Modelling of Hybrid Energy Systems

Chapter 1

Malek Belouda

Abstract

1. Introduction

3

New Design Methodologies for

Study: Wind Turbine System)

Sizing Electrochemical Storage in

Renewable Energy Systems (Case

This chapter presents four original methodologies for sizing electrochemical storage devices in renewable energy systems. The case study is taken to apply these methodologies on an electrochemical storage device (a battery bank) inside a wind turbine system. The storage device acts together with wind cycles and consumption profile, particularly for a remote application. In general, in a context of optimal design for such systems, the optimization process time (long processing time) is hampered by the wide number of system simulations caused by the long duration of the actual wind speed measurements used as input data for the problem. Two sizing methodologies are based on a statistical approach, and the two other methodologies are based on the synthesis of compact wind speed profiles by means of evolutionary algorithms. The results are discussed from the point of view of the relevance of the battery bank sizing and in terms of computation cost, this later issue being crucial in view of an integrated optimal design (IOD) process.

Keywords: renewable energy systems, electrochemical storage, sizing,

Renewable energy productions are characterized by the unpredictability and the intermittence of the environmental data such as solar irradiation and wind speed in photovoltaic and wind system productions. Therefore, the main criteria when supplying remote areas from renewable source (wind energy/solar irradiation) are the continuity and reliability of electricity supply. The satisfaction of these two criteria can be reached by inserting storage devices (electrochemical devices, hydraulic devices, etc.), but the high owning cost of such solution denotes a major inconvenient for this alternative [1–5]. Hence, an optimal sizing design of the renewable production system coupled with the storage device appears as a guarantee to assure reliability and cheap electricity to supply consumers in isolated sites. The optimal design is achieved by performing global optimization process using a several simulations [6–12]. Nevertheless, these simulations are performed in large time duration, since the environmental data (wind speed, solar irradiation) are characterized

wind profile synthesis, optimization, evolutionary algorithm

by unpredictability which needs great amounts of such data.

#### Chapter 1

## New Design Methodologies for Sizing Electrochemical Storage in Renewable Energy Systems (Case Study: Wind Turbine System)

Malek Belouda

### Abstract

This chapter presents four original methodologies for sizing electrochemical storage devices in renewable energy systems. The case study is taken to apply these methodologies on an electrochemical storage device (a battery bank) inside a wind turbine system. The storage device acts together with wind cycles and consumption profile, particularly for a remote application. In general, in a context of optimal design for such systems, the optimization process time (long processing time) is hampered by the wide number of system simulations caused by the long duration of the actual wind speed measurements used as input data for the problem. Two sizing methodologies are based on a statistical approach, and the two other methodologies are based on the synthesis of compact wind speed profiles by means of evolutionary algorithms. The results are discussed from the point of view of the relevance of the battery bank sizing and in terms of computation cost, this later issue being crucial in view of an integrated optimal design (IOD) process.

Keywords: renewable energy systems, electrochemical storage, sizing, wind profile synthesis, optimization, evolutionary algorithm

#### 1. Introduction

Renewable energy productions are characterized by the unpredictability and the intermittence of the environmental data such as solar irradiation and wind speed in photovoltaic and wind system productions. Therefore, the main criteria when supplying remote areas from renewable source (wind energy/solar irradiation) are the continuity and reliability of electricity supply. The satisfaction of these two criteria can be reached by inserting storage devices (electrochemical devices, hydraulic devices, etc.), but the high owning cost of such solution denotes a major inconvenient for this alternative [1–5]. Hence, an optimal sizing design of the renewable production system coupled with the storage device appears as a guarantee to assure reliability and cheap electricity to supply consumers in isolated sites. The optimal design is achieved by performing global optimization process using a several simulations [6–12]. Nevertheless, these simulations are performed in large time duration, since the environmental data (wind speed, solar irradiation) are characterized by unpredictability which needs great amounts of such data.

In this context, this chapter scrutinizes the optimal design of an electrochemical storage device (a battery bank) associated to a renewable energy system (a wind turbine (WT)) in order to supply continuously a typical farm in a remote site, considering environmental data potentials and load demand variations are a crucial step in the design of these systems. In the case study, the battery bank is exposed to a "time phasing" (Tph) between the generating WT energy/power (consequences of the wind data) and the consumption profile with a time cycle of 24 h, which is a specific problem when sizing the battery bank: indeed, the difference between power production and power consumption profiles is not sufficient to characterize the battery sizing. The time phasing of this power difference is also of prime importance as it sets the battery energy which is also essential in the battery sizing process.

turbulence is neglected. Therefore, a Weibull distribution represents wind speed features. To find the most critical constraints on the battery, we require including all correlations between renewable power production and load profile (e.g., time windows with high wind powers and small load powers and inversely). So, the process computation cost is rather expensive especially when a global integrated design process is performed, where all components have to be simultaneously optimized. Thus, the computation cost of these statistical approaches presents an actual problematic. In order to face this problem, two other methodologies are investigated for reducing environmental data profile durations while keeping their feature trace in terms of variability, intensity, and statistics. These approaches are based on the original approach proposed in [13]. This latter approach is adapted for compacting wind speed profiles. The idea consists of aggregating elementaryparameterized segments to generate a compact environmental data profiles. This is performed by satisfying target indicators representing the environmental data features of a reference profile of larger duration. This inverse problem involving the determination of the segment parameters is solved using a genetic algorithm.

New Design Methodologies for Sizing Electrochemical Storage in Renewable Energy Systems…

The considered system is a 8-kW-full WT battery charger without active control and with minimum number of sensors (Figure 1). This WT is sizing in manner that the wind power extraction of this configuration matches very closely the behavior of active WT systems operating at optimal wind powers by using an MPPT control device. The deterministic load profile is set on 24 h and day by day repeated as

A lead acid Yuasa NP 38-12I is considered as a battery element. The basic

The battery sizing algorithm is based on an upper saturated integration of powers in the battery bank. The idea of a saturated integration of the battery power is related to considering charge powers are no more integrated if the state of charge (SOC) of the storage device reaches its maximal level. Thus, we consider that charge power is wasted in order to avoid the storage device oversizing occasioned with a simple integration especially during huge wind speeds with reduced

2. Renewable system description

DOI: http://dx.doi.org/10.5772/intechopen.85613

characteristics are summarized in Table 1.

3. Statistical battery bank sizing methodologies

Basic characteristics of the considered lead acid battery element.

3.1 First statistical approach (environmental data profile distribution)

At a particular location characterized by a specific wind-energy potential, wind speed can be predicted by several statistical distribution models from the

indicated in Figure 2.

consumption.

Table 1.

5

Four generic battery sizing methodologies are investigated. Two methodologies are based on statistical approaches, and two other methodologies are based on compacting environmental data duration. These methodologies are applied, as a case study, on a renewable energy system consisting of 8 kW standalone wind turbine (Figure 1).

Statistical methodologies determine the power and energy constraints associated with the battery bank from temporal Monte-Carlo-based simulations including environmental data and consumption profile variations. Environmental data evolution is considered as stochastic, while the consumption demand is deterministically day to day regenerated (Figure 2). Only slow dynamics of the wind potential is taken into account. This means that fast dynamics of wind speed related to

Figure 1. Case study: a WT system with battery for standalone application.

Figure 2. Daily load demand profile.

New Design Methodologies for Sizing Electrochemical Storage in Renewable Energy Systems… DOI: http://dx.doi.org/10.5772/intechopen.85613

turbulence is neglected. Therefore, a Weibull distribution represents wind speed features. To find the most critical constraints on the battery, we require including all correlations between renewable power production and load profile (e.g., time windows with high wind powers and small load powers and inversely). So, the process computation cost is rather expensive especially when a global integrated design process is performed, where all components have to be simultaneously optimized. Thus, the computation cost of these statistical approaches presents an actual problematic. In order to face this problem, two other methodologies are investigated for reducing environmental data profile durations while keeping their feature trace in terms of variability, intensity, and statistics. These approaches are based on the original approach proposed in [13]. This latter approach is adapted for compacting wind speed profiles. The idea consists of aggregating elementaryparameterized segments to generate a compact environmental data profiles. This is performed by satisfying target indicators representing the environmental data features of a reference profile of larger duration. This inverse problem involving the determination of the segment parameters is solved using a genetic algorithm.

#### 2. Renewable system description

The considered system is a 8-kW-full WT battery charger without active control and with minimum number of sensors (Figure 1). This WT is sizing in manner that the wind power extraction of this configuration matches very closely the behavior of active WT systems operating at optimal wind powers by using an MPPT control device. The deterministic load profile is set on 24 h and day by day repeated as indicated in Figure 2.

A lead acid Yuasa NP 38-12I is considered as a battery element. The basic characteristics are summarized in Table 1.

The battery sizing algorithm is based on an upper saturated integration of powers in the battery bank. The idea of a saturated integration of the battery power is related to considering charge powers are no more integrated if the state of charge (SOC) of the storage device reaches its maximal level. Thus, we consider that charge power is wasted in order to avoid the storage device oversizing occasioned with a simple integration especially during huge wind speeds with reduced consumption.


#### Table 1.

In this context, this chapter scrutinizes the optimal design of an electrochemical storage device (a battery bank) associated to a renewable energy system (a wind turbine (WT)) in order to supply continuously a typical farm in a remote site, considering environmental data potentials and load demand variations are a crucial step in the design of these systems. In the case study, the battery bank is exposed to a "time phasing" (Tph) between the generating WT energy/power (consequences of the wind data) and the consumption profile with a time cycle of 24 h, which is a specific problem when sizing the battery bank: indeed, the difference between power production and power consumption profiles is not sufficient to characterize the battery sizing. The time phasing of this power difference is also of prime importance as it sets the battery energy which is also essential in the battery sizing

Four generic battery sizing methodologies are investigated. Two methodologies

Statistical methodologies determine the power and energy constraints associated

are based on statistical approaches, and two other methodologies are based on compacting environmental data duration. These methodologies are applied, as a case study, on a renewable energy system consisting of 8 kW standalone wind

with the battery bank from temporal Monte-Carlo-based simulations including environmental data and consumption profile variations. Environmental data evolution is considered as stochastic, while the consumption demand is deterministically day to day regenerated (Figure 2). Only slow dynamics of the wind potential is taken into account. This means that fast dynamics of wind speed related to

process.

Figure 1.

Figure 2.

4

Daily load demand profile.

Case study: a WT system with battery for standalone application.

turbine (Figure 1).

Energy Storage Devices

Basic characteristics of the considered lead acid battery element.

#### 3. Statistical battery bank sizing methodologies

#### 3.1 First statistical approach (environmental data profile distribution)

At a particular location characterized by a specific wind-energy potential, wind speed can be predicted by several statistical distribution models from the wind-energy potential. In this approach, the sizing process is based on the generation of a wind cycle from its statistical distribution [14, 15].

Note that the battery power used by the sizing algorithm is given by:

New Design Methodologies for Sizing Electrochemical Storage in Renewable Energy Systems…

3.2 Second statistical approach (extracted wind power distribution)

waiting until stabilization of the number of battery cells.

sizing process.

DOI: http://dx.doi.org/10.5772/intechopen.85613

first methodology.

Table 2.

7

Statistical approach results.

Table 2 shows the battery element number and the computational time (TCPU) under different wind speed profiles. The computational time is the time needed by the processor to simulate the system model and to perform the storage device

In order to reduce TCPU, a critical factor in an integrated optimal design (IOD) context, this approach is based on the direct generation of the extracted power (PWT) histogram instead of the wind speed histogram as proposed in the first methodology. The extracted power for each wind speed interval is estimated by simulating the WT system. PWT is synthesized on the same time scales as with the

The PWT histogram is built from wind statistics. Thus, we obtain directly the WT power profile from its distribution by means of random number generation and interpolation techniques exactly as described in the first methodology. Therefore, the PWT can be directly generated before to obtain the battery power PBAT used for the storage bank sizing process (Figure 5). Similarly to the first methodology, 11 PWT cycles are produced with a progressive duration from 1 to 200 days and

The consumption profile and the power and energy levels which depend on the wind potential magnitude and phase decide the storage device sizing. Hence, determining the pertinent storage device sizing must be under the "worst" case conditions (maximum power and energy). In order to realize these conditions, several wind speed profiles with increasing duration have to be produced until battery sizing stabilization (Figure 6), i.e., battery cells become quasi-constant.

The synoptic of the random process of wind speed generation of the is shown in Figure 3. The continuous temporal wind speed profiles are generated from statistical distribution by interpolating some number of samples generated with a random number generator according to the recognized statistical distribution.

Figure 4 shows the synoptic battery bank sizing process. The idea consists of generating 11 wind cycles with a progressive duration from 1 to 200 days. These cycles are synthesized from a Weibull distribution of the wind speed during Nd days (Nd = {1, 2, 3, 10, 20, 30, 50, 70, 100, 150, 200 days}). After simulation of the WT system, 11 extracted wind powers (Pwind) are produced. The consumption power (Pload) is daily repeated during the Nd days.

Figure 4. Battery bank sizing process based on wind profile generation form its distribution.

New Design Methodologies for Sizing Electrochemical Storage in Renewable Energy Systems… DOI: http://dx.doi.org/10.5772/intechopen.85613

Note that the battery power used by the sizing algorithm is given by:

Table 2 shows the battery element number and the computational time (TCPU) under different wind speed profiles. The computational time is the time needed by the processor to simulate the system model and to perform the storage device sizing process.

#### 3.2 Second statistical approach (extracted wind power distribution)

In order to reduce TCPU, a critical factor in an integrated optimal design (IOD) context, this approach is based on the direct generation of the extracted power (PWT) histogram instead of the wind speed histogram as proposed in the first methodology. The extracted power for each wind speed interval is estimated by simulating the WT system. PWT is synthesized on the same time scales as with the first methodology.

The PWT histogram is built from wind statistics. Thus, we obtain directly the WT power profile from its distribution by means of random number generation and interpolation techniques exactly as described in the first methodology. Therefore, the PWT can be directly generated before to obtain the battery power PBAT used for the storage bank sizing process (Figure 5). Similarly to the first methodology, 11 PWT cycles are produced with a progressive duration from 1 to 200 days and waiting until stabilization of the number of battery cells.


#### Table 2. Statistical approach results.

wind-energy potential. In this approach, the sizing process is based on the genera-

The consumption profile and the power and energy levels which depend on the wind potential magnitude and phase decide the storage device sizing. Hence, determining the pertinent storage device sizing must be under the "worst" case conditions (maximum power and energy). In order to realize these conditions, several wind speed profiles with increasing duration have to be produced until battery sizing stabilization (Figure 6), i.e., battery cells become quasi-constant.

The synoptic of the random process of wind speed generation of the is shown in Figure 3. The continuous temporal wind speed profiles are generated from statistical distribution by interpolating some number of samples generated with a random

Figure 4 shows the synoptic battery bank sizing process. The idea consists of generating 11 wind cycles with a progressive duration from 1 to 200 days. These cycles are synthesized from a Weibull distribution of the wind speed during Nd days (Nd = {1, 2, 3, 10, 20, 30, 50, 70, 100, 150, 200 days}). After simulation of the WT system, 11 extracted wind powers (Pwind) are produced. The consumption

number generator according to the recognized statistical distribution.

power (Pload) is daily repeated during the Nd days.

Battery bank sizing process based on wind profile generation form its distribution.

Figure 3.

Figure 4.

6

Wind speed generation process.

Energy Storage Devices

tion of a wind cycle from its statistical distribution [14, 15].

4. Battery sizing based on "compact synthesis approach"

4.1 Synthesis process of compact environmental data profiles

(ΔSminref ≤ ΔS<sup>n</sup> ≤ ΔSmaxref) and its duration Δt<sup>n</sup> (0 ≤ Δtn ≤ Δtcompact). In order to fulfill the constraint related to the time duration, i.e.,

bank for which maximum powers and energy range are pertinent).

4.2 Compact synthesis approach based on storage system features

The first approach uses, as target indicators, the storage system features. The storage system global sizing is related to the maximum storage power PBATMAX, the minimum storage power PBATMIN, and the maximum energy quantity imposed to this storage ES. These variables target indicators of the inverse problem, and

Plot of battery cell number versus cycle duration. (a) Variable profile generated by segments, (b) Pattern

∑Δtn = Δtcompact, a time scaling step is executed after the variable profile generation. The compact fictitious profile generation synthesis consists of finding all segment parameters fulfilling all target indicators given by the reference data (actual profile) on the reduced duration Δtcompact. This is performed by solving an inverse problem, using evolutionary algorithms, with 2 N parameters where N denotes the compact profile segment number [16]. As evolutionary algorithm we have chosen the clearing method [17] well suited to treat this kind of problem with high dimensionality and high multimodality. Target indicators are also related to the design context itself (in this case study, the WT system has to charge a battery

dence with reference actual profile.

DOI: http://dx.doi.org/10.5772/intechopen.85613

Figure 7.

9

parameters: ΔSn et Δtn.

In this approach an actual wind speed profile of 200 days duration is considered as reference data. In order to generate a compact wind speed profile with a reduced duration Δtcompact, the "compact synthesis process" is applied on this profile. Two methodologies are scrutinized, differenced by the target indicators used for generating the fictitious compact wind speed profile in order to establish its correspon-

New Design Methodologies for Sizing Electrochemical Storage in Renewable Energy Systems…

The principle of compact environmental data synthesis process consists of the generation of a fictitious profile of temperature, solar irradiation, wind speed, etc. by satisfying some constraints related essentially to variable characteristics, i.e., minimum, maximum, and average values, probability distribution function, etc. These constraints are expressed in terms of "target indicators" that can be evaluated from a set of reference profiles usually of large duration: here we have considered a 200-day wind profile. The fictitious profile is obtained by aggregating elementary segments as shown in Figure 7. Each segment is characterized by its amplitude ΔS<sup>n</sup>

Figure 5. Storage device sizing process based on extracted power generation profile form its distribution.

Figure 6. Plot of battery cell number versus cycle duration.

#### 3.3 Results

To face the stochastic nature of wind speed, several simulations of the 11 wind speed cycles (with an increasing number of days from 1 to 200) have been performed. Table 2 gives the average number of battery cells <Nbt> obtained after 10 simulations for both methodologies. <Nbt> obtained from the 11 generated wind speed cycles are shown in Figure 6.

New Design Methodologies for Sizing Electrochemical Storage in Renewable Energy Systems… DOI: http://dx.doi.org/10.5772/intechopen.85613

#### 4. Battery sizing based on "compact synthesis approach"

In this approach an actual wind speed profile of 200 days duration is considered as reference data. In order to generate a compact wind speed profile with a reduced duration Δtcompact, the "compact synthesis process" is applied on this profile. Two methodologies are scrutinized, differenced by the target indicators used for generating the fictitious compact wind speed profile in order to establish its correspondence with reference actual profile.

#### 4.1 Synthesis process of compact environmental data profiles

The principle of compact environmental data synthesis process consists of the generation of a fictitious profile of temperature, solar irradiation, wind speed, etc. by satisfying some constraints related essentially to variable characteristics, i.e., minimum, maximum, and average values, probability distribution function, etc. These constraints are expressed in terms of "target indicators" that can be evaluated from a set of reference profiles usually of large duration: here we have considered a 200-day wind profile. The fictitious profile is obtained by aggregating elementary segments as shown in Figure 7. Each segment is characterized by its amplitude ΔS<sup>n</sup> (ΔSminref ≤ ΔS<sup>n</sup> ≤ ΔSmaxref) and its duration Δt<sup>n</sup> (0 ≤ Δtn ≤ Δtcompact).

In order to fulfill the constraint related to the time duration, i.e., ∑Δtn = Δtcompact, a time scaling step is executed after the variable profile generation. The compact fictitious profile generation synthesis consists of finding all segment parameters fulfilling all target indicators given by the reference data (actual profile) on the reduced duration Δtcompact. This is performed by solving an inverse problem, using evolutionary algorithms, with 2 N parameters where N denotes the compact profile segment number [16]. As evolutionary algorithm we have chosen the clearing method [17] well suited to treat this kind of problem with high dimensionality and high multimodality. Target indicators are also related to the design context itself (in this case study, the WT system has to charge a battery bank for which maximum powers and energy range are pertinent).

#### 4.2 Compact synthesis approach based on storage system features

The first approach uses, as target indicators, the storage system features. The storage system global sizing is related to the maximum storage power PBATMAX, the minimum storage power PBATMIN, and the maximum energy quantity imposed to this storage ES. These variables target indicators of the inverse problem, and

Figure 7.

Plot of battery cell number versus cycle duration. (a) Variable profile generated by segments, (b) Pattern parameters: ΔSn et Δtn.

3.3 Results

Figure 6.

8

Figure 5.

Energy Storage Devices

speed cycles are shown in Figure 6.

Plot of battery cell number versus cycle duration.

