was calculated

*PR*| = (*I*

**2.2. Fabrication and electrochemical characterization of flexible transparent** 

*OFF* − *I*

where, IPR is the photoresponse current, IOFF is the discharge current at UV light "off" condition, and ION is the discharge current at UV light "on" condition. The photoresponse current increased linearly with increasing incident intensity (**Figure 5e**). This study suggested that the self-powered device has a massive potential in wearable and portable device applications.

First, silver nanowire (AgNW) was spin-coated on polydimethylsiloxane (PDMS) substrate and subsequently dried. Then, PEDOT: PSS/PU nanocomposite was spin-coated over the AgNW/PDMS substrate and film was dried at 150°C for 1 hr. The PEDOT:PSS/PU nanocomposite was prepared by mixing PEDOT:PSS (5–8 wt% dimethyl sulfoxide (DMSO) & 1 wt% zonyl) with 4 wt% polyurethane dispersion (PU). The morphology of the fabricated film was measured through FE-SEM image and result was shown in **Figure 6a**. The resultant FE-SEM

*ON*) (1)

*2.1.4. Integration and functional characterization of self-powered UV sensor*

higher mechanical flexibility to device.

174 Advancements in Energy Storage Technologies

using the following relation [61]:

**supercapacitor device**


**Figure 5.** (a) Electric circuit diagram of the self-powered photosensor. (b) Charging of F-SCs by piezoelectric nanogenerator; inset is the structure of F-SC. (c) Time-dependent response of multiple ON/OFF cycles at a constant illumination intensity of 8 mW cm−2 at λ = 365 nm. (d) Time-dependent photoresponse with different illumination intensity. (e) Photoresponse current versus light intensity. Figure is adapted with permission from the Ref. [19]. Copyright of Elsevier.

image is clearly indicating that the deposition of Ag NWs on the flexible PDMS substrate and Ag NWs are randomly oriented on the substrate. Further, spin-coating PEDOT:PSS/PU

supercapacitor electrode and additionally, PDMS layer was covered over the electrode to avoid the delamination and increase the stretchable nature. Here, AgNW/PEDOT:PSS/PU coated PDMS substrate was used as an active strain sensor and schematic representation of the fabricated electrode was given in **Figure 6f**. Further, the schematic representation of strain sensor placed on different parts of the human body is shown in **Figure 6g**. To demonstrate the capability of the fabricated strain sensor, it has integrated with supercapacitor device. The original

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177

**Figure 7.** Monitoring of strain caused by muscle movement for functions of the trachea and esophagus. (a) Stretchable

by source measurement unit, (b) breathing, (c) coughing, (d) drinking, (e) saliva swallowing, and (f) eating. (g) Circuit

versus time measured by SC charged by TENG, through (h) breathing, (i) coughing, (j) drinking, (k) saliva swallowing, and (l) eating, respectively. Figures are reproduced with permission from Ref. [23]. Copyright of American Chemical

diagram of strain according to the sensor with SC charged by TENG. Resistance change (ΔR/R<sup>0</sup>

) of the strain sensor versus time measured

) of the strain sensor

and transparent strain sensor attached to the neck. Resistance change (ΔR/R<sup>0</sup>

Society.

**Figure 6.** (a) FE-SEM image of the AgNW/PEDOT:PSS/PU film on PDMS substrate. (b) Schematic representation, (c) CV and (d) galvanostatic charge-discharge curves of the fabricated transparent flexible supercapacitor device. Schematic illustration of fabricated (e) TENG, (f) transparent strain sensor, and (g) strain sensor on different parts of human body. (h) Digital photograph of the self-powered strain sensor. Figure adapted with permission from the Ref. [23]. Copyright of American Chemical Society.

deposition eliminates the Ag NWs adhesive problems and connect silver nanowires. Moreover, deposition of PEDOT:PSS/PU nanocomposite provides higher flexibility and stretchability to the device by embedding the Ag NW inside polymer matrices. Finally, the flexible supercapacitor device was fabricated by sandwiching poly(vinyl alcohol)/phosphoric acid (PVA/ H3 PO<sup>4</sup> ) film between two PDMS/AgNW/PEDOT: PSS/PU substrate [23]. The transparent, stretchable supercapacitor device was schematically illustrated in **Figure 6b**. The performance of the fabricated flexible supercapacitor device was evaluated based on CV and GCD curves at a different scan rate and current densities and results displayed in **Figure 6**c and d. The fabricated PDMS/AgNW/PEDOT:PSS/PU symmetric supercapacitor delivered a maximum areal capacitance of 190 μF cm−2 and 396 μF cm−2 at a scan rate of 50 mV s−1 and a current density of 4 μA cm−2, respectively.

#### *2.2.1. Fabrication and characterization of transparent, stretchable self-powered patchable sensor*

The arch type triboelectric nanogenerator (TENG) device was fabricated by placing an archshaped PES/AgNW/PEDOT:PSS/PU on PDMS/AgNW/PEDOT:PSS/PU and schematic was shown in **Figure 6e**. The fabricated device designed to contact the arch-shaped PES surface with PDMS surface. The transparent, stretchable strain sensor was fabricated same as supercapacitor electrode and additionally, PDMS layer was covered over the electrode to avoid the delamination and increase the stretchable nature. Here, AgNW/PEDOT:PSS/PU coated PDMS substrate was used as an active strain sensor and schematic representation of the fabricated electrode was given in **Figure 6f**. Further, the schematic representation of strain sensor placed on different parts of the human body is shown in **Figure 6g**. To demonstrate the capability of the fabricated strain sensor, it has integrated with supercapacitor device. The original

deposition eliminates the Ag NWs adhesive problems and connect silver nanowires. Moreover, deposition of PEDOT:PSS/PU nanocomposite provides higher flexibility and stretchability to the device by embedding the Ag NW inside polymer matrices. Finally, the flexible supercapacitor device was fabricated by sandwiching poly(vinyl alcohol)/phosphoric acid (PVA/

**Figure 6.** (a) FE-SEM image of the AgNW/PEDOT:PSS/PU film on PDMS substrate. (b) Schematic representation, (c) CV and (d) galvanostatic charge-discharge curves of the fabricated transparent flexible supercapacitor device. Schematic illustration of fabricated (e) TENG, (f) transparent strain sensor, and (g) strain sensor on different parts of human body. (h) Digital photograph of the self-powered strain sensor. Figure adapted with permission from the Ref. [23]. Copyright

*2.2.1. Fabrication and characterization of transparent, stretchable self-powered patchable* 

The arch type triboelectric nanogenerator (TENG) device was fabricated by placing an archshaped PES/AgNW/PEDOT:PSS/PU on PDMS/AgNW/PEDOT:PSS/PU and schematic was shown in **Figure 6e**. The fabricated device designed to contact the arch-shaped PES surface with PDMS surface. The transparent, stretchable strain sensor was fabricated same as

) film between two PDMS/AgNW/PEDOT: PSS/PU substrate [23]. The transparent, stretchable supercapacitor device was schematically illustrated in **Figure 6b**. The performance of the fabricated flexible supercapacitor device was evaluated based on CV and GCD curves at a different scan rate and current densities and results displayed in **Figure 6**c and d. The fabricated PDMS/AgNW/PEDOT:PSS/PU symmetric supercapacitor delivered a maximum areal capacitance of 190 μF cm−2 and 396 μF cm−2 at a scan rate of 50 mV s−1 and a current density of

H3 PO<sup>4</sup>

*sensor*

4 μA cm−2, respectively.

of American Chemical Society.