To face the stochastic nature of wind speed, several simulations of the 11 wind

performed. Table 2 gives the average number of battery cells <Nbt> obtained after 10 simulations for both methodologies. <Nbt> obtained from the 11 generated wind

speed cycles (with an increasing number of days from 1 to 200) have been

Storage device sizing process based on extracted power generation profile form its distribution.

they are extracted from simulation of the WT system over the reference profile of days.

The reference value of the storage useful energy ESref is given by the following equation:

$$E\_{\text{Sref}} = \text{max}E(t) - \text{min}E(t) \tag{1}$$

with

$$E(t) = \int\_{0}^{t} P\_{BAT}(\tau) d\tau \quad t \in \left[0, \Delta t\_{ref}\right] \tag{2}$$

To avoid oversizing during wide charge period (reduced consumption versus huge winds), the storage is only sized in discharge mode. Thus, E(t) is computed as a saturated integral, with 0 as upper limit. To take into account the reference wind cycle statistic features, an additional target indicator is considered: the cumulative distribution function CDF(Vref) calculated from the corresponding probability density function PDFref which is evaluated on 20 equally spaced intervals between 0 and the maximum wind speed value Vrefmax and related to the reference wind speed behavior.

Therefore, the inverse problem is set to minimize the global error ε in the synthesis profile process by

$$\varepsilon = \left(\frac{E\_S - E\_{S\text{ref}}}{E\_{S\text{ref}}}\right)^2 + \left(\frac{P\_{BAT\text{MAX}} - P\_{BAT\text{MAX}\text{ref}}}{P\_{BAT\text{MAX}\text{ref}}}\right)^2 + \left(\frac{P\_{BAT\text{MIN}} - P\_{BAT\text{MIN}\text{ref}}}{P\_{BAT\text{MIN}\text{ref}}}\right)^2 + \varepsilon\_{\text{stat}}\tag{3}$$

where the statistic error εstat denotes the mean squared error between both CDFs relative to reference and generated wind speed profiles:

$$\varepsilon\_{\text{stat}} = \frac{\mathbf{1}}{\mathbf{20}} \times \sum\_{k=1}^{20} \left( \frac{\text{CDF}(k) - \text{CDF}\_{\text{ref}}(k)}{\text{CDF}\_{\text{ref}}(k)} \right)^2 \tag{4}$$

4.3 Compact synthesis approach using wind-based targets

Actual "reference" wind speed profile, storage power, and energy.

Influence of ΔTcompact on the global error ε.

We first consider three indicators Vmax, Vmin, and <V<sup>3</sup>

Note that <V<sup>3</sup>

11

Figure 8.

Table 3.

The selected target indicators are only related to the wind features: this approach can then be considered as generic in the case of any WT system whatever its sizing.

> is used instead of the average wind speed value <V> because the

maximum and minimum speed values and the average cubic wind speed value.

New Design Methodologies for Sizing Electrochemical Storage in Renewable Energy Systems…

DOI: http://dx.doi.org/10.5772/intechopen.85613

WT power is directly proportional to the cubic wind speed value. Similarly to the previous approach, we also add the CDF as target indicator associated with the wind profile in order to take account of the wind statistic. Finally, we consider as last indicator related to "wind energy" with the variable EV which is defined as

> representing the

All "ref" indexed variables are based on the reference wind profile of Figure 8. The inverse problem is solved with the clearing algorithm [17] using a population size of 100 individuals and a number of generations of 500,000.

Multiple optimization runs are performed with different compaction times Δtcompact. In order to guarantee a global error ε less than 10�, the minimum compaction time was determined using dichotomous search. The values of ε versus compaction time are shown in Table 3. The minimum value for Δtcompact assuring the completion of the target indicators with adequate accuracy is about 10 days. The generated wind profile is obtained from the aggregation of 109 elementary segments fulfilling all target indicators. The characteristics of this compact wind cycle and its CDF are displayed in Figure 9. It can be seen from this figure that the CDF of this wind profile closely coincides with that of the reference wind profile.

In Table 4 a comparison between the target indicator values related to the storage sizing of the reference profile and the compact profile is generated with the clearing algorithm. A good agreement between those values indicates that the compact wind profile will lead to the same storage device sizing as with the reference wind profile on larger duration.

New Design Methodologies for Sizing Electrochemical Storage in Renewable Energy Systems… DOI: http://dx.doi.org/10.5772/intechopen.85613

Figure 8. Actual "reference" wind speed profile, storage power, and energy.


Table 3.

they are extracted from simulation of the WT system over the reference profile

E tðÞ¼

ðt

0

PBAT MAX � PBAT MAX ref PBAT MAX ref !<sup>2</sup>

size of 100 individuals and a number of generations of 500,000.

relative to reference and generated wind speed profiles:

<sup>ε</sup>stat <sup>¼</sup> <sup>1</sup> 20 � ∑ 20 k¼1

The reference value of the storage useful energy ESref is given by the following

PBATð Þτ dτ t∈ 0; Δtref

To avoid oversizing during wide charge period (reduced consumption versus huge winds), the storage is only sized in discharge mode. Thus, E(t) is computed as a saturated integral, with 0 as upper limit. To take into account the reference wind cycle statistic features, an additional target indicator is considered: the cumulative distribution function CDF(Vref) calculated from the corresponding probability density function PDFref which is evaluated on 20 equally spaced intervals between 0 and the maximum wind speed value Vrefmax and related to the reference wind speed

Therefore, the inverse problem is set to minimize the global error ε in the

where the statistic error εstat denotes the mean squared error between both CDFs

All "ref" indexed variables are based on the reference wind profile of Figure 8. The inverse problem is solved with the clearing algorithm [17] using a population

Multiple optimization runs are performed with different compaction times Δtcompact. In order to guarantee a global error ε less than 10�, the minimum compaction time was determined using dichotomous search. The values of ε versus compaction time are shown in Table 3. The minimum value for Δtcompact assuring the completion of the target indicators with adequate accuracy is about 10 days. The generated wind profile is obtained from the aggregation of 109 elementary segments fulfilling all target indicators. The characteristics of this compact wind cycle and its CDF are displayed in Figure 9. It can be seen from this figure that the CDF of this wind profile closely coincides with that of the reference wind profile. In Table 4 a comparison between the target indicator values related to the storage sizing of the reference profile and the compact profile is generated with the clearing algorithm. A good agreement between those values indicates that the compact wind profile will lead to the same storage device sizing as with the reference

þ

CDF kð Þ� CDFrefð Þk CDFrefð Þk

!<sup>2</sup>

ESref ¼ maxE tðÞ� minE tð Þ (1)

� � (2)

PBAT MIN � PBAT MIN ref PBAT MIN ref !<sup>2</sup>

þ εstat

(3)

(4)

of days.

Energy Storage Devices

equation:

with

behavior.

<sup>ε</sup> <sup>¼</sup> ES � ESref ESref !<sup>2</sup>

synthesis profile process by

þ

wind profile on larger duration.

10

Influence of ΔTcompact on the global error ε.

#### 4.3 Compact synthesis approach using wind-based targets

The selected target indicators are only related to the wind features: this approach can then be considered as generic in the case of any WT system whatever its sizing.

We first consider three indicators Vmax, Vmin, and <V<sup>3</sup> > representing the maximum and minimum speed values and the average cubic wind speed value. Note that <V<sup>3</sup> > is used instead of the average wind speed value <V> because the WT power is directly proportional to the cubic wind speed value. Similarly to the previous approach, we also add the CDF as target indicator associated with the wind profile in order to take account of the wind statistic. Finally, we consider as last indicator related to "wind energy" with the variable EV which is defined as

EV ref <sup>¼</sup> <sup>Δ</sup>tcompact Δtref

energy EVref is scaled according to the compact profile duration:

EV ref <sup>¼</sup> <sup>Δ</sup>tcompact Δtref

parameters as in the previous subsection. Multiple optimization runs were

Target indicators of the reference versus generated wind speed profile with Δtcompact = 10 days.

tors are very close in both cases.

DOI: http://dx.doi.org/10.5772/intechopen.85613

Figure 10.

Table 5.

13

Generated wind speed with corresponding CDF.

parameters as in the previous subsection. Multiple optimization runs were

The inverse problem is solved with the clearing algorithm with the same control

performed with different compaction times Δtcompact. The minimum value for this variable ensuring a global error less than 10�<sup>2</sup> was identical to that found with the previous approach (i.e., 10 days). Figure 10 shows the characteristics of the generated wind profile obtained for Δtcompact = 10 days, from the aggregation of 130 elementary segments fulfilling all target indicators. The good agreement between the compact generated profile and the reference profile can also be observed in this figure in terms of CDF. Finally, Table 5 shows that the values of the target indica-

New Design Methodologies for Sizing Electrochemical Storage in Renewable Energy Systems…

Here, εstat is computed according to (3), and the reference intermittent wind

The inverse problem is solved with the clearing algorithm with the same control

performed with different compaction times Δtcompact. The minimum value for this variable ensuring a global error less than 10�<sup>2</sup> was identical to that found with the previous approach (i.e., 10 days). Figure 10 shows the characteristics of the generated wind profile obtained for Δtcompact = 10 days, from the aggregation of 130 elementary segments fulfilling all target indicators. The good agreement between

� EVref Δt ð Þ real (8)

� EVref Δt ð Þ real (9)

#### Figure 9.

Generated wind speed with corresponding CDF.


#### Table 4.

Target indicators of the generated wind speed profile.

$$E\_V = \max\_{t \in [0, \Delta t]} E(t) - \min\_{t \in [0, \Delta t]} E(t) \tag{5}$$

with

$$E(t) = \int\_0^t (V^3(\tau) - \, \ast V^3 \rhd) d\tau \qquad t \in [0, \Delta t] \tag{6}$$

where EV represents an "intermittent wind pseudo energy". In fact, EV plays a similar role with ES in the previous approach for the storage system.

Note that the wind power being proportional to V<sup>3</sup> , Ev is not actually an energy (in Joules or kWh) but can be seen as a "pseudo energy" which is qualitatively related to wind energy.

The global error ε to be minimized with this second approach can be expressed as

$$\varepsilon = \left(\frac{V\_{\text{max}} - V\_{\text{max}\,\text{ref}}}{V\_{\text{max}\,\text{ref}}}\right)^2 + \left(\frac{V\_{\text{min}} - V\_{\text{min}\,\text{ref}}}{V\_{\text{min}\,\text{ref}}}\right)^2 + \left(\frac{\kappa V^3 - \kappa \, V^3 \boldsymbol{\upbeta}\_{\text{ref}}}{\kappa \, V^3 \boldsymbol{\upbeta}\_{\text{ref}}}\right)^2 + \left(\frac{E\_V - E\_V \boldsymbol{\uppi}\_{\text{ref}}}{E\_V \boldsymbol{\uppi}\_{\text{ref}}}\right)^2 + \varepsilon\_{\text{stat}} \tag{7}$$

where εstat is computed according to (3) and where the reference intermittent wind energy EVref is scaled according to the compact profile duration:

New Design Methodologies for Sizing Electrochemical Storage in Renewable Energy Systems… DOI: http://dx.doi.org/10.5772/intechopen.85613

$$E\_{V\text{ }ref} = \frac{\Delta t\_{compact}}{\Delta t\_{ref}} \times E\_{V\text{ref}}(\Delta t\_{real}) \tag{8}$$

The inverse problem is solved with the clearing algorithm with the same control parameters as in the previous subsection. Multiple optimization runs were performed with different compaction times Δtcompact. The minimum value for this variable ensuring a global error less than 10�<sup>2</sup> was identical to that found with the previous approach (i.e., 10 days). Figure 10 shows the characteristics of the generated wind profile obtained for Δtcompact = 10 days, from the aggregation of 130 elementary segments fulfilling all target indicators. The good agreement between the compact generated profile and the reference profile can also be observed in this figure in terms of CDF. Finally, Table 5 shows that the values of the target indicators are very close in both cases.

Here, εstat is computed according to (3), and the reference intermittent wind energy EVref is scaled according to the compact profile duration:

$$E\_{V\text{ }ref} = \frac{\Delta t\_{compact}}{\Delta t\_{ref}} \times E\_{V\text{ref}}(\Delta t\_{real}) \tag{9}$$

The inverse problem is solved with the clearing algorithm with the same control parameters as in the previous subsection. Multiple optimization runs were performed with different compaction times Δtcompact. The minimum value for this variable ensuring a global error less than 10�<sup>2</sup> was identical to that found with the previous approach (i.e., 10 days). Figure 10 shows the characteristics of the generated wind profile obtained for Δtcompact = 10 days, from the aggregation of 130 elementary segments fulfilling all target indicators. The good agreement between

Figure 10. Generated wind speed with corresponding CDF.


#### Table 5.

Target indicators of the reference versus generated wind speed profile with Δtcompact = 10 days.

EV ¼ max t∈½ � 0;Δt

V3

similar role with ES in the previous approach for the storage system.

ð Þ� <sup>τ</sup> <sup>&</sup>lt;V<sup>3</sup>

(in Joules or kWh) but can be seen as a "pseudo energy" which is qualitatively

The global error ε to be minimized with this second approach can be

where EV represents an "intermittent wind pseudo energy". In fact, EV plays a

<sup>þ</sup> <sup>&</sup>lt;V<sup>3</sup>

where εstat is computed according to (3) and where the reference intermittent

<sup>&</sup>gt; � <sup>&</sup>lt;V<sup>3</sup>

<V<sup>3</sup> >ref !<sup>2</sup>

>ref

þ

E tðÞ¼

Target indicators of the generated wind speed profile.

þ

ðt

0

Note that the wind power being proportional to V<sup>3</sup>

Vmin � Vmin ref Vmin ref !<sup>2</sup>

wind energy EVref is scaled according to the compact profile duration:

with

Table 4.

Figure 9.

Energy Storage Devices

Generated wind speed with corresponding CDF.

related to wind energy.

<sup>ε</sup> <sup>¼</sup> <sup>V</sup>max � <sup>V</sup>max ref Vmax ref !<sup>2</sup>

expressed as

12

E tðÞ� min t∈½ � 0;Δt

<sup>&</sup>gt; � �d<sup>τ</sup> <sup>t</sup>∈½ � <sup>0</sup>; <sup>Δ</sup><sup>t</sup> (6)

E tð Þ (5)

, Ev is not actually an energy

EV � EV ref EV ref !<sup>2</sup>

þ εstat

(7)

a given deterministic power demand. These approaches are based on the exploitation of wind speed distribution from a Weibull law or directly the extracted power histogram at the WT output. It has been shown that a robust sizing of the storage device can be obtained from the stochastic generation of either the wind speed profile or the extracted WT output power using a specific algorithm. In this algorithm, the battery required active energy is calculated by upper saturated integration of the battery power. Two supplementary approaches have been developed for compacting wind speed profiles. These approaches consist in generating compact wind profiles by aggregating elementary-parameterized segments in order to fulfill target indicators representing the features of a reference wind profile of larger duration. The inverse problem involving the determination of the segment param-

New Design Methodologies for Sizing Electrochemical Storage in Renewable Energy Systems…

eters is solved with an evolutionary algorithm. It is shown that both latter

speed but also temperature, sun irradiation, etc.).

DOI: http://dx.doi.org/10.5772/intechopen.85613

Acknowledgements

Research and Technology.

Communication ISTIC, Tunisia

provided the original work is properly cited.

Author details

Malek Belouda

15

approaches are able to represent the main features of the reference profile in terms of wind farm potential and are also relevant for evaluating the critical conditions imposed to the battery storage (i.e., power and energy needs) in a hybrid WT system. All sizing methods have yielded roughly to same battery size but with different wind profiles durations. Statistical methods have provided a gain of 2.5 in time window reduction, while compact synthesis methods have led to a gain of 20. From these compacts profiles, subsequent reduction of the computation time should be obtained in the context of the optimization process of such systems. Note that this synthesis approach is very generic and could be extrapolated beyond the particular field of WT design and may be applied in the whole range of electrical engineering applications, by processing any types of environmental variables (wind

This work was supported by the Tunisian Ministry of Higher Education,

1 LAPER, Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis, Tunisia

2 University of Carthage, The Higher Institute of Information Technologies and

© 2019 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,

\*Address all correspondence to: malek.belouda@gmail.com

Figure 11. Illustration of the phase shift of the wind profile (generated with the second method) on the battery sizing.

the compact generated profile and the reference profile can also be observed in this figure in terms of CDF. Finally, Table 5 shows that the values of the target indicators are very close in both cases.

For comparison with the previous approach, we also give the sizing of the battery obtained from the simulation of the compact profile. It should be noted that contrarily to the first approach, the second one does not include phase correlations between wind and load profiles because it only considers wind speed variations to generate the compact wind speed profile. Consequently, the second approach does not ensure finding the most critical constraints on the storage device in terms of production—load phase shift. This can be a posteriori done by sequentially shifting the obtained wind profile on its 10-day time window in compliance with the deterministic load profile day to day repeated. The maximum storage energy quantity ES is computed for each phase shift and the highest (most critical) value is returned (see Figure 11). By this way, a value of 34.4 kWh is obtained for ES which is very close to that resulting from the reference profile simulation (i.e., 32.3 kWh).

#### 5. Conclusions

In this chapter, new methodologies for sizing electrochemical devices into renewable energy systems are presented. As case of study, a battery bank devoted to a standalone WT system has been developed and compared. A passive WT structure, minimizing the number of sensors and the electronic part, has been chosen because of its reliability and its low cost. The two first sizing methodologies take account of stochastic features of wind energy potential in a particular location with

New Design Methodologies for Sizing Electrochemical Storage in Renewable Energy Systems… DOI: http://dx.doi.org/10.5772/intechopen.85613

a given deterministic power demand. These approaches are based on the exploitation of wind speed distribution from a Weibull law or directly the extracted power histogram at the WT output. It has been shown that a robust sizing of the storage device can be obtained from the stochastic generation of either the wind speed profile or the extracted WT output power using a specific algorithm. In this algorithm, the battery required active energy is calculated by upper saturated integration of the battery power. Two supplementary approaches have been developed for compacting wind speed profiles. These approaches consist in generating compact wind profiles by aggregating elementary-parameterized segments in order to fulfill target indicators representing the features of a reference wind profile of larger duration. The inverse problem involving the determination of the segment parameters is solved with an evolutionary algorithm. It is shown that both latter approaches are able to represent the main features of the reference profile in terms of wind farm potential and are also relevant for evaluating the critical conditions imposed to the battery storage (i.e., power and energy needs) in a hybrid WT system. All sizing methods have yielded roughly to same battery size but with different wind profiles durations. Statistical methods have provided a gain of 2.5 in time window reduction, while compact synthesis methods have led to a gain of 20. From these compacts profiles, subsequent reduction of the computation time should be obtained in the context of the optimization process of such systems. Note that this synthesis approach is very generic and could be extrapolated beyond the particular field of WT design and may be applied in the whole range of electrical engineering applications, by processing any types of environmental variables (wind speed but also temperature, sun irradiation, etc.).

#### Acknowledgements

the compact generated profile and the reference profile can also be observed in this figure in terms of CDF. Finally, Table 5 shows that the values of the target indica-

Illustration of the phase shift of the wind profile (generated with the second method) on the battery sizing.

For comparison with the previous approach, we also give the sizing of the battery obtained from the simulation of the compact profile. It should be noted that contrarily to the first approach, the second one does not include phase correlations between wind and load profiles because it only considers wind speed variations to generate the compact wind speed profile. Consequently, the second approach does not ensure finding the most critical constraints on the storage device in terms of production—load phase shift. This can be a posteriori done by sequentially shifting the obtained wind profile on its 10-day time window in compliance with the deterministic load profile day to day repeated. The maximum storage energy quantity ES is computed for each phase shift and the highest (most critical) value is returned (see Figure 11). By this way, a value of 34.4 kWh is obtained for ES which is very close to that resulting from the reference profile simulation (i.e., 32.3 kWh).

In this chapter, new methodologies for sizing electrochemical devices into renewable energy systems are presented. As case of study, a battery bank devoted to a standalone WT system has been developed and compared. A passive WT structure, minimizing the number of sensors and the electronic part, has been chosen because of its reliability and its low cost. The two first sizing methodologies take account of stochastic features of wind energy potential in a particular location with

tors are very close in both cases.

Figure 11.

Energy Storage Devices

5. Conclusions

14

This work was supported by the Tunisian Ministry of Higher Education, Research and Technology.

#### Author details

#### Malek Belouda

1 LAPER, Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis, Tunisia

2 University of Carthage, The Higher Institute of Information Technologies and Communication ISTIC, Tunisia

\*Address all correspondence to: malek.belouda@gmail.com

© 2019 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.

#### References

[1] Gavanidou E, Bakirtzis A. Design of a stand alone system with renewable energy sources using trade-off methods. IEEE Transactions on Energy Conversion. 1992;7(1)

[2] Chedid R, Rahman S. Unit sizing and control of hybrid wind-solar power systems. IEEE Transactions on Energy Conversion. 1997;12(1)

[3] Kellogg W, Nehrir M, Venkataramanan G, Gerez V. Generation unit sizing and cost analysis for stand-alone wind, photovoltaic, and hybrid wind/PV systems. IEEE Transactions On Energy Conversion. 1998;13(1)

[4] Bernard-Agustín JL, Dufo-Lopez R, Rivas-Ascaso DM. Design of isolated hybrid systems minimizing costs and pollutant emissions. Renewable Energy. 2006;31(14):2227-2244

[5] Senjyu T, Hayashi D, Yona A, Urasaki N, Funabashi T. Optimal configuration of power generating systems in isolated island with renewable energy. Renewable Energy. 2007;32:1917-1933

[6] Belfkira R, Nichita C, Reghem P, Barakat G. Modeling and optimal sizing of hybrid energy system. In: International Power Electronics and Motion Control Conference (EPE-PEMC); 1–3 September 2008; Poznan, Poland: IEEE PEMC

[7] Lim JH. Optimal combination and sizing of a new and renewable hybrid generation system. International Journal of Future Generation Communication and Networking. 2012;5(2)

[8] Tran DH, Sareni B, Roboam X, Espanet C. Integrated optimal design of a passive wind turbine system: An experimental validation. IEEE Transactions on Sustainable Energy. 2010;1(1):48-56

[9] Gupta SC, Kumar Y, Agnihotri G. REAST: Renewable energy analysis and sizing tool. Journal of Electrical Systems. 2011;7(2):206-224

Chapter 2

Abstract

ultracapacitor, battery aging

1. Introduction

transportation.

17

New Energy Management

Ultracapacitor Aging

Concepts for Hybrid and Electric

During the lifetime of an energy storage system, its health deteriorates from use

due to irreversible internal changes to the system. This degradation results in decreased capacity and efficiency of the battery or capacitor. This chapter reviews empirical aging models for lithium-ion battery and ultracapacitor energy storage systems. It will explore how operating conditions like large currents, high temperature, or deep discharge cycles impact the health of the energy storage system. After reviewing aging models, this chapter will then show how these models can be used in vehicle energy management control systems to reduce energy storage system aging. This includes both aging-aware control and control of hybrid energy storage

Keywords: electric vehicle, hybrid vehicle, energy management, lithium ion,

The internal combustion engine is a major contributor to greenhouse gas emissions and hydrocarbon pollution across the globe. Motor vehicles account for a major portion of pollutants such as carbon monoxide, nitrogen oxide, and volatile organic compounds [1]. Alternative powertrain vehicles (APVs), such as electric vehicles (EVs) and hybrid-electric vehicles (HEVs), are potential technological solutions to reduce transportation-sector emissions and fuel consumption. However, APVs require large amounts of battery-stored energy, which can be cost and weight prohibitive [2]. Degradation of the battery further adds to the lifetime cost of an APV, and battery degradation rate has been shown to be inversely correlated with fuel economy [3, 4]. Technologies that improve battery lifespan and fuel economy will reduce this lifetime cost and hasten the adoption of sustainable

Lithium-based batteries serve as the current main battery of choice for vehicle transportation because of their high energy density and ability for high cycle life. Improving cycle life of lithium batteries means limiting large currents in and out of

systems (systems that include both a battery and an ultracapacitor).

Powertrains: Considering the

Francis Assadian, Kevin Mallon and Brian Walker

Impact of Lithium Battery and

[10] Protogeropoulos C, Brinkworth B, Marshall R. Sizing and technoeconomical optimization for hybrid solar photovoltaic/wind power systems with battery storage. International Journal of Energy Research. 1997;21

[11] Morgan T, Marshall R, Brinkworth B. "ARES"—A refined simulation program for the sizing and optimization of autonomous hybrid energy systems. Solar Energy. 1997;59(4–6)

[12] Seeling-Hochmuth G. A combined optimization concept for the design and operation strategy of hybrid-PV energy systems. Solar Energy. 1997;61(2)

[13] Jaafar A, Sareni B, Roboam X. Signal synthesis by means of evolutionary algorithms. Journal on Inverse Problems in Science and Engineering. 2012; 20(12):93-104

[14] Belouda M, Belhadj J, Sareni B, Roboam X. Battery sizing for a stand alone passive wind system using statistical techniques. In: 8th International Multi-Conference on Systems, Signals & Devices; Sousse, Tunisia. 2011

[15] Roboam X, Abdelli A, Sareni B. Optimization of a passive small wind turbine based on mixed Weibullturbulence statistics of wind. In: Electrimacs 2008; Québec, Canada. 2008

[16] Schwefel H-P. Evolution and Optimum Seeking. Wiley; 1995

[17] Petrowski A. A clearing procedure as a niching method for genetic algorithms. In: Proceedings of the IEEE International Conference on Evolutionary Computation; Nagoya, Japan: 1996. pp. 798-803

#### Chapter 2

References

Energy Storage Devices

[1] Gavanidou E, Bakirtzis A. Design of a stand alone system with renewable energy sources using trade-off methods.

[9] Gupta SC, Kumar Y, Agnihotri G. REAST: Renewable energy analysis and sizing tool. Journal of Electrical Systems.

[10] Protogeropoulos C, Brinkworth B,

[11] Morgan T, Marshall R, Brinkworth B. "ARES"—A refined simulation program for the sizing and optimization of autonomous hybrid energy systems.

[12] Seeling-Hochmuth G. A combined optimization concept for the design and operation strategy of hybrid-PV energy systems. Solar Energy. 1997;61(2)

[13] Jaafar A, Sareni B, Roboam X. Signal synthesis by means of evolutionary algorithms. Journal on Inverse Problems in Science and Engineering. 2012;

[14] Belouda M, Belhadj J, Sareni B, Roboam X. Battery sizing for a stand alone passive wind system using statistical techniques. In: 8th International Multi-Conference on Systems, Signals & Devices; Sousse,

[15] Roboam X, Abdelli A, Sareni B. Optimization of a passive small wind turbine based on mixed Weibullturbulence statistics of wind. In: Electrimacs 2008; Québec, Canada.

[16] Schwefel H-P. Evolution and Optimum Seeking. Wiley; 1995

International Conference on

Japan: 1996. pp. 798-803

[17] Petrowski A. A clearing procedure as a niching method for genetic

algorithms. In: Proceedings of the IEEE

Evolutionary Computation; Nagoya,

Marshall R. Sizing and technoeconomical optimization for hybrid solar photovoltaic/wind power systems with battery storage. International Journal of Energy Research. 1997;21

Solar Energy. 1997;59(4–6)

20(12):93-104

Tunisia. 2011

2008

2011;7(2):206-224

[2] Chedid R, Rahman S. Unit sizing and control of hybrid wind-solar power systems. IEEE Transactions on Energy

Generation unit sizing and cost analysis for stand-alone wind, photovoltaic, and

[4] Bernard-Agustín JL, Dufo-Lopez R, Rivas-Ascaso DM. Design of isolated hybrid systems minimizing costs and pollutant emissions. Renewable Energy.

IEEE Transactions on Energy Conversion. 1992;7(1)

Conversion. 1997;12(1)

[3] Kellogg W, Nehrir M, Venkataramanan G, Gerez V.

2006;31(14):2227-2244

2007;32:1917-1933

Poland: IEEE PEMC

2010;1(1):48-56

16

[5] Senjyu T, Hayashi D, Yona A, Urasaki N, Funabashi T. Optimal configuration of power generating systems in isolated island with

renewable energy. Renewable Energy.

[6] Belfkira R, Nichita C, Reghem P, Barakat G. Modeling and optimal sizing

International Power Electronics and Motion Control Conference (EPE-PEMC); 1–3 September 2008; Poznan,

[7] Lim JH. Optimal combination and sizing of a new and renewable hybrid generation system. International Journal of Future Generation Communication

of hybrid energy system. In:

and Networking. 2012;5(2)

[8] Tran DH, Sareni B, Roboam X, Espanet C. Integrated optimal design of a passive wind turbine system: An experimental validation. IEEE Transactions on Sustainable Energy.

1998;13(1)

hybrid wind/PV systems. IEEE Transactions On Energy Conversion.

## New Energy Management Concepts for Hybrid and Electric Powertrains: Considering the Impact of Lithium Battery and Ultracapacitor Aging

Francis Assadian, Kevin Mallon and Brian Walker

### Abstract

During the lifetime of an energy storage system, its health deteriorates from use due to irreversible internal changes to the system. This degradation results in decreased capacity and efficiency of the battery or capacitor. This chapter reviews empirical aging models for lithium-ion battery and ultracapacitor energy storage systems. It will explore how operating conditions like large currents, high temperature, or deep discharge cycles impact the health of the energy storage system. After reviewing aging models, this chapter will then show how these models can be used in vehicle energy management control systems to reduce energy storage system aging. This includes both aging-aware control and control of hybrid energy storage systems (systems that include both a battery and an ultracapacitor).

Keywords: electric vehicle, hybrid vehicle, energy management, lithium ion, ultracapacitor, battery aging

#### 1. Introduction

The internal combustion engine is a major contributor to greenhouse gas emissions and hydrocarbon pollution across the globe. Motor vehicles account for a major portion of pollutants such as carbon monoxide, nitrogen oxide, and volatile organic compounds [1]. Alternative powertrain vehicles (APVs), such as electric vehicles (EVs) and hybrid-electric vehicles (HEVs), are potential technological solutions to reduce transportation-sector emissions and fuel consumption. However, APVs require large amounts of battery-stored energy, which can be cost and weight prohibitive [2]. Degradation of the battery further adds to the lifetime cost of an APV, and battery degradation rate has been shown to be inversely correlated with fuel economy [3, 4]. Technologies that improve battery lifespan and fuel economy will reduce this lifetime cost and hasten the adoption of sustainable transportation.

Lithium-based batteries serve as the current main battery of choice for vehicle transportation because of their high energy density and ability for high cycle life. Improving cycle life of lithium batteries means limiting large currents in and out of the battery as much as possible to lower degradation and heat affects. Ultracapacitors (UC) can be added to vehicles to improve battery life by taking excess power away from the battery and storing it in temporary energy storage [5]. Capacitors can quickly unload power back into the system for high load situations such as a hard acceleration, taking away the need for a high-power drain from the battery.

2.2 Ultracapacitor aging

DOI: http://dx.doi.org/10.5772/intechopen.83770

time, temperature, and cell voltage [23–25].

3. Control-oriented aging

control applications.

3.1 Power-law model

aging model as follows:

19

dQ loss

hours), current Ib (in C), and temperature T (in K).

Lithium batteries have a high energy density but low power density, meaning that although they store large amounts of energy, that energy cannot be accessed quickly. Additionally, high currents to and from the battery are a stress factor for battery degradation. A potential solution to these problems is to integrate UCs into the energy storage system. UCs store energy in the electric field of an electrochemical double layer and have a high power density but low energy, allowing them to serve as complements to battery energy storage [5]. By integrating UCs into the powertrain, it becomes possible to meet the vehicle power requirements with a smaller battery and reduce battery degradation by restricting the magnitude of the current going to or from the battery [5, 22]. Aging of UCs is primarily dependent on

New Energy Management Concepts for Hybrid and Electric Powertrains: Considering…

Current research is interested in optimal control and sizing of the UC to reduce battery aging [26], and in particular how battery aging and fuel economy are jointly impacted. Some related work includes Ref. [27], in which the authors develop an optimal control policy to govern UC behavior and demonstrate clear aging improvements over a passive (uncontrolled) system. Refs. [28, 29] carried out a parametric study on battery degradation versus UC size in EVs, using a rule-based control system to govern power allocation. Ref. [30] developed a control strategy integrating UCs with lead-acid batteries in a HEV for battery life extension, and found that a 50% increase in battery cycle life would be needed for the UC to be cost-effective. Ref. [31] experimentally demonstrated a decrease in battery power fade and temperature rise in lithium-ion batteries due to UCs on an EV load profile.

Meeting this goal of mitigating energy storage system degradation in APVs through control requires forming simplified models of the battery aging dynamics that can be included in or be used to generate an EMS. This chapter will summarize several approaches in the literature for using energy storage aging models for

Ref. [15] developed a lithium-ion battery empirical aging model for normalized battery capacity loss Q loss, based on an Arrhenius equation. The model uses experimental data to relate battery degradation to on charge throughput Ah (in ampere-

Ea, B, and z are fitted parameters, while A(Ib) is a fitted function of current. R is the ideal gas constant. Here, Q loss = 0 indicates a new battery, while Q loss = 0.2, for example, indicates a 20% decrease in the storage capacity. This model treats current and temperature as static values. So, Eq. (1) can be differentiated to form a dynamic

This model of aging has been used for aging control in, for example, [3, 4, 12].

RT � z Ah ð Þ<sup>z</sup>�<sup>1</sup> dAh

RT Ah<sup>z</sup> (1)

dt (2)

<sup>Q</sup> loss <sup>¼</sup> A Ið Þ<sup>b</sup> exp �Ea <sup>þ</sup> B Ij j <sup>b</sup>

dt <sup>¼</sup> A Ið Þ<sup>b</sup> exp �Ea <sup>þ</sup> B Ij j <sup>b</sup>

This chapter will begin with a brief review of existing literature on empirical modeling of lithium-ion battery and ultracapacitor degradation. Then, a few select aging models will be reoriented for use in an APV energy management system (EMS). Finally, an example showing how to utilize these control-oriented models will be shown.

#### 2. Energy storage aging review

#### 2.1 Lithium ion battery aging

Aging of batteries is primarily caused by the formation of substrates in the chemical reaction pathways and the formation of cracks in the electrode materials from repeated stress cycles [6]. These aging mechanisms are accelerated by high charge and discharge rates, extreme battery temperatures, and deep depths of discharge [7]. Aging of the battery causes capacity fade (a decrease in the charge storage capacity) and power fade (a decrease in the battery efficiency). However, models of the cell chemistry that include the thermal and stress/strain relationships used to describe aging are computationally intensive and are ill-suited for use in APV EMSs [6, 8].

Research of battery aging in APVs instead tends to utilize empirical models [4, 9–14]. Using empirical aging models for vehicle battery degradation analysis provides a good trade-off between precision and complexity. These empirical models do not consider the physical or chemical processes of the battery degradation but instead approximate the battery's health by fitting experimental data to aging factors like charge throughput, calendar life, and number of charge/discharge cycles.

For instance, Refs. [9, 10, 15, 16] develop aging models that relate charge throughput to degradation, with temperature and current magnitude as additional stress factors. Refs. [17, 18] include depth of discharge as an additional stress factor, while [18] also distinguishes the impact of charging and discharging currents on battery degradation. The aging models for hybrid vehicle applications in [13, 14] consider a number of charge/discharge cycles and calendar life and use temperature, depth of discharge, and average state of charge as aging stress factors. Other models in the literature such as [8, 19, 20] use simple cycle counting to measure the state of health.

Current research works to integrate battery aging dynamics into these EMSs to form controllers that actively regulate battery degradation. In Ref. [4], the authors developed an SDP-based EMS for a parallel-HEV passenger vehicle that accounted for battery wear by mapping operating conditions to substrate growth, and associating substrate growth with battery state of health. The authors also analyzed how reducing battery aging increased the fuel consumption. In Refs. [4, 21], the authors developed a deterministic EMS for a parallel-HEV passenger vehicle that regulates battery degradation using a "severity factor" map: the control policy penalizes battery usage by an amount related to the severity of the operating conditions (in terms of temperature and current magnitude). The authors of [4] also showed an inverse correlation between the battery aging and fuel consumption.

New Energy Management Concepts for Hybrid and Electric Powertrains: Considering… DOI: http://dx.doi.org/10.5772/intechopen.83770

#### 2.2 Ultracapacitor aging

the battery as much as possible to lower degradation and heat affects.

battery.

will be shown.

Energy Storage Devices

APV EMSs [6, 8].

cycles.

state of health.

18

2. Energy storage aging review

2.1 Lithium ion battery aging

Ultracapacitors (UC) can be added to vehicles to improve battery life by taking excess power away from the battery and storing it in temporary energy storage [5]. Capacitors can quickly unload power back into the system for high load situations such as a hard acceleration, taking away the need for a high-power drain from the

This chapter will begin with a brief review of existing literature on empirical modeling of lithium-ion battery and ultracapacitor degradation. Then, a few select aging models will be reoriented for use in an APV energy management system (EMS). Finally, an example showing how to utilize these control-oriented models

Aging of batteries is primarily caused by the formation of substrates in the chemical reaction pathways and the formation of cracks in the electrode materials from repeated stress cycles [6]. These aging mechanisms are accelerated by high charge and discharge rates, extreme battery temperatures, and deep depths of discharge [7]. Aging of the battery causes capacity fade (a decrease in the charge storage capacity) and power fade (a decrease in the battery efficiency). However, models of the cell chemistry that include the thermal and stress/strain relationships used to describe aging are computationally intensive and are ill-suited for use in

Research of battery aging in APVs instead tends to utilize empirical models [4, 9–14]. Using empirical aging models for vehicle battery degradation analysis provides a good trade-off between precision and complexity. These empirical models do not consider the physical or chemical processes of the battery degradation but instead approximate the battery's health by fitting experimental data to aging factors like charge throughput, calendar life, and number of charge/discharge

For instance, Refs. [9, 10, 15, 16] develop aging models that relate charge throughput to degradation, with temperature and current magnitude as additional stress factors. Refs. [17, 18] include depth of discharge as an additional stress factor, while [18] also distinguishes the impact of charging and discharging currents on battery degradation. The aging models for hybrid vehicle applications in [13, 14] consider a number of charge/discharge cycles and calendar life and use temperature, depth of discharge, and average state of charge as aging stress factors. Other models in the literature such as [8, 19, 20] use simple cycle counting to measure the

Current research works to integrate battery aging dynamics into these EMSs to form controllers that actively regulate battery degradation. In Ref. [4], the authors developed an SDP-based EMS for a parallel-HEV passenger vehicle that accounted for battery wear by mapping operating conditions to substrate growth, and associating substrate growth with battery state of health. The authors also analyzed how reducing battery aging increased the fuel consumption. In Refs. [4, 21], the authors developed a deterministic EMS for a parallel-HEV passenger vehicle that regulates battery degradation using a "severity factor" map: the control policy penalizes battery usage by an amount related to the severity of the operating conditions (in terms of temperature and current magnitude). The authors of [4] also showed an

inverse correlation between the battery aging and fuel consumption.

Lithium batteries have a high energy density but low power density, meaning that although they store large amounts of energy, that energy cannot be accessed quickly. Additionally, high currents to and from the battery are a stress factor for battery degradation. A potential solution to these problems is to integrate UCs into the energy storage system. UCs store energy in the electric field of an electrochemical double layer and have a high power density but low energy, allowing them to serve as complements to battery energy storage [5]. By integrating UCs into the powertrain, it becomes possible to meet the vehicle power requirements with a smaller battery and reduce battery degradation by restricting the magnitude of the current going to or from the battery [5, 22]. Aging of UCs is primarily dependent on time, temperature, and cell voltage [23–25].

Current research is interested in optimal control and sizing of the UC to reduce battery aging [26], and in particular how battery aging and fuel economy are jointly impacted. Some related work includes Ref. [27], in which the authors develop an optimal control policy to govern UC behavior and demonstrate clear aging improvements over a passive (uncontrolled) system. Refs. [28, 29] carried out a parametric study on battery degradation versus UC size in EVs, using a rule-based control system to govern power allocation. Ref. [30] developed a control strategy integrating UCs with lead-acid batteries in a HEV for battery life extension, and found that a 50% increase in battery cycle life would be needed for the UC to be cost-effective. Ref. [31] experimentally demonstrated a decrease in battery power fade and temperature rise in lithium-ion batteries due to UCs on an EV load profile.

#### 3. Control-oriented aging

Meeting this goal of mitigating energy storage system degradation in APVs through control requires forming simplified models of the battery aging dynamics that can be included in or be used to generate an EMS. This chapter will summarize several approaches in the literature for using energy storage aging models for control applications.

#### 3.1 Power-law model

Ref. [15] developed a lithium-ion battery empirical aging model for normalized battery capacity loss Q loss, based on an Arrhenius equation. The model uses experimental data to relate battery degradation to on charge throughput Ah (in amperehours), current Ib (in C), and temperature T (in K).

$$Q\_{loss} = A(I\_b) \exp\left(\frac{-E\_a + B|I\_b|}{RT}\right) A h^x \tag{1}$$

Ea, B, and z are fitted parameters, while A(Ib) is a fitted function of current. R is the ideal gas constant. Here, Q loss = 0 indicates a new battery, while Q loss = 0.2, for example, indicates a 20% decrease in the storage capacity. This model treats current and temperature as static values. So, Eq. (1) can be differentiated to form a dynamic aging model as follows:

$$\frac{dQ\_{\text{loss}}}{dt} = A(I\_b) \exp\left(\frac{-E\_a + B|I\_b|}{RT}\right) \cdot z(Ah)^{x-1} \frac{dAh}{dt} \tag{2}$$

This model of aging has been used for aging control in, for example, [3, 4, 12].