176 Advancements in Energy Storage Technologies

**Figure 7.** Monitoring of strain caused by muscle movement for functions of the trachea and esophagus. (a) Stretchable and transparent strain sensor attached to the neck. Resistance change (ΔR/R<sup>0</sup> ) of the strain sensor versus time measured by source measurement unit, (b) breathing, (c) coughing, (d) drinking, (e) saliva swallowing, and (f) eating. (g) Circuit diagram of strain according to the sensor with SC charged by TENG. Resistance change (ΔR/R<sup>0</sup> ) of the strain sensor versus time measured by SC charged by TENG, through (h) breathing, (i) coughing, (j) drinking, (k) saliva swallowing, and (l) eating, respectively. Figures are reproduced with permission from Ref. [23]. Copyright of American Chemical Society.

photocopy of the integrated device was shown in **Figure 6h**. The ability of the fabricated device was calibrated by attaching the device at neck to monitor the muscle movements of the trachea during breathing and coughing and esophagus during drinking, swallowing, and eating. At first, the integrated device was charged by the external power source and used to run the strain sensor. The measured output signal from the embedded device was shown in **Figure 7**, and it is clear that the fabricated device highly sensitive to the muscle movement, well distinguishable between the nature of applied strain. To demonstrate the self-powered operation of the integrated device, the supercapacitor was charge through the triboelectric nanogenerator by mechanical vibration (pushing). The charging performance of the supercapacitor device with integrated TENG was shown in **Figure 8**. The results indicating that the integrated supercapacitor device charge up to 0.9 V at the short period (1500 s) of mechanical vibration.

bimetallic (Ni, Co) hydroxide in a standard three-electrode system in an aqueous solution

would be predictable to exhibit better electrochemical performance in terms of high specific capacitance, high-rate capability, and high energy density. The expectation of enriched performance is mainly due to the enlarged active surface area and open pores which facilitates the diffusion of electrolyte, highly porous interconnected network of nickel metal improves the fast electron transport, the large surface area of electrode contact with the electrolyte as well as lower resistance, better adhesion between the substrate and electroactive material.

by FE-SEM. **Figure 9a**–**b**. depicts the 3D porous interconnected Ni dendritic walls. The dendritic walls were composed of numerous interlinked nanoparticles and display continuous

O4

/Ni nanostructure film was evaluated by energy-dispersive X-ray spectroscopy

crystalline structure (JCPDS file no: 20-0781). The electrochemical character-

flower-like nanostructures over the Ni surface. The elemental composition of the as-prepared

and shown in **Figure 9e**–**f**. The Energy-dispersive X-ray spectroscopy (EDS) spectrum shows the distinctive peaks of Ni, Co and O elements present in the sample, which confirmed the

angles of 37.1°, 59.1° and 64.9° correspond to the (311), (511) and (440) plane reflections of

ization of as-prepared electrode was analyzed by CV and GCD curves in 2 M KOH electro-

chemical process, which attributed to the reversible faradaic redox processes of Ni2+/Ni3+ and Co2+/Co3+ transitions [64]. Further, the GCD curves also exhibited a non-linear behavior with voltage plateau indicated the faradaic behavior of the electrodes. The calculated volumet-

negative electrode, with polyvinyl alcohol- potassium hydroxide (PVA-KOH) gel electrolyte on a polyethylene terephthalate (PET) substrate was fabricated, for real-world applications. The two electrodes were assembled in parallel with separation of 1 mm on the PET substrate using PVA-KOH. The typical CV curves of F-SC at different scan rates as shown in **Figure 10a**, signifying the typical pseudocapacitive behavior. **Figure 10b**. shows the GCD curves of F-SC at different current densities. The GCD curves of F-SC also reveal symmetry and linear in nature, confirms that the device has excellent electrochemical reversibility and capacitive behavior. The calculated gravimetric and volumetric capacitance of the full cell is 18.8 F g−1 and 1.86 F cm−3, respectively. Further, the F-SC exhibited excellent cyclic stability (**Figure 10c**), even after 5000 cycles, with a capacitance retention of ~ 100%. The key parameter of the F-SC such as energy density and power density was calculated from the GCD curves (**Figure 10d**).

O4

) 2 .6H2

Fabrication and Characterization of Supercapacitors toward Self-Powered System

at 300°C for 2 h [63]. The construction of 3D architectures electrode

O4

/Ni. Further, the elemental mapping images clearly display the

/Ni exhibited the distinct diffraction peaks at the diffraction

O4

O4

/Ni electrodes show two pairs of redox peaks during the electro-

O) at a constant potential of -1 V for 5 min and followed by thermal transforma-

O) and 0.04 M cobalt nitrate

179

http://dx.doi.org/10.5772/intechopen.73647

/Ni nanostructures were examined

/Ni (**Figure 9c**–**d**) shows the highly porous

O4

/Ni fiber electrodes were 29.7 F cm−3 and

/Ni structure. Also,

/Ni fiber electrodes. The CV

/Ni used as a positive and

containing 1:2 molar ratio of 0.02 M nickel nitrate (Ni(NO<sup>3</sup>

The morphology of 3D porous Ni films and 3D-NiCo<sup>2</sup>

interspaces. The FE-SEM images of 3D-NiCo2

O4

O4

ric and gravimetric capacitance of the 3D-NiCo2

O4

lyte. **Figure 9g**–**h** shows the CV and GCD curves of 3D-NiCo2

Flexible solid-state fiber supercapacitor based on two 3D-NiCo<sup>2</sup>

uniform distribution of Ni, Co and O elements within the 3D-NiCo2

O4

(Co(NO3

3D-NiCo2

spinel NiCo2

O4

formation of 3D-NiCo2

XRD spectrum of 3D-NiCo<sup>2</sup>

O4

curves of the 3D-NiCo2

300 F g−1, respectively.

)2 .6H2

tion into spinel NiCo2

Further, the self-charged power was used to power the strain sensor to monitor the muscle movement. The output performance of the sensor powered by self-charged supercapacitor device is highly sensitive and almost same type of response observed as like externally charged device. The output performance of the strain sensor was given in **Figure 7h**–**i**. Similarly, the fabricated device showed higher sensitivity to the various human body activity like twisting, turning the wrist, clenching, etc. This result opens up to use self-powered systems for multiple application in wearable application as well internet of things (IoT).