#### 3.2 Cycle life model

The Palmgren-Miner (PM) rule is a common method for analyzing fatigue life in mechanical systems and has been shown to effectively approximate the battery health over nonuniform charge and discharge cycles [8, 32, 33]. As per the PM rule, each charge and discharge cycle is considered to damage the battery by an amount related to the cycle life at that cycle's depth of discharge, charge and discharge current, and temperature. Ref. [18], for instance, models the cycle life of a battery as a function of depth of discharge DoD, charging current Ic, discharging current Id, and temperature T.

$$\text{CL} = f(\text{DoD}, I\_c, I\_d, T) \tag{3}$$

where θ<sup>c</sup> is the UC temperature, V is the UC voltage, and the remaining vari-

This section develops a model for a hybrid energy storage system electric vehicle (HESS-EV)—specifically, an electric bus that uses a lithium-ion battery pack for energy storage and an ultracapacitor pack for handling large power requests. This example study will be used to show how active control of aging factors can improve the lifespan of the energy storage system without compromising energy consump-

For this study, a backward-facing quasi-static vehicle model [34] is used to represent the vehicle dynamics. In this model, it is assumed that the driver accurately follows the velocity of a given drive cycle, eliminating the need for a driver model and allowing the time-history of the electrical load placed on the powertrain

This vehicle model, illustrated in Figure 3, considers inertial forces, aerodynamic drag, and rolling resistance (note that road incline is neglected for this

ρAfCDv<sup>2</sup>

<sup>v</sup> (9)

Fdrag <sup>¼</sup> <sup>1</sup> 2

model is ready to be used for control as is. Ref. [23] defines the UC end-of-life condition as similar to batteries: when the capacitance of the UC has faded by 20%.

New Energy Management Concepts for Hybrid and Electric Powertrains: Considering…

4. Case study: electric vehicle with hybrid energy storage

tion. This system is depicted in Figures 1 and 2.

<sup>c</sup> , θ0, Vref , V0, and K) are experimentally fitted parameters. This

ables (Tref

life, <sup>θ</sup>ref

DOI: http://dx.doi.org/10.5772/intechopen.83770

4.1 Vehicle dynamics

to be calculated in advance.

Figure 1. HESS-EV model.

Figure 2.

21

HESS-EV block diagram.

chapter). The drag force is given by

Assume there is a charge-discharge cycle k with operating conditions DoDk, Ic,k, Id,k, and Tk, and so the cycle life for these operating conditions is CLk. Then, under the PM rule, the damage Dk from cycle k is assumed to be

$$D\_k = \mathbf{1}/\mathbf{C}\mathbf{L}\_k\tag{4}$$

For multiple charge and discharge cycles, the damage from each cycle can be added to find the total damage Dtot. For the total damage up to cycle k,

$$D\_{\text{tot}}(k) = \sum\_{i=1}^{k} D\_i \tag{5}$$

Cumulative damage of zero denotes that the battery is unaged while cumulative damage of one means the battery has reached the end of its life. Typically, 20% capacity fade indicates a battery's end of life. So,

$$Q\_{loss}(k) = \mathbf{0}.\mathbf{2} \cdot D\_{tot}(k)\tag{6}$$

However, the above method does not readily lend itself to use in control; full charge and discharge cycles can take a long time to develop, and the EMS must act at a faster rate. One possibility is that the energy management system could consider how its decision would cause the damage of the current cycle to grow or lessen. For instance, consider a battery to be at operating conditions DoDj, Ic,j, Id,j, and Tj. The EMS then makes some decision such that the operating conditions become DoDk, Ic,k, Id,k, and Tk. Using Eqs. (3) and (4), the change in damage due to the EMS's decision can be calculated as

$$
\Delta D = D\_k - D\_j = \frac{1}{f(DoD\_k, I\_{c,k}, I\_{d,k}, T\_k)} - \frac{1}{f\left(DoD\_j, I\_{c,j}, I\_{d,j}, T\_j\right)}\tag{7}
$$

Then, Eq. (7) could be used in formulating an energy management strategy, such that the EMS would seek to minimize the additional damage caused by each decision it makes.

#### 3.3 Ultracapacitor aging

Ref. [23] provides the following model for ultracapacitor aging, where SoA is the state of aging where 0 indicates start of life and 1 indicates end of life.

$$\frac{dSoA}{dt} = \frac{1}{T\_{\text{life}}^{ref}} \cdot \exp\left(\ln\left(2\right) \frac{\theta\_{\text{\textdegree}} - \theta\_{\text{\textdegree}}^{ref}}{\theta\_0}\right) \cdot \left(\exp\left(\ln\left(2\right) \frac{V - V^{ref}}{V\_0}\right) + K\right) \tag{8}$$

New Energy Management Concepts for Hybrid and Electric Powertrains: Considering… DOI: http://dx.doi.org/10.5772/intechopen.83770

where θ<sup>c</sup> is the UC temperature, V is the UC voltage, and the remaining variables (Tref life, <sup>θ</sup>ref <sup>c</sup> , θ0, Vref , V0, and K) are experimentally fitted parameters. This model is ready to be used for control as is. Ref. [23] defines the UC end-of-life condition as similar to batteries: when the capacitance of the UC has faded by 20%.

#### 4. Case study: electric vehicle with hybrid energy storage

This section develops a model for a hybrid energy storage system electric vehicle (HESS-EV)—specifically, an electric bus that uses a lithium-ion battery pack for energy storage and an ultracapacitor pack for handling large power requests. This example study will be used to show how active control of aging factors can improve the lifespan of the energy storage system without compromising energy consumption. This system is depicted in Figures 1 and 2.

#### 4.1 Vehicle dynamics

3.2 Cycle life model

Energy Storage Devices

and temperature T.

The Palmgren-Miner (PM) rule is a common method for analyzing fatigue life in

Assume there is a charge-discharge cycle k with operating conditions DoDk, Ic,k, Id,k, and Tk, and so the cycle life for these operating conditions is CLk. Then, under

For multiple charge and discharge cycles, the damage from each cycle can be

k i¼1

Cumulative damage of zero denotes that the battery is unaged while cumulative damage of one means the battery has reached the end of its life. Typically, 20%

However, the above method does not readily lend itself to use in control; full charge and discharge cycles can take a long time to develop, and the EMS must act at a faster rate. One possibility is that the energy management system could consider how its decision would cause the damage of the current cycle to grow or lessen. For instance, consider a battery to be at operating conditions DoDj, Ic,j, Id,j, and Tj. The EMS then makes some decision such that the operating conditions become DoDk, Ic,k, Id,k, and Tk. Using Eqs. (3) and (4), the change in damage due to

f DoD ð Þ <sup>k</sup>;Ic,k;Id,k; Tk

Then, Eq. (7) could be used in formulating an energy management strategy, such that the EMS would seek to minimize the additional damage caused by each

Ref. [23] provides the following model for ultracapacitor aging, where SoA is the

state of aging where 0 indicates start of life and 1 indicates end of life.

c θ0

� exp ln 2ð Þ <sup>θ</sup><sup>c</sup> � <sup>θ</sup>ref

!

Dtotð Þ¼ k ∑

added to find the total damage Dtot. For the total damage up to cycle k,

the PM rule, the damage Dk from cycle k is assumed to be

capacity fade indicates a battery's end of life. So,

the EMS's decision can be calculated as

decision it makes.

dSoA dt <sup>¼</sup> <sup>1</sup> Tref life

20

3.3 Ultracapacitor aging

<sup>Δ</sup><sup>D</sup> <sup>¼</sup> Dk � Dj <sup>¼</sup> <sup>1</sup>

CL ¼ f DoD;Ic ð Þ ;Id; T (3)

Dk ¼ 1=CLk (4)

Q lossð Þ¼ k 0:2 � Dtotð Þk (6)

� <sup>1</sup>

� exp ln 2ð Þ <sup>V</sup> � <sup>V</sup>ref

f DoDj;Ic,j;Id,j; Tj

V<sup>0</sup>

þ K

(8)

!

!

� � (7)

Di (5)

mechanical systems and has been shown to effectively approximate the battery health over nonuniform charge and discharge cycles [8, 32, 33]. As per the PM rule, each charge and discharge cycle is considered to damage the battery by an amount related to the cycle life at that cycle's depth of discharge, charge and discharge current, and temperature. Ref. [18], for instance, models the cycle life of a battery as a function of depth of discharge DoD, charging current Ic, discharging current Id,

> For this study, a backward-facing quasi-static vehicle model [34] is used to represent the vehicle dynamics. In this model, it is assumed that the driver accurately follows the velocity of a given drive cycle, eliminating the need for a driver model and allowing the time-history of the electrical load placed on the powertrain to be calculated in advance.

This vehicle model, illustrated in Figure 3, considers inertial forces, aerodynamic drag, and rolling resistance (note that road incline is neglected for this chapter). The drag force is given by

$$F\_{drag} = \frac{1}{2} \rho A\_f C\_D v\_v^2 \tag{9}$$

Figure 2. HESS-EV block diagram.

Figure 3. Vehicle diagram.

where ρ is the air density, Af is the frontal area, CD is the drag coefficient, and vv is the vehicle velocity. Rolling resistance is given by

$$F\_{roll} = \mathbf{M}\_v \mathbf{g} \mathbf{C}\_R \tag{10}$$

4.2 Powertrain model

Vehicle physical parameters.

Table 1.

4.2.1 Transmission

losses, the motor torque is given by

τ<sup>m</sup> ¼

and the motor speed is given by

can be found in Table 1.

23

8 >>>>><

>>>>>:

in terms of the motor torque and angular velocity.

This subsection describes the modeling of the HESS-EV powertrain, including the transmission, motor, battery, and ultracapacitor subsystems, as indicated in Figure 1. The goal of the vehicle model is to capture the primary forces on the vehicle while maintaining model simplicity. Both these make simulation of the system easier and make optimal control methods, such as dynamic programming or model-predictive control, less computationally complex. Otherwise, the energy

Parameter Variable Value Vehicle mass Mv 18,181 kg Frontal area Af 8.02 m<sup>2</sup> Drag coefficient CD 0.55 Roll resistance coefficient CR 0.008 Wheel inertia Jw 20.52 kg-m<sup>2</sup> Motor inertia Jm 0.277 kg-m<sup>2</sup> Wheel radius Rw 0.48 m Final drive ratio Nfd 5.1:1 Gearbox ratio Ngb 5:1

New Energy Management Concepts for Hybrid and Electric Powertrains: Considering…

DOI: http://dx.doi.org/10.5772/intechopen.83770

Next, the vehicle speed and traction force are transformed into motor torque and motor speed. Assuming transmission efficiency of ηtrans, represented as torque

Ftraction !=ηtrans Ftraction <sup>≥</sup> <sup>0</sup>

Ftraction ! � <sup>η</sup>trans Ftraction , <sup>0</sup>

<sup>ω</sup><sup>m</sup> <sup>¼</sup> NfdNgb Rw

Then, the mechanical power needed to drive the vehicle Pmech can be expressed

Here, positive Pmech indicates acceleration. Parameter values for the transmission

(15)

vv (16)

Pmech ¼ τ<sup>m</sup> � ω<sup>m</sup> (17)

management system might suffer from the "curse of dimensionality".

Rw NfdNgb

Rw NfdNgb

where Mv is the vehicle's total mass, g is the acceleration due to gravity, and CR is the rolling resistance coefficient. In a backward-facing model, the inertial force is determined from the vehicle acceleration and the vehicle mass as

$$F\_{inertial} = \mathcal{M}\_{eq} \frac{dv\_v}{dt} \,. \tag{11}$$

Meq is the mass of the bus plus the equivalent mass due to the rotational inertia of the motor and wheels.

$$M\_{eq} = M\_v + 4f\_w \left(\frac{1}{R\_w}\right)^2 + f\_m \left(\frac{N\_{fd}N\_{gb}}{R\_w}\right)^2,\tag{12}$$

where Jw is the rotational inertia of one wheel, Jm is the rotational inertia of the motor, Rw is the wheel radius, Nfd is the final drive ratio, and Ngb is the gearbox ratio. The acceleration term in Eq. (11) is approximated from a given velocity profile according to

$$\frac{dv\_v}{dt}(t) \approx \frac{v\_v(t + \Delta t) - v\_{v(t - \Delta t)}}{2\Delta t}.\tag{13}$$

The inertial, drag, and rolling resistance forces sum together to give the traction force on the bus.

$$F\_{traction} = F\_{inertial} + F\_{drag} + F\_{roll} \tag{14}$$

Parameter values for the vehicle model can be found in Table 1. The bus is assumed to be fully loaded and at its maximum allowable weight. Vehicle parameters are estimated from existing literature on bus simulation [35–37].

New Energy Management Concepts for Hybrid and Electric Powertrains: Considering… DOI: http://dx.doi.org/10.5772/intechopen.83770


#### Table 1.

where ρ is the air density, Af is the frontal area, CD is the drag coefficient, and vv

where Mv is the vehicle's total mass, g is the acceleration due to gravity, and CR is the rolling resistance coefficient. In a backward-facing model, the inertial force is

Meq is the mass of the bus plus the equivalent mass due to the rotational inertia

where Jw is the rotational inertia of one wheel, Jm is the rotational inertia of the motor, Rw is the wheel radius, Nfd is the final drive ratio, and Ngb is the gearbox ratio. The acceleration term in Eq. (11) is approximated from a given velocity

dt ð Þ<sup>t</sup> <sup>≈</sup> vvð Þ� <sup>t</sup> <sup>þ</sup> <sup>Δ</sup><sup>t</sup> vv tð Þ �Δ<sup>t</sup>

Parameter values for the vehicle model can be found in Table 1. The bus is assumed to be fully loaded and at its maximum allowable weight. Vehicle parame-

ters are estimated from existing literature on bus simulation [35–37].

The inertial, drag, and rolling resistance forces sum together to give the traction

1 Rw <sup>2</sup> dvv

þ Jm

NfdNgb Rw <sup>2</sup>

Ftraction ¼ Finertial þ Fdrag þ Froll (14)

Finertial ¼ Meq

Froll ¼ MvgCR (10)

dt : (11)

<sup>2</sup>Δ<sup>t</sup> : (13)

, (12)

is the vehicle velocity. Rolling resistance is given by

of the motor and wheels.

Figure 3. Vehicle diagram.

Energy Storage Devices

profile according to

force on the bus.

22

determined from the vehicle acceleration and the vehicle mass as

Meq ¼ Mv þ 4Jw

dvv

Vehicle physical parameters.

#### 4.2 Powertrain model

This subsection describes the modeling of the HESS-EV powertrain, including the transmission, motor, battery, and ultracapacitor subsystems, as indicated in Figure 1. The goal of the vehicle model is to capture the primary forces on the vehicle while maintaining model simplicity. Both these make simulation of the system easier and make optimal control methods, such as dynamic programming or model-predictive control, less computationally complex. Otherwise, the energy management system might suffer from the "curse of dimensionality".

#### 4.2.1 Transmission

Next, the vehicle speed and traction force are transformed into motor torque and motor speed. Assuming transmission efficiency of ηtrans, represented as torque losses, the motor torque is given by

$$\tau\_m = \begin{cases} \left(\frac{R\_w}{N\_{fd}N\_{gb}}F\_{\text{fraction}}\right) / \eta\_{\text{trans}} & F\_{\text{fraction}} \ge \mathbf{0} \\\\ \left(\frac{R\_w}{N\_{fd}N\_{gb}}F\_{\text{fraction}}\right) \cdot \eta\_{\text{trans}} & F\_{\text{fraction}} < \mathbf{0} \end{cases} \tag{15}$$

and the motor speed is given by

$$
\rho\_m = \frac{N\_{\vec{f}d} N\_{\text{gb}}}{R\_w} \nu\_v \tag{16}
$$

Then, the mechanical power needed to drive the vehicle Pmech can be expressed in terms of the motor torque and angular velocity.

$$P\_{mech} = \tau\_m \cdot o\_m \tag{17}$$

Here, positive Pmech indicates acceleration. Parameter values for the transmission can be found in Table 1.

#### 4.2.2 Motor and power electronics

The electrical power demand of the motor, Pdem, is calculated from Pmech and an efficiency parameter ηmotor, 0 , ηmotor , 1. ηmotor is a function of τ<sup>m</sup> and ω<sup>m</sup> and is determined from a static efficiency map.

$$P\_{dem} = \begin{cases} P\_{mech} / \eta\_{trans} & \tau\_m \ge 0 \\ P\_{mech} \cdot \eta\_{trans} & \tau\_m < 0 \end{cases} \tag{18}$$

The efficiency map is obtained from the National Renewable Energy Laboratory's Advanced Vehicle Simulator (ADVISOR) data library [38] and scaled to the appropriate size using the scaling method in [5]. It includes both the motor efficiency and the efficiency of the power electronics. The modeled vehicle utilizes a 250 kW AC induction motor.

The power demand for the electric motor is provided by battery power Pbatt and ultracapacitor power Puc. As part of the quasi-static simulation, it is assumed that the power demand is always met.

$$P\_{dem} = P\_{batt} + P\_{uc} \tag{19}$$

where Nser is the number of cells in series, Npar is the number in parallel, and Rcell is the resistance of a single cell. The open circuit voltage of the vehicle battery pack

New Energy Management Concepts for Hybrid and Electric Powertrains: Considering…

Using the equivalent circuit in Figure 4, the battery terminal voltage can be

found from the OCV and battery power Pbatt as follows:

Then, substituting Eq. (23) into Eq. (24) and solving yields

<sup>2</sup> Vocv <sup>þ</sup>

cells in series was chosen so that the OCV would be in line with the

V2

VT <sup>¼</sup> <sup>1</sup>

which can be integrated to obtain the state of charge.

Vocv ¼ Nser � Vcell (22)

Ibatt ¼ Pbatt=VT (23) VT ¼ Vocv � Ibatt � Req (24)

<sup>T</sup> ¼ Vocv � VT � PbattReq (25)

Q batt

(26)

C.

, (27)

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

V2 ocv � <sup>4</sup>PbattReq � � <sup>q</sup>

VT can then be substituted back into Eq. (23) to obtain the battery current,

SOC kð Þ¼ <sup>þ</sup> <sup>1</sup> SOC kð Þþ <sup>Δ</sup><sup>t</sup> � Ibatt

where Qbatt is the capacity of the battery pack in coulombs and Δt is the

The parameters for the battery model can be found in Table 2. The number of

Parameter Variable Value Battery cells in parallel Npar 400 cells Parallel sets in series Nser 100 sets Total charge capacity Qbatt 340 Ah Battery temperature T 35<sup>∘</sup>

is likewise given by

Battery cell and battery pack equivalent circuit.

DOI: http://dx.doi.org/10.5772/intechopen.83770

Figure 5.

timestep.

Table 2. Battery parameters.

25

Because Pdem is set by the drive cycle and Puc is a controlled variable, Pbatt is fixed and dependent on both Pdem and Puc. Therefore, Eq. (19) can be rewritten as

$$P\_{batt} = P\_{dem} - P\_{uc} \tag{20}$$

#### 4.3 Energy storage systems

The previous subsections detailed how the driver's electrical power request would be determined. As depicted in Figure 4, the EMS decides how that power is split between the lithium-ion battery and the ultracapacitor. This subsection will first detail the modeling of the battery, followed by modeling of the ultracapacitor.

#### 4.3.1 Battery

This HESS-EV uses lithium-ion batteries represented by the simple battery model shown in Figure 5, where Vcell is the open-circuit voltage (OCV) of a single battery cell, while Rcell represents the combined effects of ohmic resistances, diffusion resistances, and charge-transfer resistances [5]. This quasistatic model requires only a single state variable, state of charge (SOC). The OCV as well as the resistance are considered to vary with SOC per experimental data for a lithium-ironphosphate battery [39].

The equivalent resistance of the complete battery pack is given by

$$R\_{eq} = R\_{cell} \frac{N\_{ser}}{N\_{par}} \tag{21}$$

$$\begin{array}{c} P\_{req} \xrightarrow{\begin{subarray}{c} \text{\\_CONFIG} \\ \text{\\_System} \end{subarray}} \begin{array}{c} \text{\\_Setz} \\ \text{\\_System} \end{array}} \xrightarrow{\begin{bmatrix} P\_{batt} \\ P\_{uc} \end{bmatrix}} \begin{array}{c} \text{\\_Battery & \text{\\_}} \\ \text{\\_Ultracpace} \end{array}} \xrightarrow{\begin{cases} \text{\\_SoC} \\ \text{\\_}} \begin{array}{c} \text{\\_}} \text{\\_} \\ \text{\\_}} \end{array}$$

Figure 4. Battery and UC block diagram.

New Energy Management Concepts for Hybrid and Electric Powertrains: Considering… DOI: http://dx.doi.org/10.5772/intechopen.83770

Figure 5. Battery cell and battery pack equivalent circuit.

4.2.2 Motor and power electronics

Energy Storage Devices

250 kW AC induction motor.

4.3 Energy storage systems

4.3.1 Battery

Figure 4.

24

phosphate battery [39].

Battery and UC block diagram.

the power demand is always met.

determined from a static efficiency map.

The electrical power demand of the motor, Pdem, is calculated from Pmech and an efficiency parameter ηmotor, 0 , ηmotor , 1. ηmotor is a function of τ<sup>m</sup> and ω<sup>m</sup> and is

Pdem <sup>¼</sup> Pmech=ηtrans <sup>τ</sup><sup>m</sup> <sup>≥</sup> <sup>0</sup>

The power demand for the electric motor is provided by battery power Pbatt and ultracapacitor power Puc. As part of the quasi-static simulation, it is assumed that

Because Pdem is set by the drive cycle and Puc is a controlled variable, Pbatt is fixed

and dependent on both Pdem and Puc. Therefore, Eq. (19) can be rewritten as

The previous subsections detailed how the driver's electrical power request would be determined. As depicted in Figure 4, the EMS decides how that power is split between the lithium-ion battery and the ultracapacitor. This subsection will first detail the modeling of the battery, followed by modeling of the ultracapacitor.

This HESS-EV uses lithium-ion batteries represented by the simple battery model shown in Figure 5, where Vcell is the open-circuit voltage (OCV) of a single battery cell, while Rcell represents the combined effects of ohmic resistances, diffusion resistances, and charge-transfer resistances [5]. This quasistatic model requires only a single state variable, state of charge (SOC). The OCV as well as the resistance

are considered to vary with SOC per experimental data for a lithium-iron-

The equivalent resistance of the complete battery pack is given by

Req ¼ Rcell

Nser Npar

Pdem ¼ Pbatt þ Puc (19)

Pbatt ¼ Pdem–Puc (20)

The efficiency map is obtained from the National Renewable Energy Laboratory's Advanced Vehicle Simulator (ADVISOR) data library [38] and scaled to the appropriate size using the scaling method in [5]. It includes both the motor efficiency and the efficiency of the power electronics. The modeled vehicle utilizes a

Pmech � ηtrans τ<sup>m</sup> , 0

(18)

(21)

where Nser is the number of cells in series, Npar is the number in parallel, and Rcell is the resistance of a single cell. The open circuit voltage of the vehicle battery pack is likewise given by

$$\mathbf{V}\_{ocv} = \mathbf{N}\_{scr} \cdot \mathbf{V}\_{cell} \tag{22}$$

Using the equivalent circuit in Figure 4, the battery terminal voltage can be found from the OCV and battery power Pbatt as follows:

$$I\_{\text{batt}} = \mathcal{P}\_{\text{batt}} / \mathcal{V}\_T \tag{23}$$

$$V\_T = V\_{acv} - I\_{batt} \cdot R\_{eq} \tag{24}$$

Then, substituting Eq. (23) into Eq. (24) and solving yields

$$\mathbf{V}\_T^2 = \mathbf{V}\_{ocv} \cdot \mathbf{V}\_T - P\_{\text{batt}} \mathbf{R}\_{eq} \tag{25}$$

$$\mathbf{V}\_{T} = \frac{1}{2} \left( \mathbf{V}\_{\alpha c v} + \sqrt{\mathbf{V}\_{\alpha c v}^{2} - 4 \mathbf{P}\_{batt} \mathbf{R}\_{eq}} \right) \tag{26}$$

VT can then be substituted back into Eq. (23) to obtain the battery current, which can be integrated to obtain the state of charge.

$$\text{SOC}(k+1) = \text{SOC}(k) + \Delta t \cdot \frac{I\_{\text{batt}}}{Q\_{\text{batt}}},\tag{27}$$

where Qbatt is the capacity of the battery pack in coulombs and Δt is the timestep.

The parameters for the battery model can be found in Table 2. The number of cells in series was chosen so that the OCV would be in line with the


Table 2. Battery parameters.

Figure 6.

Battery aging and dynamics block diagram.

recommendations in [40]. The number of cells in parallel was chosen so that the bus can be driven for 4 hours continuously to meet the power requirements of [40, 41].

For this example, the cycle counting method described in Eqs. (3)–(7) is used, with the aging model in Ref. [18] used to determine cycle life. This is illustrated in Figure 6. The battery is assumed to operate at a constant 35<sup>∘</sup> C.

#### 4.3.2 Ultracapacitor

The ultracapacitor model is similar in nature to the battery model, so the dynamics here will be presented more briefly. For this study, a second-order equivalent circuit based on the 100F ultracapacitor model in [42] is used to model the individual ultracapacitors. Parameters for this model are given in Table 3. Like with the battery, the ultracapacitor pack consists of ultracapacitors arranged in series and parallel as shown in Figure 7.

As shown in Figure 1, the UC is connected to the DC bus through a converter, so that the voltage of the UC pack is independent of the voltage at the DC bus. The ultracapacitor pack takes on total power Puc and has Npc cells in parallel per set and Nsc sets of cells in series. Then, the power going to each individual cell is

$$P\_{\rm nc,cell} = \frac{P\_{\rm nc}}{N\_{\rm pc}N\_{\rm sc}} \tag{28}$$

Then, substituting Eq. (23) into Eq. (24) and solving yields

New Energy Management Concepts for Hybrid and Electric Powertrains: Considering…

<sup>2</sup> <sup>q</sup>2=C<sup>2</sup> <sup>þ</sup>

T,uc ¼ q2=C<sup>2</sup> � VT,uc � PbattReq (31)

<sup>2</sup> � 4Puc,cellR<sup>2</sup>

� � (33)

quc ¼ q<sup>1</sup> þ q<sup>2</sup> (35)

� � (34)

(32)

C.

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

q2 2=C<sup>2</sup>

VT can then be substituted back into Eq. (23) to obtain the battery current.

q\_ <sup>1</sup> ¼ 1=R<sup>1</sup> q1=C<sup>1</sup> � q2=C<sup>2</sup>

q\_ <sup>2</sup> ¼ Iuc � 1=R<sup>1</sup> q1=C<sup>1</sup> � q2=C<sup>2</sup>

For this example, the cycle counting model in [23] and Section 3.3 is used. This is illustrated in Figure 8. The ultracapacitor is assumed to operate at a constant 45<sup>∘</sup>

A deterministic dynamic programming (DDP) controller is used for the EMS. DDP is a form of optimal control—in this example, the DDP controller solves the following optimization problem over a known velocity profile that is N steps long. For this system, the minimum, target, and maximum charge are qmin ¼ 50 C, qtgt ¼ 150 C, and qmax ¼ 250 C, and the minimum and maximum UC power are Puc,min ¼ �62:5W and Puc,max ¼ 62:5W so as not to exceed manufacturer specified operating conditions [42]. Note that these are constraints on each cell, not the entire

� � q

V2

VT,uc <sup>¼</sup> <sup>1</sup>

Figure 7.

Figure 8.

27

Ultracapacitor pack equivalent circuit.

DOI: http://dx.doi.org/10.5772/intechopen.83770

Then, the state equations for the two capacitors are

The total charge in the capacitor quc is given by

pack. The battery power constraints will be discussed shortly.

4.4 Energy management system

Ultracapacitor aging & dynamics block diagram.

Let q<sup>1</sup> be the charge that corresponds to the C<sup>1</sup> capacitor, and let q<sup>2</sup> be the charge that corresponds to the C<sup>2</sup> capacitor. For a given power Puc,cell, the current and terminal voltage can be found in a similar manner to Eqs. (23)–(26).

$$I\_{\rm uc} = P\_{\rm uc,cell} / \mathcal{V}\_{T,\rm uc} \tag{29}$$

$$V\_{T,uc} = q\_2 / C\_2 - I\_{uc} R\_2 \tag{30}$$


Table 3. Ultracapacitor parameters. New Energy Management Concepts for Hybrid and Electric Powertrains: Considering… DOI: http://dx.doi.org/10.5772/intechopen.83770

Figure 7. Ultracapacitor pack equivalent circuit.

recommendations in [40]. The number of cells in parallel was chosen so that the bus can be driven for 4 hours continuously to meet the power requirements of [40, 41]. For this example, the cycle counting method described in Eqs. (3)–(7) is used, with the aging model in Ref. [18] used to determine cycle life. This is illustrated in

The ultracapacitor model is similar in nature to the battery model, so the dynamics here will be presented more briefly. For this study, a second-order equivalent circuit based on the 100F ultracapacitor model in [42] is used to model the individual ultracapacitors. Parameters for this model are given in Table 3. Like with the battery, the ultracapacitor pack consists of ultracapacitors arranged in series and

Nsc sets of cells in series. Then, the power going to each individual cell is

As shown in Figure 1, the UC is connected to the DC bus through a converter, so that the voltage of the UC pack is independent of the voltage at the DC bus. The ultracapacitor pack takes on total power Puc and has Npc cells in parallel per set and

Puc,cell <sup>¼</sup> Puc

that corresponds to the C<sup>2</sup> capacitor. For a given power Puc,cell, the current and

Parameter Variable Value UC parallel cells Npc 100 UC series sets Nsc 100 Resistor 1 R1 29.6 mΩ Capacitor 1 C1 31.7 F Resistor 2 R2 14.7 mΩ Capacitor 2 C2 74.1781 F Temperature θ 45<sup>∘</sup>

terminal voltage can be found in a similar manner to Eqs. (23)–(26).

NpcNsc

Iuc ¼ Puc,cell=VT,uc (29) VT,uc ¼ q2=C<sup>2</sup> � IucR<sup>2</sup> (30)

Let q<sup>1</sup> be the charge that corresponds to the C<sup>1</sup> capacitor, and let q<sup>2</sup> be the charge

C.

(28)

C

Figure 6. The battery is assumed to operate at a constant 35<sup>∘</sup>

4.3.2 Ultracapacitor

Energy Storage Devices

Figure 6.

Table 3.

26

Ultracapacitor parameters.

parallel as shown in Figure 7.

Battery aging and dynamics block diagram.

Then, substituting Eq. (23) into Eq. (24) and solving yields

$$\mathbf{V}\_{T,uc}^{2} = \mathbf{q}\_{2}/\mathbf{C}\_{2} \cdot \mathbf{V}\_{T,uc} - P\_{\text{batt}}R\_{eq} \tag{31}$$

$$\mathbf{V}\_{T,uc} = \frac{1}{2} \left( q\_2 / \mathbf{C}\_2 + \sqrt{q\_2^2 / \mathbf{C}\_2^2 - 4 \mathbf{P}\_{uc,cell} \mathbf{R}\_2} \right) \tag{32}$$

VT can then be substituted back into Eq. (23) to obtain the battery current. Then, the state equations for the two capacitors are

$$\dot{q}\_1 = \mathbf{1}/\mathbf{R}\_1 \left( q\_1/\mathbf{C}\_1 - q\_2/\mathbf{C}\_2 \right) \tag{33}$$

$$\dot{q}\_2 = I\_{\rm nc} - \mathbf{1}/R\_1 \left( q\_1/\mathbf{C}\_1 - q\_2/\mathbf{C}\_2 \right) \tag{34}$$

The total charge in the capacitor quc is given by

$$q\_{uc} = q\_1 + q\_2 \tag{35}$$

For this example, the cycle counting model in [23] and Section 3.3 is used. This is illustrated in Figure 8. The ultracapacitor is assumed to operate at a constant 45<sup>∘</sup> C.

#### 4.4 Energy management system

A deterministic dynamic programming (DDP) controller is used for the EMS. DDP is a form of optimal control—in this example, the DDP controller solves the following optimization problem over a known velocity profile that is N steps long. For this system, the minimum, target, and maximum charge are qmin ¼ 50 C, qtgt ¼ 150 C, and qmax ¼ 250 C, and the minimum and maximum UC power are Puc,min ¼ �62:5W and Puc,max ¼ 62:5W so as not to exceed manufacturer specified operating conditions [42]. Note that these are constraints on each cell, not the entire pack. The battery power constraints will be discussed shortly.

Figure 8. Ultracapacitor aging & dynamics block diagram.

$$\begin{aligned} \text{minimize } & \sum\_{i=0}^{N} \left( q\_{uc}(i) - q\_{uc, \text{tgt}} \right)^{2} + \mathbf{Q}\_{\mathbf{1}} \cdot \left( \Delta D(i) \right)^{2} \\ \text{subject to } & q\_{min} < q\_{uc} < q\_{max} \\ & P\_{uc, \text{min}} < P\_{uc} < P\_{uc, \text{max}} \\ & P\_{batt, \text{min}} < P\_{batt} < P\_{batt, \text{max}} \end{aligned} \tag{36}$$

In plain terms, the DDP controller finds how to split power between the battery and ultracapacitor in such a way as to


The method to solve DDP problems can be found in Ref. [5]. In order to demonstrate the benefit of actively controlling aging, two versions of the controller will be tested:

1. Load-leveling DDP: Q1 is set to zero. A battery power constraint of Pbatt,min ¼ �3:2W and Pbatt,max ¼ 3:2W per cell prevents large power (and therefore large current) going to the battery, and the cost function will bring the UC charge back to the target afterwards.

turn, there are more power losses due to the internal resistances of the capacitor pack. This is illustrated in Figure 10, which shows the charge in the UC for two different values of Q1. One can see how a small penalty on Q<sup>1</sup> means the controller will focus mostly on keeping the UC charge near the target value; this in turn means

New Energy Management Concepts for Hybrid and Electric Powertrains: Considering…

DOI: http://dx.doi.org/10.5772/intechopen.83770

less current through the UC, so less power losses from the UCs.

Lifespan vs. fuel economy for HESS-EV, where Q<sup>1</sup> is the penalty on battery damage.

Figure 9.

Figure 10.

29

Ultracapacitor charge for two different values of Q1:

2. Active Aging Control: Battery power is unconstrained, but battery damage is directly penalized. A range of values are used for Q1.

#### 4.5 Simulation

The HESS-EV is simulated on the Manhattan Bus Cycle [43]—an urban bus velocity profile—for 4 hours at a time. After each simulation, the aging for the battery and ultracapacitor is measured. The battery capacity and ultracapacitor capacitances are then updated, and the next simulation begins. This process is repeated until the battery reaches its end of life.

#### 4.6 Results

The lifespan of the battery in years is estimated by measuring the total kilometers driven before the battery reached the end of its life, and then using the Federal Highway Administration's average annual kilometers driven by transit busses [44] to convert the kilometers driven into an approximate number of years. Additionally, the ultracapacitor degradation and average kWh/km for the HESS-EV over the lifespan of the battery are measured.

Figure 9 shows a comparison between battery lifespan and fuel economy for both controller types. Clearly, the aging-aware control outperforms the loadleveling type controller: In all cases, the battery with actively-controlled aging has a longer lifespan.

Additionally, this figure shows a trade-off between efficiency and battery lifespan: As the battery lifespan increases with greater penalties on battery damage, the energy efficiency of the vehicle drops. This is because greater penalties on aging cause more current to pass through UC in order to reduce the load on the battery. In New Energy Management Concepts for Hybrid and Electric Powertrains: Considering… DOI: http://dx.doi.org/10.5772/intechopen.83770

Figure 9. Lifespan vs. fuel economy for HESS-EV, where Q<sup>1</sup> is the penalty on battery damage.

turn, there are more power losses due to the internal resistances of the capacitor pack. This is illustrated in Figure 10, which shows the charge in the UC for two different values of Q1. One can see how a small penalty on Q<sup>1</sup> means the controller will focus mostly on keeping the UC charge near the target value; this in turn means less current through the UC, so less power losses from the UCs.

Figure 10. Ultracapacitor charge for two different values of Q1:

minimize ∑

and ultracapacitor in such a way as to

1. Keep the UC charge near a target value

2. Minimize the aging of the battery

bounds

Energy Storage Devices

4.5 Simulation

4.6 Results

longer lifespan.

28

the controller will be tested:

N i¼0

subject to qmin , quc , qmax

The method to solve DDP problems can be found in Ref. [5].

the UC charge back to the target afterwards.

repeated until the battery reaches its end of life.

lifespan of the battery are measured.

directly penalized. A range of values are used for Q1.

1. Load-leveling DDP: Q1 is set to zero. A battery power constraint of

qucðÞ�i quc,tgt <sup>2</sup>

Puc,min , Puc , Puc,max Pbatt,min , Pbatt , Pbatt,max

In plain terms, the DDP controller finds how to split power between the battery

3. Ensure that the UC charge, UC power, and battery power stays within given

In order to demonstrate the benefit of actively controlling aging, two versions of

Pbatt,min ¼ �3:2W and Pbatt,max ¼ 3:2W per cell prevents large power (and therefore large current) going to the battery, and the cost function will bring

2. Active Aging Control: Battery power is unconstrained, but battery damage is

The HESS-EV is simulated on the Manhattan Bus Cycle [43]—an urban bus velocity profile—for 4 hours at a time. After each simulation, the aging for the battery and ultracapacitor is measured. The battery capacity and ultracapacitor capacitances are then updated, and the next simulation begins. This process is

The lifespan of the battery in years is estimated by measuring the total kilometers driven before the battery reached the end of its life, and then using the Federal Highway Administration's average annual kilometers driven by transit busses [44] to convert the kilometers driven into an approximate number of years. Additionally, the ultracapacitor degradation and average kWh/km for the HESS-EV over the

Figure 9 shows a comparison between battery lifespan and fuel economy for both controller types. Clearly, the aging-aware control outperforms the loadleveling type controller: In all cases, the battery with actively-controlled aging has a

Additionally, this figure shows a trade-off between efficiency and battery lifespan: As the battery lifespan increases with greater penalties on battery damage, the energy efficiency of the vehicle drops. This is because greater penalties on aging cause more current to pass through UC in order to reduce the load on the battery. In

<sup>þ</sup> Q1 � ð Þ <sup>Δ</sup>D ið Þ <sup>2</sup>

(36)

Acknowledgements

DOI: http://dx.doi.org/10.5772/intechopen.83770

Conflict of interest

Author details

31

Francis Assadian\*, Kevin Mallon and Brian Walker University of California—Davis, Davis, CA, USA

provided the original work is properly cited.

\*Address all correspondence to: fassadian@ucdavis.edu

© 2019 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,

This work was supported by the University of California, Davis and by the

New Energy Management Concepts for Hybrid and Electric Powertrains: Considering…

Vertically Integrated Projects (VIP) program at UC Davis.

The authors declare no conflict of interest.

Figure 11. Battery lifespan vs. UC aging. Q<sup>1</sup> is the penalty on battery damage.

Figure 11 shows the relationship between battery degradation and ultracapacitor degradation. Two things are apparent: one, there is an inverse relationship between the two—increasing battery lifespan comes at the cost of reducing ultracapacitor lifespan. A cause of this can be observed in Figure 10. Second, ultracapacitor degradation happens much more slowly than battery degradation. Despite the battery reaching the end of its lifespan, the ultracapacitor ages no more than 6–11%.

#### 5. Conclusions

Deterioration of energy storage systems is inevitable, but by understanding the process it becomes possible to control and slow the capacity and efficiency fade. This chapter covered empirical aging models for lithium-ion and ultracapacitor systems and their use in vehicle energy management. First, existing work on different lithium ion and ultracapacitor aging models was reviewed, as well as those models' application in energy management control strategies. After reviewing aging models and discussing how to adapt empirical aging models for control, a case study was carried out on an ultracapacitor-augmented electric vehicle to show how actively controlling aging can improve an EMS. This case study included the steps necessary to model the vehicle and powertrain dynamics as well as simple or quasistatic models of the battery and ultracapacitor. DDP was generally used in two types of controllers: a load-leveling type controller that was unaware of aging dynamics, and a "smart" controller that incorporated battery aging dynamics into its design. When simulated, the aging-aware controller outperformed the simple controller, offering longer battery lifespan without any cost in fuel economy or vehicle performance. This demonstrates how advanced control—making EMSs aware of energy storage aging dynamics—can improve the efficiency and viability of alternative powertrain vehicles.

New Energy Management Concepts for Hybrid and Electric Powertrains: Considering… DOI: http://dx.doi.org/10.5772/intechopen.83770

#### Acknowledgements

This work was supported by the University of California, Davis and by the Vertically Integrated Projects (VIP) program at UC Davis.

### Conflict of interest

The authors declare no conflict of interest.

### Author details

Figure 11 shows the relationship between battery degradation and ultracapacitor degradation. Two things are apparent: one, there is an inverse relationship between the two—increasing battery lifespan comes at the cost of reducing ultracapacitor lifespan. A cause of this can be observed in Figure 10. Second, ultracapacitor degradation happens much more slowly than battery degradation. Despite the battery reaching the end of its lifespan, the ultracapacitor ages no more than 6–11%.

Battery lifespan vs. UC aging. Q<sup>1</sup> is the penalty on battery damage.