#### **2.3. Fabrication and integration of supercapacitor device with solar energy harvesters**

#### *2.3.1. Synthesis and characterization of 3D-NiCo<sup>2</sup> O4 /Ni fiber electrodes*

The three dimensional (3D) porous nickel (Ni) films on metal fiber substrate were deposited using electrodeposition with a hydrogen bubble template method [62]. Briefly, the 3D porous Ni film was electrodeposited at a constant current of 2.5 A using DC power supply with the electrolyte containing a 0.1 M NiCl2 and 2 M NH4 Cl and then dried at 60°C for 12 h in hot air over. The 3D-NiCo2 O4 /Ni nanostructures were prepared by the electrodeposition of

**Figure 8.** Charging property of the transparent and stretchable SC charged by a TENG. (a) Schematic of the rectifying circuit and SC. (b) Charging curve of the SC charged by the power generated by the TENG, and charging steps of the SC (inset). Figure is adapted with permission from the Ref. [23]. Copyright of American Chemical Society.

bimetallic (Ni, Co) hydroxide in a standard three-electrode system in an aqueous solution containing 1:2 molar ratio of 0.02 M nickel nitrate (Ni(NO<sup>3</sup> ) 2 .6H2 O) and 0.04 M cobalt nitrate (Co(NO3 )2 .6H2 O) at a constant potential of -1 V for 5 min and followed by thermal transformation into spinel NiCo2 O4 at 300°C for 2 h [63]. The construction of 3D architectures electrode would be predictable to exhibit better electrochemical performance in terms of high specific capacitance, high-rate capability, and high energy density. The expectation of enriched performance is mainly due to the enlarged active surface area and open pores which facilitates the diffusion of electrolyte, highly porous interconnected network of nickel metal improves the fast electron transport, the large surface area of electrode contact with the electrolyte as well as lower resistance, better adhesion between the substrate and electroactive material.

The morphology of 3D porous Ni films and 3D-NiCo<sup>2</sup> O4 /Ni nanostructures were examined by FE-SEM. **Figure 9a**–**b**. depicts the 3D porous interconnected Ni dendritic walls. The dendritic walls were composed of numerous interlinked nanoparticles and display continuous interspaces. The FE-SEM images of 3D-NiCo2 O4 /Ni (**Figure 9c**–**d**) shows the highly porous flower-like nanostructures over the Ni surface. The elemental composition of the as-prepared 3D-NiCo2 O4 /Ni nanostructure film was evaluated by energy-dispersive X-ray spectroscopy and shown in **Figure 9e**–**f**. The Energy-dispersive X-ray spectroscopy (EDS) spectrum shows the distinctive peaks of Ni, Co and O elements present in the sample, which confirmed the formation of 3D-NiCo2 O4 /Ni. Further, the elemental mapping images clearly display the uniform distribution of Ni, Co and O elements within the 3D-NiCo2 O4 /Ni structure. Also, XRD spectrum of 3D-NiCo<sup>2</sup> O4 /Ni exhibited the distinct diffraction peaks at the diffraction angles of 37.1°, 59.1° and 64.9° correspond to the (311), (511) and (440) plane reflections of spinel NiCo2 O4 crystalline structure (JCPDS file no: 20-0781). The electrochemical characterization of as-prepared electrode was analyzed by CV and GCD curves in 2 M KOH electrolyte. **Figure 9g**–**h** shows the CV and GCD curves of 3D-NiCo2 O4 /Ni fiber electrodes. The CV curves of the 3D-NiCo2 O4 /Ni electrodes show two pairs of redox peaks during the electrochemical process, which attributed to the reversible faradaic redox processes of Ni2+/Ni3+ and Co2+/Co3+ transitions [64]. Further, the GCD curves also exhibited a non-linear behavior with voltage plateau indicated the faradaic behavior of the electrodes. The calculated volumetric and gravimetric capacitance of the 3D-NiCo2 O4 /Ni fiber electrodes were 29.7 F cm−3 and 300 F g−1, respectively.

Flexible solid-state fiber supercapacitor based on two 3D-NiCo<sup>2</sup> O4 /Ni used as a positive and negative electrode, with polyvinyl alcohol- potassium hydroxide (PVA-KOH) gel electrolyte on a polyethylene terephthalate (PET) substrate was fabricated, for real-world applications. The two electrodes were assembled in parallel with separation of 1 mm on the PET substrate using PVA-KOH. The typical CV curves of F-SC at different scan rates as shown in **Figure 10a**, signifying the typical pseudocapacitive behavior. **Figure 10b**. shows the GCD curves of F-SC at different current densities. The GCD curves of F-SC also reveal symmetry and linear in nature, confirms that the device has excellent electrochemical reversibility and capacitive behavior. The calculated gravimetric and volumetric capacitance of the full cell is 18.8 F g−1 and 1.86 F cm−3, respectively. Further, the F-SC exhibited excellent cyclic stability (**Figure 10c**), even after 5000 cycles, with a capacitance retention of ~ 100%. The key parameter of the F-SC such as energy density and power density was calculated from the GCD curves (**Figure 10d**).

**Figure 8.** Charging property of the transparent and stretchable SC charged by a TENG. (a) Schematic of the rectifying circuit and SC. (b) Charging curve of the SC charged by the power generated by the TENG, and charging steps of the SC

photocopy of the integrated device was shown in **Figure 6h**. The ability of the fabricated device was calibrated by attaching the device at neck to monitor the muscle movements of the trachea during breathing and coughing and esophagus during drinking, swallowing, and eating. At first, the integrated device was charged by the external power source and used to run the strain sensor. The measured output signal from the embedded device was shown in **Figure 7**, and it is clear that the fabricated device highly sensitive to the muscle movement, well distinguishable between the nature of applied strain. To demonstrate the self-powered operation of the integrated device, the supercapacitor was charge through the triboelectric nanogenerator by mechanical vibration (pushing). The charging performance of the supercapacitor device with integrated TENG was shown in **Figure 8**. The results indicating that the integrated superca-

pacitor device charge up to 0.9 V at the short period (1500 s) of mechanical vibration.

tiple application in wearable application as well internet of things (IoT).

*2.3.1. Synthesis and characterization of 3D-NiCo<sup>2</sup>*

the electrolyte containing a 0.1 M NiCl2

O4

hot air over. The 3D-NiCo2

178 Advancements in Energy Storage Technologies

**harvesters**

**2.3. Fabrication and integration of supercapacitor device with solar energy** 

Further, the self-charged power was used to power the strain sensor to monitor the muscle movement. The output performance of the sensor powered by self-charged supercapacitor device is highly sensitive and almost same type of response observed as like externally charged device. The output performance of the strain sensor was given in **Figure 7h**–**i**. Similarly, the fabricated device showed higher sensitivity to the various human body activity like twisting, turning the wrist, clenching, etc. This result opens up to use self-powered systems for mul-

*O4*

The three dimensional (3D) porous nickel (Ni) films on metal fiber substrate were deposited using electrodeposition with a hydrogen bubble template method [62]. Briefly, the 3D porous Ni film was electrodeposited at a constant current of 2.5 A using DC power supply with

and 2 M NH4

*/Ni fiber electrodes*

/Ni nanostructures were prepared by the electrodeposition of

Cl and then dried at 60°C for 12 h in

(inset). Figure is adapted with permission from the Ref. [23]. Copyright of American Chemical Society.

The calculated energy density and power density of the F-SC is 2.18 W h kg−1 (0.21 mWh cm−3) and 21.6 W kg−1 (2.1 mW cm−3). The obtained values are higher than or comparable to previ-

**Figure 10.** (a) Cyclic voltammograms of F-SC at different scan rates. (b) Galvanostatic charge/discharge profiles of F-SC at various currents. (c) the charge/discharge stability of F-SC at 0.8 mA. (d) Ragone plot of the F-SC. Reproduced from

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181

The fabrication and photovoltaic characterization of the DSSC as follows. First, TiCl4

ment was conducted by immersing the cleaned FTO glass in the 40 mM titanium tetrachloride

tert-butanol/acetonitrile solution (1:1 vol.) for 12 h. The Pt counter electrode (CE) was deposited on the drilled FTO glass by spin-coating (2000 rpm, 2 times) using 30 mM of the H2

treated FTO glass by doctor blade process followed by calcination at 550°C for 1 h. After

treat-

PtCl<sup>6</sup>

paste was deposited on the

in 0.5 mM of N-719 in

*2.3.2. Fabrication and characterization of dye-sensitized solar cells (DSSCs)*

) solution for 30 min at 70°C. Then, the photoanode TiO<sup>2</sup>

that, the dye coating was performed by dipping the as-prepared TiO2

ously reported F-SCs [20, 65–67].

Ref. [7] with permission from the Royal Society of Chemistry.

(TiCl4

TiCl4

**Figure 9.** FE-SEM images of 3D-Ni (a-b) and 3D-NiCo2 O4 /Ni (c-d) nanostructures. (e) EDS and (f) X-ray diffraction (XRD) spectra of 3D-NiCo2 O4 /Ni. (g) CV and (h) GCD curves of 3D-NiCo2 O4 /Ni. Figure is adapted with permission from the Ref. [7]. Copyright of Royal Society of Chemistry.

Fabrication and Characterization of Supercapacitors toward Self-Powered System http://dx.doi.org/10.5772/intechopen.73647 181

**Figure 10.** (a) Cyclic voltammograms of F-SC at different scan rates. (b) Galvanostatic charge/discharge profiles of F-SC at various currents. (c) the charge/discharge stability of F-SC at 0.8 mA. (d) Ragone plot of the F-SC. Reproduced from Ref. [7] with permission from the Royal Society of Chemistry.

The calculated energy density and power density of the F-SC is 2.18 W h kg−1 (0.21 mWh cm−3) and 21.6 W kg−1 (2.1 mW cm−3). The obtained values are higher than or comparable to previously reported F-SCs [20, 65–67].

#### *2.3.2. Fabrication and characterization of dye-sensitized solar cells (DSSCs)*

**Figure 9.** FE-SEM images of 3D-Ni (a-b) and 3D-NiCo2

Ref. [7]. Copyright of Royal Society of Chemistry.

O4

180 Advancements in Energy Storage Technologies

spectra of 3D-NiCo2

O4

O4

/Ni. (g) CV and (h) GCD curves of 3D-NiCo2

/Ni (c-d) nanostructures. (e) EDS and (f) X-ray diffraction (XRD)

/Ni. Figure is adapted with permission from the

The fabrication and photovoltaic characterization of the DSSC as follows. First, TiCl4 treatment was conducted by immersing the cleaned FTO glass in the 40 mM titanium tetrachloride (TiCl4 ) solution for 30 min at 70°C. Then, the photoanode TiO<sup>2</sup> paste was deposited on the TiCl4 treated FTO glass by doctor blade process followed by calcination at 550°C for 1 h. After that, the dye coating was performed by dipping the as-prepared TiO2 in 0.5 mM of N-719 in tert-butanol/acetonitrile solution (1:1 vol.) for 12 h. The Pt counter electrode (CE) was deposited on the drilled FTO glass by spin-coating (2000 rpm, 2 times) using 30 mM of the H2 PtCl<sup>6</sup>

solution in isopropyl alcohol (IPA) and annealed at 450 ֯C for 30 min. The working electrode and CE were assembled using 60 μm of Surlyn, and an electrolyte (0.5 M 1-hexyl-2,3-dimethylimidazolium iodide (C-tri), 0.02 M iodine, 0.5 M 4-tert-butylpyridine, and 0.05 M lithium iodide in acetonitrile) was added through a pre-drilled hole.

#### *2.3.3. Integration and functional characterization of self-powered device*

To demonstrate self-powered application, the fabricated fiber supercapacitor was integrated with DSSC and LED. The schematic illustration of the self-powered device is shown in **Figure 11a**. The self-powered system comprises of four series-connected DSSCs and three series F-SC and light emitting diode (LED). Here, DSSC served as an energy source to harvest the energy from sunlight and then to charge the supercapacitor. After that stored energy was utilized to drive LED without disruption. Initially, the F-SCs was charged with turning the switch S1 is on to connect DSSCs to the circuit. **Figure 11b** shows the current density-voltage curve of the serially-wound DSSCs. The open-circuit voltage, short-circuit current, and power conversion efficiency of the serially-wound DSSCs was 3.08 V, 3.94 mA cm−2, and 6.96%, respectively. The inset of **Figure 11b** shows the digital photograph of serially-wound DSSCs. The F-SC charged from 0 to 3.2 V about 60s signifying the stable output of DSSCs as shown in **Figure 11c**. Afterwards, to demonstrate the self-powered operation, by turning the switch S2 is on, while switch S1 is off to illuminate the commercial green LED (**Figure 11c**) using charged supercapacitors. This study validated that fiber supercapacitors could store solar energy harvested from DSSCs, which suggests their massive application potential in diverse electronic devices.