Deterioration of energy storage systems is inevitable, but by understanding the process it becomes possible to control and slow the capacity and efficiency fade. This chapter covered empirical aging models for lithium-ion and ultracapacitor systems and their use in vehicle energy management. First, existing work on different lithium ion and ultracapacitor aging models was reviewed, as well as those models' application in energy management control strategies. After reviewing aging models and discussing how to adapt empirical aging models for control, a case study was carried out on an ultracapacitor-augmented electric vehicle to show how actively controlling aging can improve an EMS. This case study included the steps necessary to model the vehicle and powertrain dynamics as well as simple or quasistatic models of the battery and ultracapacitor. DDP was generally used in two types of controllers: a load-leveling type controller that was unaware of aging dynamics, and a "smart" controller that incorporated battery aging dynamics into its design. When simulated, the aging-aware controller outperformed the simple controller, offering longer battery lifespan without any cost in fuel economy or vehicle performance. This demonstrates how advanced control—making EMSs aware of energy storage aging dynamics—can improve the efficiency and viability

5. Conclusions

Figure 11.

Energy Storage Devices

of alternative powertrain vehicles.

30

Francis Assadian\*, Kevin Mallon and Brian Walker University of California—Davis, Davis, CA, USA

\*Address all correspondence to: fassadian@ucdavis.edu

© 2019 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.

### References

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Sources. 2015;279:791-808

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A713-A720

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302-319

pp. 349-356

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Research. 1998;22:483-513

Vehicles. 2011;4:198-209

[3] Moura SJ, Stein JL, Fathy HK. Battery-health conscious power management in plug-in hybrid electric vehicles via electrochemical modeling

[4] Suri G, Onori S. A control-oriented cycle-life model for hybrid electric vehicle lithium-ion batteries. Energy.

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[6] Ramadesigan V, Northrop PWC, De S, Santhanagopalan S, Braatz RD, Subramanian VR. Modeling and

simulation of lithium-ion batteries from a systems engineering perspective. Journal of the Electrochemical Society.

[7] Pelletier S, Jabali O, Laporte G, Veneroni M. Battery degradation and behaviour for electric vehicles: Review and numerical analyses of several models. Transportation Research Part B: Methodological. 2017;103:163-164

[8] Safari M, Morcrette M, Teyssot A, Delacourt C. Life-prediction methods for lithium-ion batteries derived from a fatigue approach I. Introduction: Capacity-loss prediction based on damage accumulation. Journal of the

and stochastic control. IEEE Transactions on Control Systems Technology. 2013;21:679-694

2016;96:644-653

2012;159:R31-R45

32

[16] Spotnitz R. Simulation of capacity fade in lithium-ion batteries. Journal of Power Sources. 2003;113:72-80

[17] Sarasketa-Zabala E, Martinez-Laserna E, Berecibar M, Gandiaga I, Rodriguez-Martinez LM, Villarreal I. Realistic lifetime prediction approach for Li-ion batteries. Applied Energy. 2016;162:839-852

[18] Omar N, Monem MA, Firouz Y, Salminen J, Smekens J, Hegazy O, et al. Lithium iron phosphate based battery— Assessment of the aging parameters and development of cycle life model. Applied Energy. 2014;113:1575-1585

[19] DiOrio N, Dobos A, Janzou S, Nelson A, Lundstrom B. Technoeconomic Modeling of Battery Energy Storage in SAM. tech. rep., National Renewable Energy Laboratory (NREL) NREL/TP-6A20–64641; 2015

[20] Marano V, Onori S, Guezennec Y, Rizzoni G, Madella N. Lithium-ion batteries life estimation for plug-in hybrid electric vehicles. In: 2009 IEEE Vehicle Power and Propulsion Conference; Sept. 2009. pp. 536-543

[21] Cordoba-Arenas A, Onori S, Guezennec Y, Rizzoni G. Capacity and power fade cycle-life model for plug-in hybrid electric vehicle lithium-ion battery cells containing blended spinel and layered-oxide positive electrodes. Journal of Power Sources. 2015;278: 473-483

[22] Williamson SS. Energy Management Strategies for Electric and Plug-in Hybrid Electric Vehicles. New York, NY: Springer New York; 2013. DOI: 10.1007/978-1-4614-7711-2

[23] Kovaltchouk T, Multon B, Ahmed HB, Aubry J, Venet P. Enhanced aging model for supercapacitors taking into

account power cycling: Application to the sizing of an energy storage system in a direct wave energy converter. IEEE Transactions on Industry Applications. 2015;51:2405-2414

[24] Kovaltchouk T, Ahmed HB, Multon B, Aubry J, Venet P. An aging-aware life cycle cost comparison between supercapacitors and Li-ion batteries to smooth Direct Wave Energy Converter production. In: 2015 IEEE Eindhoven PowerTech; June 2015. pp. 1-6

[25] Hammar A, Venet P, Lallemand R, Coquery G, Rojat G. Study of accelerated aging of supercapacitors for transport applications. IEEE Transactions on Industrial Electronics. 2010;57:3972-3979

[26] Miller JM, Sartorelli G. Battery and ultracapacitor combinations #x2014; Where should the converter go? In: 2010 IEEE Vehicle Power and Propulsion Conference; 2010. pp. 1–7

[27] Song Z, Li J, Han X, Xu L, Lu L, Ouyang M, et al. Multi-objective optimization of a semi-active battery/ supercapacitor energy storage system for electric vehicles. Applied Energy. 2014;135:212-224

[28] Shen J, Dusmez S, Khaligh A. Optimization of sizing and battery cycle life in battery/ultracapacitor hybrid energy storage systems for electric vehicle applications. IEEE Transactions on Industrial Informatics. 2014;10: 2112-2121

[29] Akar A, Tavlasoglu Y, Vural B. An energy management strategy for a concept battery/ultracapacitor electric vehicle with improved battery life. IEEE Transactions on Transportation Electrification. 2017;3:191-200

[30] Carter R, Cruden A, Hall PJ. Optimizing for efficiency or battery life in a battery/supercapacitor electric

vehicle. IEEE Transactions on Vehicular Technology. 2012;61:1526-1533

[31] Zhao C, Yin H, Ma C. Quantitative evaluation of LiFePO battery cycle life improvement using ultracapacitors. IEEE Transactions on Power Electronics. 2016;31:3989-3993

[32] Zhou C, Qian K, Allan M, Zhou W. Modeling of the cost of EV battery wear due to V2G Application 621 in power systems. IEEE Transactions on Energy Conversion. 2011;26:1041-1050

[33] Mallon KR, Assadian F, Fu B. Analysis of on-board photovoltaics for a battery electric bus and their impact on battery lifespan. Energies. 2017;10:943

[34] Mohan G, Assadian F, Longo S. Comparative analysis of forward-facing models vs backwardfacing models in powertrain component sizing. In: IET Hybrid and Electric Vehicles Conference 2013 (HEVC 2013); 2013. pp. 1-6

[35] Zeng X, Yang N, Wang J, Song D, Zhang N, Shang M, et al. Predictivemodel-based dynamic coordination control strategy for power-split hybrid electric bus. Mechanical Systems and Signal Processing. Aug. 2015;6061: 785-798

[36] Sangtarash F, Esfahanian V, Nehzati H, Haddadi S, Bavanpour MA, Haghpanah B. Effect of different regenerative braking strategies on braking performance and fuel economy in a hybrid electric bus employing CRUISE vehicle simulation. SAE International Journal of Fuels and Lubricants. 2008;1(1):828-837

[37] Wang BH, Luo YG, Zhang JW. Simulation of city bus performance based on actual urban driving cycle in China. International Journal of Automotive Technology. 2008;9(4): 501-507

[38] Markel T, Brooker A, Hendricks T, Johnson V, Kelly K, Kramer B, et al. ADVISOR: A systems analysis tool for advanced vehicle modeling. Journal of Power Sources. 2002;110(2):255-266

[39] Erdinc O, Vural B, Uzunoglu M. A dynamic lithium-ion battery model considering the effects of temperature and capacity fading. In: 2009 International Conference on Clean Electrical Power, ICCEP; 2009. pp. 383-386. DOI: 10.1109/ ICCEP.2009.5212025

[40] Nelson RF. Power requirements for batteries in hybrid electric vehicles. Journal of Power Sources. 2000;91(1): 2-26

[41] Fauvel C, Napal V, Rousseau A. Medium and heavy duty hybrid electric vehicle sizing to maximize fuel consumption displacement on real world drive cycles. Los Angeles, California: Power (W); 2012. pp. 0-22. Available from: https://www.autonomie. net/docs/5%20-% 20Presentations/ Heavy%20duty/CF%20-%20EVS26.pdf

Section 2

Control of Energy Storage

Systems

35

[42] Dougal RA, Gao L, Liu S. Ultracapacitor model with automatic order selection and capacity scaling for dynamic system simulation. Journal of Power Sources. 2004;126:250-257

[43] Barlow TJ, Latham S, Mccrae IS, Boulter PG. A Reference Book of Driving Cycles for Use in the Measurement of Road Vehicle Emissions. TRL Published Project Report; 2009. Available from: https:// trid.trb.org/view/909274

[44] FHWA (Federal Highway Administration). Highway Statistics 2013; 2013

### Section 2

## Control of Energy Storage Systems

vehicle. IEEE Transactions on Vehicular

[38] Markel T, Brooker A, Hendricks T, Johnson V, Kelly K, Kramer B, et al. ADVISOR: A systems analysis tool for advanced vehicle modeling. Journal of Power Sources. 2002;110(2):255-266

[39] Erdinc O, Vural B, Uzunoglu M. A dynamic lithium-ion battery model considering the effects of temperature

[40] Nelson RF. Power requirements for batteries in hybrid electric vehicles. Journal of Power Sources. 2000;91(1):

[41] Fauvel C, Napal V, Rousseau A. Medium and heavy duty hybrid electric

vehicle sizing to maximize fuel consumption displacement on real world drive cycles. Los Angeles, California: Power (W); 2012. pp. 0-22. Available from: https://www.autonomie. net/docs/5%20-% 20Presentations/ Heavy%20duty/CF%20-%20EVS26.pdf

[42] Dougal RA, Gao L, Liu S.

trid.trb.org/view/909274

2013; 2013

[44] FHWA (Federal Highway Administration). Highway Statistics

Ultracapacitor model with automatic order selection and capacity scaling for dynamic system simulation. Journal of Power Sources. 2004;126:250-257

[43] Barlow TJ, Latham S, Mccrae IS, Boulter PG. A Reference Book of Driving Cycles for Use in the Measurement of Road Vehicle Emissions. TRL Published Project Report; 2009. Available from: https://

and capacity fading. In: 2009 International Conference on Clean Electrical Power, ICCEP; 2009. pp. 383-386. DOI: 10.1109/ ICCEP.2009.5212025

2-26

[31] Zhao C, Yin H, Ma C. Quantitative evaluation of LiFePO battery cycle life improvement using ultracapacitors. IEEE Transactions on Power Electronics. 2016;31:3989-3993

[32] Zhou C, Qian K, Allan M, Zhou W. Modeling of the cost of EV battery wear due to V2G Application 621 in power systems. IEEE Transactions on Energy Conversion. 2011;26:1041-1050

[33] Mallon KR, Assadian F, Fu B. Analysis of on-board photovoltaics for a battery electric bus and their impact on battery lifespan. Energies. 2017;10:943

[34] Mohan G, Assadian F, Longo S. Comparative analysis of forward-facing models vs backwardfacing models in powertrain component sizing. In: IET

Conference 2013 (HEVC 2013); 2013.

[35] Zeng X, Yang N, Wang J, Song D, Zhang N, Shang M, et al. Predictivemodel-based dynamic coordination control strategy for power-split hybrid electric bus. Mechanical Systems and Signal Processing. Aug. 2015;6061:

[36] Sangtarash F, Esfahanian V, Nehzati

H, Haddadi S, Bavanpour MA, Haghpanah B. Effect of different regenerative braking strategies on braking performance and fuel economy in a hybrid electric bus employing CRUISE vehicle simulation. SAE International Journal of Fuels and Lubricants. 2008;1(1):828-837

[37] Wang BH, Luo YG, Zhang JW. Simulation of city bus performance based on actual urban driving cycle in China. International Journal of Automotive Technology. 2008;9(4):

Hybrid and Electric Vehicles

pp. 1-6

785-798

501-507

34

Technology. 2012;61:1526-1533

Energy Storage Devices

Chapter 3

Abstract

1. Introduction

37

Mahmoud Elsisi

Storage Devices

Control Mechanisms of Energy

The fast acting due to the salient features of energy storage systems leads to using of it in the control applications in power system. The energy storage systems such as superconducting magnetic energy storage (SMES), capacitive energy storage (CES), and the battery of plug-in hybrid electric vehicle (PHEV) can storage the energy and contribute the active power and reactive power with the power system to extinguish the rapid change in load demands and the renewable energy sources (RES). This chapter gives an overview about the modeling of energy storage devices

Keywords: energy storage devices, superconducting magnetic energy storage (SMES), capacitive energy storage (CES), plug-in hybrid electric vehicle (PHEV)

With the increasing of distributed generator (DG) technologies, large numbers of DGs are connected with the grid in different forms, such as wind and solar power systems [1–3]. Because of the fluctuations of their output power, energy storage devices are utilized to adjust steady outputs [4, 5]. In fact, the characteristics of the different storage devices vary widely, including the amount of energy stored and the time for which this stored energy is required to be retained or released [6, 7]. The superconducting magnetic energy storage (SMES), superconducting capacitive energy storage (CES), and the battery of plug-in hybrid electric vehicle (PHEV) are able to achieve the highest possible power densities. Each storage energy device has a different model. Several control approaches are applied to control the energy storage devices. In [8, 9], model predictive control (MPC) is presented for residential energy systems with photovoltaic (PV) system and batteries. Model predictive control predicts the load and the generation over a certain time horizon into the future and finds the optimum schedule of the battery over that period which can minimize a desired objective. In [10], a voltage regulation in distribution feeders is proposed using residential energy storage units. The control method is carried out by making the charging and discharging rates of the batteries a function in the voltage at the point of common coupling. A fuzzy logic based control method of battery state of charge (SOC) is presented in [11]. This control method regulates the battery SOC at expected conditions, and consequently the energy capacity of BESS can be small. In [12], a state-of-charge feedback control technique is used to keep the charging level of the battery within its proper range while the battery energy storage system make the output fluctuation of a wind farm smooth. The optimal

and methods of control in them to adjust steady outputs.

#### Chapter 3

## Control Mechanisms of Energy Storage Devices

Mahmoud Elsisi

#### Abstract

The fast acting due to the salient features of energy storage systems leads to using of it in the control applications in power system. The energy storage systems such as superconducting magnetic energy storage (SMES), capacitive energy storage (CES), and the battery of plug-in hybrid electric vehicle (PHEV) can storage the energy and contribute the active power and reactive power with the power system to extinguish the rapid change in load demands and the renewable energy sources (RES). This chapter gives an overview about the modeling of energy storage devices and methods of control in them to adjust steady outputs.

Keywords: energy storage devices, superconducting magnetic energy storage (SMES), capacitive energy storage (CES), plug-in hybrid electric vehicle (PHEV)

#### 1. Introduction

With the increasing of distributed generator (DG) technologies, large numbers of DGs are connected with the grid in different forms, such as wind and solar power systems [1–3]. Because of the fluctuations of their output power, energy storage devices are utilized to adjust steady outputs [4, 5]. In fact, the characteristics of the different storage devices vary widely, including the amount of energy stored and the time for which this stored energy is required to be retained or released [6, 7]. The superconducting magnetic energy storage (SMES), superconducting capacitive energy storage (CES), and the battery of plug-in hybrid electric vehicle (PHEV) are able to achieve the highest possible power densities. Each storage energy device has a different model. Several control approaches are applied to control the energy storage devices. In [8, 9], model predictive control (MPC) is presented for residential energy systems with photovoltaic (PV) system and batteries. Model predictive control predicts the load and the generation over a certain time horizon into the future and finds the optimum schedule of the battery over that period which can minimize a desired objective. In [10], a voltage regulation in distribution feeders is proposed using residential energy storage units. The control method is carried out by making the charging and discharging rates of the batteries a function in the voltage at the point of common coupling. A fuzzy logic based control method of battery state of charge (SOC) is presented in [11]. This control method regulates the battery SOC at expected conditions, and consequently the energy capacity of BESS can be small. In [12], a state-of-charge feedback control technique is used to keep the charging level of the battery within its proper range while the battery energy storage system make the output fluctuation of a wind farm smooth. The optimal

design of MPC with SMES based on the bat-inspired algorithm (BIA) is introduced for load frequency control in [13]. This work is extended to include the MPC with SMES and CES in [14]. Decentralized MPC with PHEVs is utilized for frequency regulation in a smart three-area interconnected power system in [15].

#### 2. Superconducting magnetic energy storage

The SMES units are used to compensate the load increments by the injection of a real power to the system and diminished the load decrements by the absorbing of the excess real power via large superconducting inductor [16–18]. Figure 1a show a schematic diagram of SMES unit consists of superconducting inductor (L), Y-Y/Δ transformer, and controlled ac/dc bridge converter with 12-pulse thyristor. A power conversion system (PCS) is used to connect the superconducting inductor with the AC grid. The PCS is a dual-mode converter and it works as a rectifier or as an inverter in the charging and discharging modes of the inductor respectively. Obviously, the mode of operation is detected according to the nature of load perturbation. The charging phase represents the rectifier mode. In the rectifier mode, adjusted positive voltage is applied across the terminals of the inductor. Alternatively, the discharging phase represents the inverter mode. In the inverter mode, adjusted negative voltage is applied across the terminals of the inductor. The controlling in the thyristor firing angle is used to switch either rectifier or inverter modes, the converter output voltage is expressed in kV and it is given in following equation [19]:

$$E\_d = \mathfrak{Z}E\_o \cos \left( a \right) - \mathfrak{Z}I\_d R\_c \tag{1}$$

where Ed is the inductor DC voltage (kV); Eo is the converter open circuit voltage (kV); α is the thyristor firing angle (degrees); Id is the inductor current

According to the rectifier or inverter modes, the polarity of the voltage Ed is adjusted while the direction of inductor current Id does not change. As mention in the above section, the regulation of the thyristor firing angle is used to controlling the direction and magnitude of the inductor power Pd. Initially, a small positive voltage is applied to charge the inductor to its rated current according to the desired charging period of the SMES unit. The inductor voltage is reduced to zero and the inductor current reach to its rated value because the coil is superconducting. When the inductor current reached to its rated value, the SMES unit can be coupled to the power system. The error signal Δe represent the input to control the SMES voltage Ed. This error signal may be the change of system frequency, the change of system voltage, or the change of system current according to the control object. The incre-

mental voltage and current changes of the SMES coil are given as follows:

<sup>Δ</sup>Ed <sup>¼</sup> Ko

<sup>Δ</sup>Id <sup>¼</sup> <sup>1</sup> sL

<sup>Δ</sup>Ed <sup>¼</sup> <sup>1</sup>

the load disturbance is defined as follows:

back of the current deviation is shown in Figure 1b.

Pd ¼ ð Þ Edo þ ΔEd ð Þ Ido þ ΔId

computed as follows:

39

1 þ sTdc

form:

1 þ sTdc

where Tdc is the converter time constant in sec, Ko is the gain of the proportional controller in kV/Hz and s is the Laplace operator. As reported in [19], the inductor current reaches to its nominal value very slowly in the SMES unit. So, the fast rate of the current to restore its rated value is required to extinguish the next load perturbation fastly. Therefore, a negative feedback signal is used in the SMES control loop to provide fast current recovery. Thus, Eq. 2 is rewritten in following

where KIdis the negative feedback gain of the current deviation (kV/kA). In the storage mode, the coil is short-circuited, i.e. Edo ¼ 0 and there is no power transfer. So in either phase (charging/discharging), the power is defined by Pd ¼ EdId and the initial inductor power is Pdo ¼ EdoIdo, where Edo and Ido are the voltage and current magnitudes previous to load disturbance. The inductor power following to

¼ EdoIdo þ EdoΔId þ IdoΔEd þ ΔEdΔId ¼ Pdo þ EdoΔId þ IdoΔEd þ ΔEdΔId, Edo ¼ 0

Therefore, the real power incremental change ΔPd of the SMES unit in MW is

The corresponding block diagram of an SMES incorporating the negative feed-

ΔPd ¼ Pd � Pdo ¼ IdoΔEd þ ΔIdΔEd (6)

ð Þ Δe (2)

ΔEd (3)

ð Þ KoΔe � KIdΔId (4)

(5)

(kA); RC is the equivalent resistance of commutation (ohm).

Control Mechanisms of Energy Storage Devices DOI: http://dx.doi.org/10.5772/intechopen.82327

2.1 Modeling of superconducting magnetic energy storage

Figure 1. The SMES unit (a) circuit diagram and (b) corresponding block diagram.