#### **2.4. Fabrication and integration of supercapacitor device with hybrid (solar and vibrational) energy harvesters**

In this work, hybridized self-charging power textile system was developed by Wen et al., [68] to simultaneously collect outdoor/indoor sunlight and casual body movement energies and stored in an energy storage device for sustainable operation of wearable electronics. **Figure 12** shows the schematic illustration of hybridized self-charging power textile. The self-charging power textile system consists of fiber-shaped dye-sensitized solar cells (F-DSSC, top layer), fiber-shaped triboelectric nanogenerator (F-TENG, middle layer) and fiber-shaped supercapacitors (F-SC, bottom layer). In this architecture, each solar cell and supercapacitor unit is coupled to one another, making a single triboelectric nanogenerator unit is assembled to scavenge body motion energy simultaneously. Both of the harvested energies could be effortlessly converted into electricity by using several solar cell units (for solar energy) and TENG (for random body motion energy) and then stored as chemical energy in supercapacitor modules

**Figure 12.** Schematic representation of the self-charging power textile. Reproduced from Ref. [68] with permission from

O on carbon fiber electrode was synthesized using a vapor-phase

Fabrication and Characterization of Supercapacitors toward Self-Powered System

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183

•xH2

·xH2

PO<sup>4</sup>

and 4 ml of EtOH was coated onto the carbon fibers and dried at 60°C. Then the

coated carbon fibers were placed in a 50-ml Teflon-lined stainless steel autoclave with

two fiber electrodes were closely assembled into the PDMS-covered Cu-coated tube and separated by a paper septum to form an all-solid-state flexible fiber-shaped supercapacitor. Before

phological properties of the as-prepared electrodes were confirmed through SEM and XRD analysis. The SEM image (**Figure 13b**–**d**) of as-prepared fiber electrode shows the cracked mud

carbon fiber electrodes revealed an amorphous with partly rutile crystalline structure [70]. The electrochemical performance of F-SC was evaluated by CV and GCD techniques. **Figure 13e**

morphology and the diameter of the fiber are ~10 mm. The XRD pattern of RuO<sup>2</sup>

) slurry prepared from

O-coated carbon fibers. Then,

gel electrolyte for 10 min.

•xH2

O coated

O F-SC. The structural and mor-

for the operation of wearable electronics.

the American Association for the Advancement of Science.

·xH2

The binder-free RuO2

0.1 g of RuCl3

RuCl3

*2.4.1. Fabrication and characterization of fiber-shaped supercapacitors*

0.1 M NaOH solution in an oven at 190°C for 5 h to get RuO<sup>2</sup>

assembling, the fiber electrodes were immersed in a PVA/H<sup>3</sup>

**Figure 13a** shows the schematic representation of RuO2

hydrothermal technique [69]. Briefly, a Ruthenium(III) chloride (RuCl<sup>3</sup>

**Figure 11.** (a) Schematic diagram of the integration of F-SCs with DSSCs and LED. (b) J-V curve of the DSSCs connected in series under 1 sun irradiation. Inset is the digital image of four DSSCs assembled in series. (c) Charging curve of F-SCs module by DSSCs module in series; the inset is the digital photograph of green LED driven by F-SCs charged using DSSCs. Figure is adapted with permission from the Ref. [7]. Copyright of Royal Society of Chemistry.

**Figure 12.** Schematic representation of the self-charging power textile. Reproduced from Ref. [68] with permission from the American Association for the Advancement of Science.

fiber-shaped triboelectric nanogenerator (F-TENG, middle layer) and fiber-shaped supercapacitors (F-SC, bottom layer). In this architecture, each solar cell and supercapacitor unit is coupled to one another, making a single triboelectric nanogenerator unit is assembled to scavenge body motion energy simultaneously. Both of the harvested energies could be effortlessly converted into electricity by using several solar cell units (for solar energy) and TENG (for random body motion energy) and then stored as chemical energy in supercapacitor modules for the operation of wearable electronics.

#### *2.4.1. Fabrication and characterization of fiber-shaped supercapacitors*

solution in isopropyl alcohol (IPA) and annealed at 450 ֯C for 30 min. The working electrode and CE were assembled using 60 μm of Surlyn, and an electrolyte (0.5 M 1-hexyl-2,3-dimethylimidazolium iodide (C-tri), 0.02 M iodine, 0.5 M 4-tert-butylpyridine, and 0.05 M lithium

To demonstrate self-powered application, the fabricated fiber supercapacitor was integrated with DSSC and LED. The schematic illustration of the self-powered device is shown in **Figure 11a**. The self-powered system comprises of four series-connected DSSCs and three series F-SC and light emitting diode (LED). Here, DSSC served as an energy source to harvest the energy from sunlight and then to charge the supercapacitor. After that stored energy was utilized to drive LED without disruption. Initially, the F-SCs was charged with turning the switch S1 is on to connect DSSCs to the circuit. **Figure 11b** shows the current density-voltage curve of the serially-wound DSSCs. The open-circuit voltage, short-circuit current, and power conversion efficiency of the serially-wound DSSCs was 3.08 V, 3.94 mA cm−2, and 6.96%, respectively. The inset of **Figure 11b** shows the digital photograph of serially-wound DSSCs. The F-SC charged from 0 to 3.2 V about 60s signifying the stable output of DSSCs as shown in **Figure 11c**. Afterwards, to demonstrate the self-powered operation, by turning the switch S2 is on, while switch S1 is off to illuminate the commercial green LED (**Figure 11c**) using charged supercapacitors. This study validated that fiber supercapacitors could store solar energy harvested from DSSCs, which suggests their mas-

**2.4. Fabrication and integration of supercapacitor device with hybrid (solar and** 

In this work, hybridized self-charging power textile system was developed by Wen et al., [68] to simultaneously collect outdoor/indoor sunlight and casual body movement energies and stored in an energy storage device for sustainable operation of wearable electronics. **Figure 12** shows the schematic illustration of hybridized self-charging power textile. The self-charging power textile system consists of fiber-shaped dye-sensitized solar cells (F-DSSC, top layer),

**Figure 11.** (a) Schematic diagram of the integration of F-SCs with DSSCs and LED. (b) J-V curve of the DSSCs connected in series under 1 sun irradiation. Inset is the digital image of four DSSCs assembled in series. (c) Charging curve of F-SCs module by DSSCs module in series; the inset is the digital photograph of green LED driven by F-SCs charged using

DSSCs. Figure is adapted with permission from the Ref. [7]. Copyright of Royal Society of Chemistry.

iodide in acetonitrile) was added through a pre-drilled hole.

sive application potential in diverse electronic devices.

**vibrational) energy harvesters**

182 Advancements in Energy Storage Technologies

*2.3.3. Integration and functional characterization of self-powered device*

The binder-free RuO2 ·xH2 O on carbon fiber electrode was synthesized using a vapor-phase hydrothermal technique [69]. Briefly, a Ruthenium(III) chloride (RuCl<sup>3</sup> ) slurry prepared from 0.1 g of RuCl3 and 4 ml of EtOH was coated onto the carbon fibers and dried at 60°C. Then the RuCl3 coated carbon fibers were placed in a 50-ml Teflon-lined stainless steel autoclave with 0.1 M NaOH solution in an oven at 190°C for 5 h to get RuO<sup>2</sup> •xH2 O-coated carbon fibers. Then, two fiber electrodes were closely assembled into the PDMS-covered Cu-coated tube and separated by a paper septum to form an all-solid-state flexible fiber-shaped supercapacitor. Before assembling, the fiber electrodes were immersed in a PVA/H<sup>3</sup> PO<sup>4</sup> gel electrolyte for 10 min. **Figure 13a** shows the schematic representation of RuO2 ·xH2 O F-SC. The structural and morphological properties of the as-prepared electrodes were confirmed through SEM and XRD analysis. The SEM image (**Figure 13b**–**d**) of as-prepared fiber electrode shows the cracked mud morphology and the diameter of the fiber are ~10 mm. The XRD pattern of RuO<sup>2</sup> •xH2 O coated carbon fiber electrodes revealed an amorphous with partly rutile crystalline structure [70]. The electrochemical performance of F-SC was evaluated by CV and GCD techniques. **Figure 13e**

shows the CV curves of F-SC at different scan rates. It can be observed that the CV curves are maintained their initial shape even at higher scan rates, revealed their good capacitive behavior and better rate capability. The GCD curves of F-SC at various current densities were shown in **Figure 13f**. The GCD curves displayed the symmetric and triangle in shape under various current densities, confirmed the good capacitive behavior. The calculated energy specific capacitance and energy density of F-SC is 1.9 mF cm−1 and 1.37 mJ cm−1, respectively. Further, the F-SC better cycling stability even after 5000 cycles with no obvious capacitance change as well as excellent mechanical stability under various bending conditions (from 0° to 180°).

prepared photoanode was immersed in a 3 × 10−4 M of N719 dye solution in ACN and tBA (v/v = 1/1) at room temperature for 24 h. Thirdly, the platinum counter electrode was fabricated

thermal treatment at 400°C for 30 min. Finally, F-DSSC was fabricated by inserting a Pt-coated carbon fiber and a dye-sensitized Ti photo anode into the Cu-coated ethylene vinyl acetate (EVA) tube in parallel and then injected an electrolyte into the tubing (0.1 M LiI, 0.05 M I2, 0.6 M DMPII, and 0.5 M tBP in MPN). Finally, the pipe was sealed with sealing glue to prevent the electrolyte leakage. The schematic illustration and digital photograph of F-DSSC are shown in

surface were confirmed through SEM images (**Figure 14b**–**d**). The current density-voltage (J-V) characteristic of F-DSSC was assessed under standard illumination (100 mW cm − 2; AM1.5). The short-circuit current density, open-circuit voltage, fill factor, and power conversion effi-

A fiber-shaped triboelectric nanogenerator (F-TENG) was fabricated by connecting a Cu-coated EVA tube as a triboelectric electrode and the PDMS-covered Cu-coated EVA tube as another electrode. The copper (Cu) electrode with 1 mm thickness was deposited onto the EVA tubing surface by physical vapor deposition at 100 W in Ar atmosphere for 40 min. Then, the PDMScovered Cu-coated EVA tubing was deposited by dip-coating process [72, 73] and dried at room temperature for 12 h. **Figure 14e**–**f** displays the schematic diagram and a digital photograph of F-TENG. The output performance of the fabricated F-TENG was studied through the periodic contact and separation under different frequencies. The open-circuit voltage (~12.6 V) and charge of the device are almost constant (~4.5 nC,) when the frequencies vary from 1 to 5 Hz, but the short-circuit current (ISC) increased from ~0.06 to ~0.15 μA. These result con-

The hybridized self-charging power textile was fabricated by intertwined several solar cells and supercapacitors into the fabric to form a textile structure with serial/parallel connection (tune the output voltage and capacitance of devices to drive real wearable electronics). The textile-based F-TENG system was constructed by assembling the intertwined F-DSSC textile

illustration and (f) digital photograph of F-TENG. Figure is adapted with permission from the Ref. [68]. Copyright of

O aqueous solution (5 mg/ml) for 5 min followed by

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185

Fabrication and Characterization of Supercapacitors toward Self-Powered System

nanotubes with a diameter of ~50 nm on Ti wires

nanotube arrays. (e) Schematic

PtCl<sup>6</sup>

•6H2

ciency of the F-DSSC is 11.92 mA cm−2, 0.74 V, 0.64, and 5.64%, respectively.

firmed that the fabricated device could harvest the renewable energy efficiently.

*2.4.3. Fabrication and testing of the hybridized self-charging power textile*

**Figure 14.** (a) Schematic diagram and (b) photograph of F-DSSC. (c-d) SEM images of TiO2

American Association for the Advancement of Science.

by soaking carbon fiber in an H<sup>2</sup>

**Figure 14a**. Vertically oriented arrays of TiO2

#### *2.4.2. Fabrication and functional characterization of fiber-shaped hybrid energy harvester using dye-sensitized solar cells and triboelectric nanogenerator*

Firstly, a photoanode (TiO2 nanotube) was prepared on Ti wire surface by anodization techniques in a solution containing 0.3 wt % NH<sup>4</sup> F/EG and 8 wt % H<sup>2</sup> O at 60 V for 6 h using a two-electrode cell with Pt wire as a counter electrode [71]. After that, the anodized Ti wire was annealed at 500°C for 1 h and then immersed in 40 mM TiCl<sup>4</sup> solution at 70°C for 30 min. Afterwards, the as-prepared samples were annealed again at 450°C for 30 min. Secondly, the

**Figure 13.** (a) Schematic diagram and (b) photograph of F-SC. (c-d) SEM images of RuO2 ·xH2 O-coated carbon fiber electrode. (e) CV and (f) GCD curves of F-SC at various scan rates and current densities. Figure is adapted with permission from the Ref. [68]. Copyright of American Association for the Advancement of Science.

prepared photoanode was immersed in a 3 × 10−4 M of N719 dye solution in ACN and tBA (v/v = 1/1) at room temperature for 24 h. Thirdly, the platinum counter electrode was fabricated by soaking carbon fiber in an H<sup>2</sup> PtCl<sup>6</sup> •6H2 O aqueous solution (5 mg/ml) for 5 min followed by thermal treatment at 400°C for 30 min. Finally, F-DSSC was fabricated by inserting a Pt-coated carbon fiber and a dye-sensitized Ti photo anode into the Cu-coated ethylene vinyl acetate (EVA) tube in parallel and then injected an electrolyte into the tubing (0.1 M LiI, 0.05 M I2, 0.6 M DMPII, and 0.5 M tBP in MPN). Finally, the pipe was sealed with sealing glue to prevent the electrolyte leakage. The schematic illustration and digital photograph of F-DSSC are shown in **Figure 14a**. Vertically oriented arrays of TiO2 nanotubes with a diameter of ~50 nm on Ti wires surface were confirmed through SEM images (**Figure 14b**–**d**). The current density-voltage (J-V) characteristic of F-DSSC was assessed under standard illumination (100 mW cm − 2; AM1.5). The short-circuit current density, open-circuit voltage, fill factor, and power conversion efficiency of the F-DSSC is 11.92 mA cm−2, 0.74 V, 0.64, and 5.64%, respectively.