#### Control Mechanisms of Energy Storage Devices DOI: http://dx.doi.org/10.5772/intechopen.82327

design of MPC with SMES based on the bat-inspired algorithm (BIA) is introduced for load frequency control in [13]. This work is extended to include the MPC with SMES and CES in [14]. Decentralized MPC with PHEVs is utilized for frequency

The SMES units are used to compensate the load increments by the injection of a real power to the system and diminished the load decrements by the absorbing of the excess real power via large superconducting inductor [16–18]. Figure 1a show a schematic diagram of SMES unit consists of superconducting inductor (L), Y-Y/Δ transformer, and controlled ac/dc bridge converter with 12-pulse thyristor. A power conversion system (PCS) is used to connect the superconducting inductor with the AC grid. The PCS is a dual-mode converter and it works as a rectifier or as an inverter in the charging and discharging modes of the inductor respectively. Obviously, the mode of operation is detected according to the nature of load perturbation. The charging phase represents the rectifier mode. In the rectifier mode, adjusted positive voltage is applied across the terminals of the inductor. Alternatively, the discharging phase represents the inverter mode. In the inverter mode, adjusted negative voltage is applied across the terminals of the inductor. The controlling in the thyristor firing angle is used to switch either rectifier or inverter modes, the converter

regulation in a smart three-area interconnected power system in [15].

output voltage is expressed in kV and it is given in following equation [19]:

Ed ¼ 2Eo cosð Þ� α 2IdRc (1)

2. Superconducting magnetic energy storage

Energy Storage Devices

Figure 1.

38

The SMES unit (a) circuit diagram and (b) corresponding block diagram.

where Ed is the inductor DC voltage (kV); Eo is the converter open circuit voltage (kV); α is the thyristor firing angle (degrees); Id is the inductor current (kA); RC is the equivalent resistance of commutation (ohm).

#### 2.1 Modeling of superconducting magnetic energy storage

According to the rectifier or inverter modes, the polarity of the voltage Ed is adjusted while the direction of inductor current Id does not change. As mention in the above section, the regulation of the thyristor firing angle is used to controlling the direction and magnitude of the inductor power Pd. Initially, a small positive voltage is applied to charge the inductor to its rated current according to the desired charging period of the SMES unit. The inductor voltage is reduced to zero and the inductor current reach to its rated value because the coil is superconducting. When the inductor current reached to its rated value, the SMES unit can be coupled to the power system. The error signal Δe represent the input to control the SMES voltage Ed. This error signal may be the change of system frequency, the change of system voltage, or the change of system current according to the control object. The incremental voltage and current changes of the SMES coil are given as follows:

$$
\Delta E\_d = \frac{K\_o}{1 + sT\_{dc}} \left(\Delta e\right) \tag{2}
$$

$$
\Delta I\_d = \frac{1}{sL} \Delta E\_d \tag{3}
$$

where Tdc is the converter time constant in sec, Ko is the gain of the proportional controller in kV/Hz and s is the Laplace operator. As reported in [19], the inductor current reaches to its nominal value very slowly in the SMES unit. So, the fast rate of the current to restore its rated value is required to extinguish the next load perturbation fastly. Therefore, a negative feedback signal is used in the SMES control loop to provide fast current recovery. Thus, Eq. 2 is rewritten in following form:

$$
\Delta E\_d = \frac{1}{1 + sT\_{dc}} \left( K\_o \Delta e - K\_{Id} \Delta I\_d \right) \tag{4}
$$

where KIdis the negative feedback gain of the current deviation (kV/kA). In the storage mode, the coil is short-circuited, i.e. Edo ¼ 0 and there is no power transfer. So in either phase (charging/discharging), the power is defined by Pd ¼ EdId and the initial inductor power is Pdo ¼ EdoIdo, where Edo and Ido are the voltage and current magnitudes previous to load disturbance. The inductor power following to the load disturbance is defined as follows:

$$\begin{aligned} P\_d &= (E\_{do} + \Delta E\_d)(I\_{do} + \Delta I\_d) \\ &= E\_{do}I\_{do} + E\_{do}\Delta I\_d + I\_{do}\Delta E\_d + \Delta E\_d \Delta I\_d = P\_{do} + E\_{do}\Delta I\_d + I\_{do}\Delta E\_d + \Delta E\_d \Delta I\_d, \ E\_{do} = 0 \end{aligned} \tag{5}$$

Therefore, the real power incremental change ΔPd of the SMES unit in MW is computed as follows:

$$
\Delta P\_d = P\_d - P\_{do} = I\_{do} \Delta E\_d + \Delta I\_d \Delta E\_d \tag{6}
$$

The corresponding block diagram of an SMES incorporating the negative feedback of the current deviation is shown in Figure 1b.

<sup>Δ</sup>Id <sup>¼</sup> Kc

where Kc is the proportional controller in kA/Hz.

Control Mechanisms of Energy Storage Devices DOI: http://dx.doi.org/10.5772/intechopen.82327

<sup>Δ</sup>Id <sup>¼</sup> <sup>1</sup>

The CES unit (a) circuit diagram and (b) corresponding block diagram.

The modes of switches during the charging and discharging of CES unit.

S1, S4 ON OFF S2, S3 OFF ON

Charging mode Discharging mode

1 þ sTdc

where KEd is the negative feedback gain of the capacitor voltage deviation (kA/kV). In the storage mode, the capacitor represents an open circuit, i.e. Ido ¼ 0

following form:

Figure 3.

Table 1.

41

1 þ sTdc

As stated in [20], the CES voltage reaches to its nominal value very slowly. So, the fast rate of the capacitor voltage to restore its rated value is required to extinguish the next load perturbation fastly. Therefore, a negative feedback signal is used in the CES control loop to provide a fast voltage recovery. Thus, Eq. 7 is rewritten in

ð Þ Δe (7)

ð Þ KcΔf � KEdΔEd (8)

Figure 2. The block diagram of SMES with controller.

Setting the parameters (L, Ko, KId and Ido) of the SMES unit to their optimistic values can enhance its role in achieving well-damped to the responses. Herein, the application of artificial intelligence (AI) techniques is suggested to search for the optimal parameters of the SMES and controller simultaneously.

#### 2.2 Control techniques of SMES

Modern control techniques such as adaptive control, fuzzy logic control, and model predictive control (MPC) can be applied to control the charging and discharging of the SMES instead of the proportional controller as shown in Figure 2. The controller and SMES parameters must be adjusted by proper optimization technique such as genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC),…etc. to give a good performance.

#### 3. Capacitive energy storage

The capacitive energy storage (CES) has an important role to stabilize the power system against to the sudden change in load demand. The static operation of the CES makes its response faster than of the mechanical systems [20–23]. Parallel storage capacitors form the CES. Figure 3a shows a schematic diagram of a CES unit connected with the AC grid by a PCS. The capacitor bank dielectric and leakage losses are defined by the resistance (R). When the load demand decreases, the capacitor charges up to its rated full value, thus releases an amount of the excess energy in the system. Contrary, the capacitor discharges to its initial value of voltage when the load demand rises suddenly and release the stored energy fastly to the grid through the PCS. A gate turn-off (GTO) thyristors is used as switches to control the direction of the capacitor current during the charging and discharging as shown in Table 1.

The controlling in the thyristor firing angle is used to switch either rectifier or inverter modes of CES to adjust the capacitor voltage as defined in Eq. 1.

#### 3.1 Modeling of superconducting magnetic energy storage

The CES unit is ready to be coupled to the power system for LFC when the rated voltage across the capacitor is attained. The current Id of CES is controlled by sensing the error signal Δe. This error signal may be the change of system frequency, the change of system voltage, or the change of system current according to the control object. The incremental current changes of the CES are given as follows:

Control Mechanisms of Energy Storage Devices DOI: http://dx.doi.org/10.5772/intechopen.82327

$$
\Delta I\_d = \frac{K\_c}{\mathbf{1} + sT\_{dc}} (\Delta e) \tag{7}
$$

where Kc is the proportional controller in kA/Hz.

As stated in [20], the CES voltage reaches to its nominal value very slowly. So, the fast rate of the capacitor voltage to restore its rated value is required to extinguish the next load perturbation fastly. Therefore, a negative feedback signal is used in the CES control loop to provide a fast voltage recovery. Thus, Eq. 7 is rewritten in following form:

$$
\Delta I\_d = \frac{1}{1 + \mathfrak{s}T\_{dc}} (K\_c \Delta \! \! \! / - K\_{Ed} \! \! \! \! \! \! / E\_d) \tag{8}
$$

where KEd is the negative feedback gain of the capacitor voltage deviation (kA/kV). In the storage mode, the capacitor represents an open circuit, i.e. Ido ¼ 0

Figure 3.

Setting the parameters (L, Ko, KId and Ido) of the SMES unit to their optimistic values can enhance its role in achieving well-damped to the responses. Herein, the application of artificial intelligence (AI) techniques is suggested to search for the

Modern control techniques such as adaptive control, fuzzy logic control, and

discharging of the SMES instead of the proportional controller as shown in Figure 2. The controller and SMES parameters must be adjusted by proper optimization technique such as genetic algorithm (GA), particle swarm optimization (PSO), and

The capacitive energy storage (CES) has an important role to stabilize the power system against to the sudden change in load demand. The static operation of the CES makes its response faster than of the mechanical systems [20–23]. Parallel storage capacitors form the CES. Figure 3a shows a schematic diagram of a CES unit connected with the AC grid by a PCS. The capacitor bank dielectric and leakage losses are defined by the resistance (R). When the load demand decreases, the capacitor charges up to its rated full value, thus releases an amount of the excess energy in the system. Contrary, the capacitor discharges to its initial value of voltage when the load demand rises suddenly and release the stored energy fastly to the grid through the PCS. A gate turn-off (GTO) thyristors is used as switches to control the direction of the capacitor current during the charging and discharging as

The controlling in the thyristor firing angle is used to switch either rectifier or

The CES unit is ready to be coupled to the power system for LFC when the rated voltage across the capacitor is attained. The current Id of CES is controlled by sensing the error signal Δe. This error signal may be the change of system frequency, the change of system voltage, or the change of system current according to the control object. The incremental current changes of the CES are given as

inverter modes of CES to adjust the capacitor voltage as defined in Eq. 1.

3.1 Modeling of superconducting magnetic energy storage

model predictive control (MPC) can be applied to control the charging and

optimal parameters of the SMES and controller simultaneously.

artificial bee colony (ABC),…etc. to give a good performance.

2.2 Control techniques of SMES

The block diagram of SMES with controller.

Figure 2.

Energy Storage Devices

3. Capacitive energy storage

shown in Table 1.

follows:

40

The CES unit (a) circuit diagram and (b) corresponding block diagram.


#### Table 1.

The modes of switches during the charging and discharging of CES unit.

Figure 4. The block diagram of CES with controller.

and no power transfer. Hence, the power is defined by PCS ¼ EdId and the initial CES power is PCSo ¼ EdoIdo, where Edo and Ido are the magnitudes of the voltage and current prior to load disturbance. Following a load disturbance, the power flow into the CES is given as follows:

$$\begin{split} P\_{\rm CS} &= (E\_{do} + \Delta E\_d)(I\_{do} + \Delta I\_d) \\ &= E\_{do}I\_{do} + E\_{do}\Delta I\_d + I\_{do}\Delta E\_d + \Delta E\_d \Delta I\_d = P\_{\rm CSo} + E\_{do}\Delta I\_d + I\_{do}\Delta E\_d + \Delta E\_d \Delta I\_d, \; I\_{do} = 0 \end{split} \tag{9}$$

Thus, the real power incremental change ΔPCS of the CES unit in MW is computed as follows:

$$
\Delta P\_{\rm CS} = P\_{\rm CS} - P\_{\rm CSo} = E\_{do}\Delta I\_d + \Delta E\_d \Delta I\_d \tag{10}
$$

grid. The change of PHEV output power is adjusted by the control signal (U)

PHEV model: (a) PHEV with controller block diagram, (b) change of PHEV output power against control

Ui, Ui j j≤ΔPmax ΔPmax, Ui>ΔPmax �ΔPmax, Ui < � ΔPmax

In this chapter, classifications of energy storage devices and control strategy for storage devices by adjusting the performance of different devices and features of the power imbalance are presented. The modeling of each storage energy devices is discussed. Furthermore, the control method for each one are cleared. These energy storage devices with modern control techniques such as adaptive control, fuzzy logic control, and model predictive control (MPC) can be applied to extinguish the

(11)

according to the limit range of output power deviation of PHEV as

8 ><

>:

ΔPPHEV ¼

rapid change in load demands and the fluctuations of RES.

where Pmax is the maximum PHEV power.

Control Mechanisms of Energy Storage Devices DOI: http://dx.doi.org/10.5772/intechopen.82327

5. Conclusion

43

Figure 5.

signal.

The corresponding block diagram of a CES unit incorporating the negative feedback of the voltage deviation is shown in Figure 3b.

Setting the parameters (C, Kc, KEd and Edo) of the CES unit to their optimistic values can enhance its role in achieving well-damped to the responses. Herein, the application of artificial intelligence (AI) techniques is suggested to search for the optimal parameters of the CES and controller simultaneously.

#### 3.2 Control techniques of CES

Modern control techniques such as adaptive control, fuzzy logic control, and MPC can be applied to control the charging and discharging of the CES instead of the proportional controller as shown in Figure 4. The controller and CES parameters must be adjusted by proper optimization technique such as GA, PSO, and ABC, …etc. to give a good performance.

#### 4. Plug-in hybrid electric vehicle model

The PHEV model is represented as first-order transfers function with very small time constant TPHEV as shown in Figure 5(a) [24–27]. The change of PHEV output power ΔPPHEV for charging or discharging is selected according to the control signal Ui of the controller. In this chapter, the control signal is determined by modern control techniques such as adaptive control, fuzzy logic control, and model predictive control (MPC). The control signal depends on the error signal to adjust the charging or discharging of PHEVs batteries. Figure 5(b) shows a bi-directional PHEV to charging and discharging power control 'vehicle to grid (V2G)'. According to the change of error, this V2G release a power to the grid or drain power from the Control Mechanisms of Energy Storage Devices DOI: http://dx.doi.org/10.5772/intechopen.82327

Figure 5. PHEV model: (a) PHEV with controller block diagram, (b) change of PHEV output power against control signal.

grid. The change of PHEV output power is adjusted by the control signal (U) according to the limit range of output power deviation of PHEV as

$$
\Delta P\_{PHEV} = \begin{cases}
 U\_{i\cdot} |U\_i| \le \Delta P\_{\max} \\
 \Delta P\_{\max}, \ U\_{i\cdot} \Delta P\_{\max} \\
 - \Delta P\_{\max}, \ U\_i < -\Delta P\_{\max}
\end{cases} \tag{11}
$$

where Pmax is the maximum PHEV power.

#### 5. Conclusion

and no power transfer. Hence, the power is defined by PCS ¼ EdId and the initial CES power is PCSo ¼ EdoIdo, where Edo and Ido are the magnitudes of the voltage and current prior to load disturbance. Following a load disturbance, the power flow into

¼ EdoIdo þ EdoΔId þ IdoΔEd þ ΔEdΔId ¼ PCSo þ EdoΔId þ IdoΔEd þ ΔEdΔId, Ido ¼ 0

Thus, the real power incremental change ΔPCS of the CES unit in MW is com-

The corresponding block diagram of a CES unit incorporating the negative

Setting the parameters (C, Kc, KEd and Edo) of the CES unit to their optimistic values can enhance its role in achieving well-damped to the responses. Herein, the application of artificial intelligence (AI) techniques is suggested to search for the

Modern control techniques such as adaptive control, fuzzy logic control, and MPC can be applied to control the charging and discharging of the CES instead of the proportional controller as shown in Figure 4. The controller and CES parameters must be adjusted by proper optimization technique such as GA, PSO, and ABC,

The PHEV model is represented as first-order transfers function with very small time constant TPHEV as shown in Figure 5(a) [24–27]. The change of PHEV output power ΔPPHEV for charging or discharging is selected according to the control signal Ui of the controller. In this chapter, the control signal is determined by modern control techniques such as adaptive control, fuzzy logic control, and model predictive control (MPC). The control signal depends on the error signal to adjust the charging or discharging of PHEVs batteries. Figure 5(b) shows a bi-directional PHEV to charging and discharging power control 'vehicle to grid (V2G)'. According to the change of error, this V2G release a power to the grid or drain power from the

feedback of the voltage deviation is shown in Figure 3b.

optimal parameters of the CES and controller simultaneously.

ΔPCS ¼ PCS � PCSo ¼ EdoΔId þ ΔEdΔId (10)

(9)

the CES is given as follows:

puted as follows:

Figure 4.

Energy Storage Devices

PCS ¼ ð Þ Edo þ ΔEd ð Þ Ido þ ΔId

The block diagram of CES with controller.

3.2 Control techniques of CES

…etc. to give a good performance.

42

4. Plug-in hybrid electric vehicle model

In this chapter, classifications of energy storage devices and control strategy for storage devices by adjusting the performance of different devices and features of the power imbalance are presented. The modeling of each storage energy devices is discussed. Furthermore, the control method for each one are cleared. These energy storage devices with modern control techniques such as adaptive control, fuzzy logic control, and model predictive control (MPC) can be applied to extinguish the rapid change in load demands and the fluctuations of RES.

Energy Storage Devices

### Author details

Mahmoud Elsisi Electrical Power and Machines Department, Faculty of Engineering (Shoubra), Benha University, Cairo, Egypt

References

2515-2523

677-687

16(4):2154-2171

[1] Heide D, Greiner M, Von Bremen L, Hoffmann C. Reduced storage and balancing needs in a fully renewable European power system with excess wind and solar power generation. Renewable Energy. 2011;36(9):

Control Mechanisms of Energy Storage Devices DOI: http://dx.doi.org/10.5772/intechopen.82327

Ambient Intelligence and Humanized

[8] Schreiber M, Hochloff P. Capacitydependent tariffs and residential energy management for photovoltaic storage systems. In: Power and Energy Society General Meeting (PES), IEEE. 2013.

[9] Worthmann K, Kellett CM, Braun P, Grüne L, Weller SR. Distributed and decentralized control of residential energy systems incorporating battery storage. IEEE Transactions on Smart

Computing. 2013;4:663-671

Grid. 2015;6(4):1914-1923

2016. pp. 1-6

pp. 2723-2726

[10] Wang Y, Wang BF, Son PL. A voltage regulation method using

distributed energy storage systems in lv distribution networks. In: Energy Conference (ENERGYCON), IEEE.

[11] Li X, Hui D, Wu L, Lai X. Control strategy of battery state of charge for wind/battery hybrid power system. In: IEEE International Symposium on Industrial Electronics (ISIE). 2010.

[12] Yoshimoto K, Nanahara T, Koshimizu G, Uchida Y. New control method for regulating state-of-charge of a battery in hybrid wind power/battery energy storage system. In: Power Systems Conference and Exposition, IEEE PES. 2006. pp. 1244-1251

[13] Elsisi M, Soliman M, Aboelela MAS, Mansour W. Optimal design of model predictive control with superconducting

[14] Elsisi M, Soliman M, Aboelela MAS,

magnetic energy storage for load frequency control of nonlinear hydrothermal power system using bat inspired algorithm. Journal of Energy

Mansour W. Improving the grid frequency by optimal design of model

Storage. 2017;12:311-318

pp. 1-5

[2] Yamegueu D, Azoumah Y, Py X, Kottin H. Experimental analysis of a solar PV/diesel hybrid system without storage: Focus on its dynamic behavior. International Journal of Electrical Power & Energy Systems. 2013;44(1):267-274

[3] Datta M, Senjyu T, Yona A, Funabashi T. Frequency control of photovoltaic–diesel hybrid system connecting to isolated power utility by using load estimator and energy storage system. IEEJ Transactions on Electrical and Electronic Engineering. 2010;5(6):

[4] Dıaz-Gonzalez F, Sumper A, Gomis-Bellmunt O, Villafafila-Robles R. A review of energy storage technologies for wind power applications. Renewable and Sustainable Energy Reviews. 2012;

[5] Bandara K, Sweet T, Ekanayake J. Photovoltaic applications for offgrid electrification using novel multi-level inverter technology with energy storage. Renewable Energy. 2012;37(1):82-88

[6] Zhou Z, Benbouzid M, Fredéric Charpentier J, Scuiller F, Tang T. A review of energy storage technologies for marine current energy systems. Renewable and Sustainable Energy

[7] Di Fazio A, Erseghe T, Ghiani E, Murroni M, Siano P, Silvestro F. Integration of renewable energy sources, energy storage systems, and electrical vehicles with smart power distribution networks. Journal of

Reviews. 2013;18:390-400

45

\*Address all correspondence to: mahmoud.elsesy@feng.bu.edu.eg

© 2019 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.