A fiber-shaped triboelectric nanogenerator (F-TENG) was fabricated by connecting a Cu-coated EVA tube as a triboelectric electrode and the PDMS-covered Cu-coated EVA tube as another electrode. The copper (Cu) electrode with 1 mm thickness was deposited onto the EVA tubing surface by physical vapor deposition at 100 W in Ar atmosphere for 40 min. Then, the PDMScovered Cu-coated EVA tubing was deposited by dip-coating process [72, 73] and dried at room temperature for 12 h. **Figure 14e**–**f** displays the schematic diagram and a digital photograph of F-TENG. The output performance of the fabricated F-TENG was studied through the periodic contact and separation under different frequencies. The open-circuit voltage (~12.6 V) and charge of the device are almost constant (~4.5 nC,) when the frequencies vary from 1 to 5 Hz, but the short-circuit current (ISC) increased from ~0.06 to ~0.15 μA. These result confirmed that the fabricated device could harvest the renewable energy efficiently.

#### *2.4.3. Fabrication and testing of the hybridized self-charging power textile*

shows the CV curves of F-SC at different scan rates. It can be observed that the CV curves are maintained their initial shape even at higher scan rates, revealed their good capacitive behavior and better rate capability. The GCD curves of F-SC at various current densities were shown in **Figure 13f**. The GCD curves displayed the symmetric and triangle in shape under various current densities, confirmed the good capacitive behavior. The calculated energy specific capacitance and energy density of F-SC is 1.9 mF cm−1 and 1.37 mJ cm−1, respectively. Further, the F-SC better cycling stability even after 5000 cycles with no obvious capacitance change as well as excellent mechanical stability under various bending conditions (from 0° to 180°).

two-electrode cell with Pt wire as a counter electrode [71]. After that, the anodized Ti wire

Afterwards, the as-prepared samples were annealed again at 450°C for 30 min. Secondly, the

nanotube) was prepared on Ti wire surface by anodization tech-

O at 60 V for 6 h using a

solution at 70°C for 30 min.

·xH2

O-coated carbon fiber

F/EG and 8 wt % H<sup>2</sup>

*2.4.2. Fabrication and functional characterization of fiber-shaped hybrid energy harvester* 

*using dye-sensitized solar cells and triboelectric nanogenerator*

was annealed at 500°C for 1 h and then immersed in 40 mM TiCl<sup>4</sup>

**Figure 13.** (a) Schematic diagram and (b) photograph of F-SC. (c-d) SEM images of RuO2

permission from the Ref. [68]. Copyright of American Association for the Advancement of Science.

electrode. (e) CV and (f) GCD curves of F-SC at various scan rates and current densities. Figure is adapted with

niques in a solution containing 0.3 wt % NH<sup>4</sup>

Firstly, a photoanode (TiO2

184 Advancements in Energy Storage Technologies

The hybridized self-charging power textile was fabricated by intertwined several solar cells and supercapacitors into the fabric to form a textile structure with serial/parallel connection (tune the output voltage and capacitance of devices to drive real wearable electronics). The textile-based F-TENG system was constructed by assembling the intertwined F-DSSC textile

**Figure 14.** (a) Schematic diagram and (b) photograph of F-DSSC. (c-d) SEM images of TiO2 nanotube arrays. (e) Schematic illustration and (f) digital photograph of F-TENG. Figure is adapted with permission from the Ref. [68]. Copyright of American Association for the Advancement of Science.

fabric as the top layer to harvest solar energy, and the bottom layer of intertwined F-SC textile was used to store harvested energies. Meanwhile, both woven textiles instantaneously engage in recreation as triboelectric layers to collect mechanical energies from human body motion, which were also stored in F-SC after rectification.

in charging curves through F-TENGs by turn on the S2 switch. Then the charged F-SCs can power the portable electronic devices including LEDs, smart watches, sensors, etc. Moreover, the charging efficiency can be improved through impedance matching of DSSCs, TENGs, and SCs. Finally, the stability of the fabricated device was tested under continuous bending motion for 1000 cycles using the linear motor, as shown in **Figure 15f**. The advancement in the

Fabrication and Characterization of Supercapacitors toward Self-Powered System

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187

In summary, a self-powered system was successfully demonstrated by charging the supercapacitor using an energy harvester and powered a photosensor as well as portable devices. The various results showed the feasibility of using a supercapacitor as an efficient energy storage components and their application in self-powered devices due its high power density (uptake pulses) leads to the high energy conversion and storage efficiency. This work offers a welcome advancement in the supercapacitor toward the self-powered system application in flexible/ wearable technology, which will pledge promising developments in self-powered flexible displays, infrastructure, and environmental monitoring, internet of things, defense technologies and wearable electronics (artificial electronic skin, smart textiles/watch straps), among others.

This work was supported by 2018 Jeju Sea Grant College Program funded by the Ministry of Oceans and Fisheries (MOF) and by the National Research Foundation of Korea (NRF)

Ananthakumar Ramadoss1,2, Balasubramaniam Saravanakumar1,3,4 and Sang-Jae Kim1

1 Nanomaterials and System Lab, Department of Mechatronics Engineering, Jeju National

2 Laboratory for Advanced Research in Polymeric Materials, Central Institute of Plastic

3 School of Chemistry and Environment, South China Normal University, Guangzhou,

4 Engineering Research Center of MTEES (Ministry of Education), Research Center of BMET (Guangdong Province), Engineering Laboratory of OFMHEB (Guangdong Province), Key Laboratory of ETESPG (GHEI) and Innovative Platform for ITBMD (Guangzhou

Municipality), South China Normal University, Guangzhou, China

\*

funded by the Korea Government GRANT (2016R1A2B2013831).

\*Address all correspondence to: kimsangj@jejunu.ac.kr

Engineering and Technology, Bhubaneswar, India

University, Jeju, Republic of Korea

present efforts provides a new path for self-powered systems in wearable technology.

**3. Conclusion**

**Acknowledgements**

**Author details**

China

To check the real-time applications, the fabricated textile device was attached with a T-Shirt (**Figure 15**) and harvests both sunlight as well as body motion in outdoor and indoor activities, respectively. The whole device consists of three F-DSSC and 6 F-SC units in serial and then intertwined separately in a 3 × 3 network. The demonstration of self-charging power textile under outdoor and indoor activities is shown in **Figure 15a**–**c**. **Figure 15d** shows the equivalent circuit of hybridized self-charging power textile. Herein, an AC generated from F-TENG was converted into DC by a bridge rectifier, and it stored in F-SC. Moreover, a diode in the circuit blocks the inflow current through an F-DSSC. The self-charging characteristic was achieved by harvesting solar and mechanical energies from human motion through the as-fabricated hybrid device and is presented in **Figure 15e**. Initially, the F-SC was charged through a turn on the switch S0 and S1, which connect F-DSSC to F-SC, while the S2 switch is in off. The stable F-DSSCs output charge the F-SC to 1.8 V from 0 at 69 s. The F-SCs charging voltage persists at 1.8 V, due to the low output voltage of the F-DSSCs, which limits their reliability and practicability and it indicated by light blue-shaded area in **Figure 15e**. Further, the F-SCs can be charged continuously to a higher voltage as highlighted (light red-shaded area)

**Figure 15.** Demonstration of the self-charging powered textile. Digital photograph of the hybrid device under outdoor (a), indoor (b), and movement (c) environments. (d) the equivalent circuit of the self-charging powered textile for portable electronics. (e) the self-charging curve of the F-DSSC and F-TENG hybrid. (f) Durability studies of the fabricated devices for 1000 cycles. Insets show the photographs of bending status between 0° and 180°. Reproduced from Ref. [68] with permission from the American Association for the Advancement of Science.

in charging curves through F-TENGs by turn on the S2 switch. Then the charged F-SCs can power the portable electronic devices including LEDs, smart watches, sensors, etc. Moreover, the charging efficiency can be improved through impedance matching of DSSCs, TENGs, and SCs. Finally, the stability of the fabricated device was tested under continuous bending motion for 1000 cycles using the linear motor, as shown in **Figure 15f**. The advancement in the present efforts provides a new path for self-powered systems in wearable technology.

#### **3. Conclusion**

In summary, a self-powered system was successfully demonstrated by charging the supercapacitor using an energy harvester and powered a photosensor as well as portable devices. The various results showed the feasibility of using a supercapacitor as an efficient energy storage components and their application in self-powered devices due its high power density (uptake pulses) leads to the high energy conversion and storage efficiency. This work offers a welcome advancement in the supercapacitor toward the self-powered system application in flexible/ wearable technology, which will pledge promising developments in self-powered flexible displays, infrastructure, and environmental monitoring, internet of things, defense technologies and wearable electronics (artificial electronic skin, smart textiles/watch straps), among others.

### **Acknowledgements**

This work was supported by 2018 Jeju Sea Grant College Program funded by the Ministry of Oceans and Fisheries (MOF) and by the National Research Foundation of Korea (NRF) funded by the Korea Government GRANT (2016R1A2B2013831).

#### **Author details**

**Figure 15.** Demonstration of the self-charging powered textile. Digital photograph of the hybrid device under outdoor (a), indoor (b), and movement (c) environments. (d) the equivalent circuit of the self-charging powered textile for portable electronics. (e) the self-charging curve of the F-DSSC and F-TENG hybrid. (f) Durability studies of the fabricated devices for 1000 cycles. Insets show the photographs of bending status between 0° and 180°. Reproduced from Ref. [68]

fabric as the top layer to harvest solar energy, and the bottom layer of intertwined F-SC textile was used to store harvested energies. Meanwhile, both woven textiles instantaneously engage in recreation as triboelectric layers to collect mechanical energies from human body motion,

To check the real-time applications, the fabricated textile device was attached with a T-Shirt (**Figure 15**) and harvests both sunlight as well as body motion in outdoor and indoor activities, respectively. The whole device consists of three F-DSSC and 6 F-SC units in serial and then intertwined separately in a 3 × 3 network. The demonstration of self-charging power textile under outdoor and indoor activities is shown in **Figure 15a**–**c**. **Figure 15d** shows the equivalent circuit of hybridized self-charging power textile. Herein, an AC generated from F-TENG was converted into DC by a bridge rectifier, and it stored in F-SC. Moreover, a diode in the circuit blocks the inflow current through an F-DSSC. The self-charging characteristic was achieved by harvesting solar and mechanical energies from human motion through the as-fabricated hybrid device and is presented in **Figure 15e**. Initially, the F-SC was charged through a turn on the switch S0 and S1, which connect F-DSSC to F-SC, while the S2 switch is in off. The stable F-DSSCs output charge the F-SC to 1.8 V from 0 at 69 s. The F-SCs charging voltage persists at 1.8 V, due to the low output voltage of the F-DSSCs, which limits their reliability and practicability and it indicated by light blue-shaded area in **Figure 15e**. Further, the F-SCs can be charged continuously to a higher voltage as highlighted (light red-shaded area)

which were also stored in F-SC after rectification.

186 Advancements in Energy Storage Technologies

with permission from the American Association for the Advancement of Science.

Ananthakumar Ramadoss1,2, Balasubramaniam Saravanakumar1,3,4 and Sang-Jae Kim1 \*

\*Address all correspondence to: kimsangj@jejunu.ac.kr

1 Nanomaterials and System Lab, Department of Mechatronics Engineering, Jeju National University, Jeju, Republic of Korea

2 Laboratory for Advanced Research in Polymeric Materials, Central Institute of Plastic Engineering and Technology, Bhubaneswar, India

3 School of Chemistry and Environment, South China Normal University, Guangzhou, China

4 Engineering Research Center of MTEES (Ministry of Education), Research Center of BMET (Guangdong Province), Engineering Laboratory of OFMHEB (Guangdong Province), Key Laboratory of ETESPG (GHEI) and Innovative Platform for ITBMD (Guangzhou Municipality), South China Normal University, Guangzhou, China

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## *Edited by Xiangping Chen and Wenping Cao*

Energy storage technologies play an important role in terms of high-efficient energy utilisation and stable energy flow in the system. This book provides a glimpse of some latest advancements in energy storage technologies, management and control, innovative energy conversion, energy efficiency and system integration. It is aimed at providing a guideline for developing similar storage systems and for the readers who are interested in energy storage-related technologies, wind energy, solar energy, smart grid and smart buildings.

Published in London, UK © 2018 IntechOpen © jimmyan / iStock

Advancements in Energy Storage Technologies

Advancements in Energy

Storage Technologies

*Edited by Xiangping Chen and Wenping Cao*