Control Mechanisms of Energy Storage Devices DOI: http://dx.doi.org/10.5772/intechopen.82327

#### References

[1] Heide D, Greiner M, Von Bremen L, Hoffmann C. Reduced storage and balancing needs in a fully renewable European power system with excess wind and solar power generation. Renewable Energy. 2011;36(9): 2515-2523

[2] Yamegueu D, Azoumah Y, Py X, Kottin H. Experimental analysis of a solar PV/diesel hybrid system without storage: Focus on its dynamic behavior. International Journal of Electrical Power & Energy Systems. 2013;44(1):267-274

[3] Datta M, Senjyu T, Yona A, Funabashi T. Frequency control of photovoltaic–diesel hybrid system connecting to isolated power utility by using load estimator and energy storage system. IEEJ Transactions on Electrical and Electronic Engineering. 2010;5(6): 677-687

[4] Dıaz-Gonzalez F, Sumper A, Gomis-Bellmunt O, Villafafila-Robles R. A review of energy storage technologies for wind power applications. Renewable and Sustainable Energy Reviews. 2012; 16(4):2154-2171

[5] Bandara K, Sweet T, Ekanayake J. Photovoltaic applications for offgrid electrification using novel multi-level inverter technology with energy storage. Renewable Energy. 2012;37(1):82-88

[6] Zhou Z, Benbouzid M, Fredéric Charpentier J, Scuiller F, Tang T. A review of energy storage technologies for marine current energy systems. Renewable and Sustainable Energy Reviews. 2013;18:390-400

[7] Di Fazio A, Erseghe T, Ghiani E, Murroni M, Siano P, Silvestro F. Integration of renewable energy sources, energy storage systems, and electrical vehicles with smart power distribution networks. Journal of

Ambient Intelligence and Humanized Computing. 2013;4:663-671

[8] Schreiber M, Hochloff P. Capacitydependent tariffs and residential energy management for photovoltaic storage systems. In: Power and Energy Society General Meeting (PES), IEEE. 2013. pp. 1-5

[9] Worthmann K, Kellett CM, Braun P, Grüne L, Weller SR. Distributed and decentralized control of residential energy systems incorporating battery storage. IEEE Transactions on Smart Grid. 2015;6(4):1914-1923

[10] Wang Y, Wang BF, Son PL. A voltage regulation method using distributed energy storage systems in lv distribution networks. In: Energy Conference (ENERGYCON), IEEE. 2016. pp. 1-6

[11] Li X, Hui D, Wu L, Lai X. Control strategy of battery state of charge for wind/battery hybrid power system. In: IEEE International Symposium on Industrial Electronics (ISIE). 2010. pp. 2723-2726

[12] Yoshimoto K, Nanahara T, Koshimizu G, Uchida Y. New control method for regulating state-of-charge of a battery in hybrid wind power/battery energy storage system. In: Power Systems Conference and Exposition, IEEE PES. 2006. pp. 1244-1251

[13] Elsisi M, Soliman M, Aboelela MAS, Mansour W. Optimal design of model predictive control with superconducting magnetic energy storage for load frequency control of nonlinear hydrothermal power system using bat inspired algorithm. Journal of Energy Storage. 2017;12:311-318

[14] Elsisi M, Soliman M, Aboelela MAS, Mansour W. Improving the grid frequency by optimal design of model

Author details

Energy Storage Devices

Mahmoud Elsisi

44

Benha University, Cairo, Egypt

provided the original work is properly cited.

Electrical Power and Machines Department, Faculty of Engineering (Shoubra),

© 2019 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,

\*Address all correspondence to: mahmoud.elsesy@feng.bu.edu.eg

predictive control with energy storage devices. Optimal Control Applications and Methods. 2018;39(1):263-280

[15] Elsisi M, Soliman M, Aboelela MAS, Mansour W. Model predictive control of plug-in hybrid electric vehicles for frequency regulation in a smart grid. IET Generation, Transmission & Distribution. 2017;11(16):3974-3983

[16] Ali MH, Wu B, Dougal RA. An overview of SMES applications in power and energy systems. IEEE Transactions on Sustainable Energy. 2010;1(1):38-47

[17] Bhatt P, Roy R, Ghoshal SP. Comparative performance evaluation of SMES–SMES, TCPS–SMES and SSSC– SMES controllers in automatic generation control for a two-area hydro–hydro system. International Journal of Electrical Power & Energy Systems. 2011;33(10):1585-1597

[18] Banerjee S, Chatterjee JK, Tripathy SC. Application of magnetic energy storage unit as load-frequency stabilizer. IEEE Transactions on Energy Conversion. 1990;5(1):46-51

[19] Tripathy SC, Balasubramanian R, Chandramohanan Nair PS. Adaptive automatic generation control with superconducting magnetic energy storage in power systems. IEEE Transactions on Energy Conversion. 1992;7(3):434-441

[20] Tripathy SC. Improved loadfrequency control with capacitive energy storage. Energy Conversion and Management. 1997;38(6):551-562

[21] Abraham RJ, Das D, Patra A. Automatic generation control of an interconnected power system with capacitive energy storage. International Journal of Electrical Power and Energy Systems Engineering. 2010;3(1):351-356

[22] Sanki P, Ray S, Shukla RR, Das D. Effect of different controllers and

capacitive energy storage on two area interconnected power system model using Matlab Simulink. In: 2014 First International Conference on Automation, Control, Energy and Systems (ACES). pp. 1-6

Chapter 4

Abstract

Modeling of a Contact-Less

Station Fed from On-Grid

Photovoltic Arrays

obtained from the Matlab software package.

three-level inverter

1. Introduction

compared with ICEs.

47

Keywords: contactless power transfer, inductive power transfer (IPT), air-core power transformer (ACPT), electric vehicle (EV), photovoltaic (PV),

Several research works have been introduced to contribute toward introducing an environmentally friendly power source to replace the conventional internal combustion engine (ICE). Electric vehicles (EVs) and fuel cell electric vehicles (FCEVs) expected to be replaced soon. It is known that the EVs have higher efficiency for the energy conversion, capability for regenerative braking and motoring, minimum emission for exhaust gases, and low noise and vibration when

Essamudin Ali Ebrahim

Electric-Vehicle Battery-Charging

Electric vehicles (EVs) are environmental friendly due to no exhaust gases or carbon dioxide. In addition, there is no noise through operation. However, up to now, there are some challenges that facing its spread through all over the world. The main problem that these vehicles face is the fast charging process of the used batteries through neat and clean source without plugs. So, this chapter deals with a proposed method for a contactless battery charger of both electric and hybrid electric vehicles (HEVs) from renewable resources. The chapter proposes a public station for fast charging. This station implies off-board battery charger fed from on-grid (OG) photovoltaic (PV) arrays through inductively power transfer (IPT). This comfortable 100 kW contactless power station is designed, modeled, and simulated as a general software package reliable to be used for any other station design. The air gap of the air-core transformer (ACT) divides the station into two parts. The first part implies roof-mounted PV array with its intelligent-controlled maximum power point tracking (MPPT) technique, power converter, three-level power inverter, and resonant compensator converter that operates at high frequency. The second one includes rectifiers and switched mode power converter with smart controller. The chapter includes samples for simulation results that are

[23] Dhundhara S, Verma YP. Evaluation of CES and DFIG unit in AGC of realistic multisource deregulated power system. International Transactions on Electrical Energy Systems. 2016:1-14

[24] Vachirasricirikul S, Ngamroo I. Robust LFC in a smart grid with wind power penetration by coordinated V2G control and frequency controller. IEEE Transactions on Smart Grid. 2014;5(1): 371-380

[25] Pahasa J, Ngamroo I. PHEVs bidirectional charging/discharging and SoC control for microgrid frequency stabilization using multiple MPC. IEEE Transactions on Smart Grid. 2015;6(2): 526-533

[26] Pahasa J, Ngamroo I. Coordinated control of wind turbine blade pitch angle and PHEVs using MPCs for load frequency control of microgrid. IEEE Systems Journal. 2016;10(1):97-105

[27] Pahasa J, Ngamroo I. Simultaneous control of frequency fluctuation and battery SOC in a smart grid using LFC and EV controllers based on optimal MIMO-MPC. Journal of Electrical Engineering & Technology. 2017;12(2): 601-611

#### Chapter 4

predictive control with energy storage devices. Optimal Control Applications and Methods. 2018;39(1):263-280

Energy Storage Devices

capacitive energy storage on two area interconnected power system model using Matlab Simulink. In: 2014 First

[23] Dhundhara S, Verma YP. Evaluation

of CES and DFIG unit in AGC of realistic multisource deregulated power system. International Transactions on Electrical Energy Systems. 2016:1-14

[24] Vachirasricirikul S, Ngamroo I. Robust LFC in a smart grid with wind power penetration by coordinated V2G control and frequency controller. IEEE Transactions on Smart Grid. 2014;5(1):

[25] Pahasa J, Ngamroo I. PHEVs bidirectional charging/discharging and SoC control for microgrid frequency stabilization using multiple MPC. IEEE Transactions on Smart Grid. 2015;6(2):

[26] Pahasa J, Ngamroo I. Coordinated control of wind turbine blade pitch angle and PHEVs using MPCs for load frequency control of microgrid. IEEE Systems Journal. 2016;10(1):97-105

[27] Pahasa J, Ngamroo I. Simultaneous control of frequency fluctuation and battery SOC in a smart grid using LFC and EV controllers based on optimal MIMO-MPC. Journal of Electrical Engineering & Technology. 2017;12(2):

International Conference on Automation, Control, Energy and

Systems (ACES). pp. 1-6

371-380

526-533

601-611

[15] Elsisi M, Soliman M, Aboelela MAS, Mansour W. Model predictive control of plug-in hybrid electric vehicles for frequency regulation in a smart grid. IET Generation, Transmission & Distribution. 2017;11(16):3974-3983

[16] Ali MH, Wu B, Dougal RA. An overview of SMES applications in power and energy systems. IEEE Transactions on Sustainable Energy. 2010;1(1):38-47

[17] Bhatt P, Roy R, Ghoshal SP.

SMES controllers in automatic generation control for a two-area hydro–hydro system. International Journal of Electrical Power & Energy Systems. 2011;33(10):1585-1597

IEEE Transactions on Energy Conversion. 1990;5(1):46-51

1992;7(3):434-441

46

Comparative performance evaluation of SMES–SMES, TCPS–SMES and SSSC–

[18] Banerjee S, Chatterjee JK, Tripathy SC. Application of magnetic energy storage unit as load-frequency stabilizer.

[19] Tripathy SC, Balasubramanian R, Chandramohanan Nair PS. Adaptive automatic generation control with superconducting magnetic energy storage in power systems. IEEE Transactions on Energy Conversion.

[20] Tripathy SC. Improved loadfrequency control with capacitive energy storage. Energy Conversion and Management. 1997;38(6):551-562

[21] Abraham RJ, Das D, Patra A. Automatic generation control of an interconnected power system with capacitive energy storage. International Journal of Electrical Power and Energy Systems Engineering. 2010;3(1):351-356

[22] Sanki P, Ray S, Shukla RR, Das D. Effect of different controllers and

## Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid Photovoltic Arrays

Essamudin Ali Ebrahim

### Abstract

Electric vehicles (EVs) are environmental friendly due to no exhaust gases or carbon dioxide. In addition, there is no noise through operation. However, up to now, there are some challenges that facing its spread through all over the world. The main problem that these vehicles face is the fast charging process of the used batteries through neat and clean source without plugs. So, this chapter deals with a proposed method for a contactless battery charger of both electric and hybrid electric vehicles (HEVs) from renewable resources. The chapter proposes a public station for fast charging. This station implies off-board battery charger fed from on-grid (OG) photovoltaic (PV) arrays through inductively power transfer (IPT). This comfortable 100 kW contactless power station is designed, modeled, and simulated as a general software package reliable to be used for any other station design. The air gap of the air-core transformer (ACT) divides the station into two parts. The first part implies roof-mounted PV array with its intelligent-controlled maximum power point tracking (MPPT) technique, power converter, three-level power inverter, and resonant compensator converter that operates at high frequency. The second one includes rectifiers and switched mode power converter with smart controller. The chapter includes samples for simulation results that are obtained from the Matlab software package.

Keywords: contactless power transfer, inductive power transfer (IPT), air-core power transformer (ACPT), electric vehicle (EV), photovoltaic (PV), three-level inverter

#### 1. Introduction

Several research works have been introduced to contribute toward introducing an environmentally friendly power source to replace the conventional internal combustion engine (ICE). Electric vehicles (EVs) and fuel cell electric vehicles (FCEVs) expected to be replaced soon. It is known that the EVs have higher efficiency for the energy conversion, capability for regenerative braking and motoring, minimum emission for exhaust gases, and low noise and vibration when compared with ICEs.

The host battery, its total cost, and the development of its performance play an important role in the electric vehicle (EV) and hybrid EV [1]. The battery charging process needs several considerations such as fastness, safety, easy, and low-priced cost.

There are two techniques for charging process: plug-in and un-plugged but connected. Out-door plug-in needs to have a large cable that creates power losses, inconvenience, and electric-chock hazards for most owners. But, on the other hand, inductively coupled power transfer (ICPT) contactless-charging system introduces an imperious solution for the risk and embarrassment problems through transferring the charging energy over a softly inductive coupler [2–4].

Through the last decade, several aspects of ICPT have been studied, such as magnetic coupler design techniques, compensation topologies, control methods, foreign object detection algorithms, and the radiation safety issues [5–7]. Also, the main power supply for the station prefers to be renewable and friend to the environment. High-rating good quality and low-priced photovoltaic (PV) cells – as a renewable energy supply – are nowadays available and suitable to this purpose [8]. Several efforts for progressing the ICPT charging technique are done as described by Miskovski and Williamson [9]. The authors suggested that a 100-kW public solar power station – with battery banks – for partial charging (30%) only and 10% of the required power is supplied from the PV array. The main disadvantages of that system are that it uses a battery bank with bulky and expensive elements for the public-charging station. Also, in Ref. [10], the author presented a charge controller of solar photovoltaic panel (SPV) fed battery. But in [11], Robalino et al. suggested a solar energized docking charging with fuel cell for electric vehicle. The study proposed an alternative method for manufactures and consumers to use the clean sources for transportation commitments. This study has limited theoretical guides for the designers to model the station.

> The average capacity for most batteries used in electric vehicles is around 20 kWh. The parts of the station are classified according to the position that is placed. Static part is the part that placed on land or under ground in the station. But, the dynamic part is that placed inside the EV as on-board battery charging. Each part of the station will be explained and designed in detail in the following sections.

Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid…

The primary-side part consists of the modeled PV panels in arrays as a neatrenewable energy source with its controller for the maximum power point tracking (MPPT) that implies one boost converter, a bi-directional three-level inverter for interfacing with the main grid and the PV arrays, and resonant converter with very high frequency including a series compensator, and all are connected with the

The PV cell, module, and arrays mathematical models can be obtained by

q Vð Þ <sup>þ</sup>IRs ð Þ nkT � <sup>1</sup> h i � <sup>V</sup> <sup>þ</sup> IRs

Also, the total current of the module can be computed from the following

Rp

(1)

air-core transformer (ACT) through its primary winding.

3.1 Modeling of the PV array with MPPT algorithm

Essamudin [8] according to the following equations: The total current of the cell is computed as follows:

I ¼ Iirr � Io e

3. The primary-side part

The proposed public station for PV battery charging.

DOI: http://dx.doi.org/10.5772/intechopen.89530

Figure 1.

equation:

49

However, this chapter introduces a study that came to provide an integrated model for a solar-powered electric vehicle charging station connected to the main grid. This chapter introduces a complete modeling and designing for a public station of electric vehicle battery charging with a wireless technique. This technique depends on inductively coupled power transfer (ICPT) by using air-core transformer (ACT) to overcome the problems of contacts and wiring due to high-rating charging current. The proposed station is fed from a renewable power resource such as PV arrays connected with the main grid. The study includes the design for the PV array with its maximum power point tracking (MPPT) and its mathematical model. Also, this chapter introduces a Matlab/m-file software package that includes a model for boost converter required for interconnecting between PV array and load with its smart control. In addition, the package introduces a complete design and model for three-level inverter interconnecting between the PV and the main grid. This chapter also proposes an air-core transformer with light weight and a few number of turns for inductively coupling. The proposed system utilizes a buck converter for battery charger to control the charging current with an intelligent PItuning controller. In addition, a high-frequency resonant converter with series compensation is modeled and simulated through this study. The validity of the system is verified by charging four 19-kW lithium-ion batteries of Honda EV in order to test the capacity and efficiency of the station.

#### 2. The proposed ICPT-PV for EV public-charging station

The proposed public-charging station consists of two main parts (as shown in Figure 1): the static and dynamic sections. The rated power of the station is 100 kW, and it depends on the types and capacity of the EV batteries to be charged. Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid… DOI: http://dx.doi.org/10.5772/intechopen.89530

Figure 1. The proposed public station for PV battery charging.

The average capacity for most batteries used in electric vehicles is around 20 kWh. The parts of the station are classified according to the position that is placed. Static part is the part that placed on land or under ground in the station. But, the dynamic part is that placed inside the EV as on-board battery charging. Each part of the station will be explained and designed in detail in the following sections.

#### 3. The primary-side part

The primary-side part consists of the modeled PV panels in arrays as a neatrenewable energy source with its controller for the maximum power point tracking (MPPT) that implies one boost converter, a bi-directional three-level inverter for interfacing with the main grid and the PV arrays, and resonant converter with very high frequency including a series compensator, and all are connected with the air-core transformer (ACT) through its primary winding.

#### 3.1 Modeling of the PV array with MPPT algorithm

The PV cell, module, and arrays mathematical models can be obtained by Essamudin [8] according to the following equations:

The total current of the cell is computed as follows:

$$I = I\_{irr} - I\_o \left[ e^{\left(\frac{q(V+lR\_t)}{nkT}\right)} - 1 \right] - \frac{V+IR\_t}{R\_p} \tag{1}$$

Also, the total current of the module can be computed from the following equation:

The host battery, its total cost, and the development of its performance play an important role in the electric vehicle (EV) and hybrid EV [1]. The battery charging process needs several considerations such as fastness, safety, easy, and low-priced cost. There are two techniques for charging process: plug-in and un-plugged but connected. Out-door plug-in needs to have a large cable that creates power losses, inconvenience, and electric-chock hazards for most owners. But, on the other hand, inductively coupled power transfer (ICPT) contactless-charging system introduces an imperious solution for the risk and embarrassment problems through transfer-

Through the last decade, several aspects of ICPT have been studied, such as magnetic coupler design techniques, compensation topologies, control methods, foreign object detection algorithms, and the radiation safety issues [5–7]. Also, the main power supply for the station prefers to be renewable and friend to the environment. High-rating good quality and low-priced photovoltaic (PV) cells – as a renewable energy supply – are nowadays available and suitable to this purpose [8]. Several efforts for progressing the ICPT charging technique are done as described by Miskovski and Williamson [9]. The authors suggested that a 100-kW public solar power station – with battery banks – for partial charging (30%) only and 10% of the required power is supplied from the PV array. The main disadvantages of that system are that it uses a battery bank with bulky and expensive elements for the public-charging station. Also, in Ref. [10], the author presented a charge controller of solar photovoltaic panel (SPV) fed battery. But in [11], Robalino et al. suggested a solar energized docking charging with fuel cell for electric vehicle. The study proposed an alternative method for manufactures and consumers to use the clean sources for transportation commitments. This study has limited theoretical guides

However, this chapter introduces a study that came to provide an integrated model for a solar-powered electric vehicle charging station connected to the main grid. This chapter introduces a complete modeling and designing for a public station of electric vehicle battery charging with a wireless technique. This technique depends on inductively coupled power transfer (ICPT) by using air-core transformer (ACT) to overcome the problems of contacts and wiring due to high-rating charging current. The proposed station is fed from a renewable power resource such as PV arrays connected with the main grid. The study includes the design for the PV array with its maximum power point tracking (MPPT) and its mathematical model. Also, this chapter introduces a Matlab/m-file software package that includes a model for boost converter required for interconnecting between PV array and load with its smart control. In addition, the package introduces a complete design and model for three-level inverter interconnecting between the PV and the main grid. This chapter also proposes an air-core transformer with light weight and a few number of turns for inductively coupling. The proposed system utilizes a buck converter for battery charger to control the charging current with an intelligent PItuning controller. In addition, a high-frequency resonant converter with series compensation is modeled and simulated through this study. The validity of the system is verified by charging four 19-kW lithium-ion batteries of Honda EV in

ring the charging energy over a softly inductive coupler [2–4].

for the designers to model the station.

Energy Storage Devices

order to test the capacity and efficiency of the station.

48

2. The proposed ICPT-PV for EV public-charging station

The proposed public-charging station consists of two main parts (as shown in

100 kW, and it depends on the types and capacity of the EV batteries to be charged.

Figure 1): the static and dynamic sections. The rated power of the station is

$$I\_M = I\_{irr} - I\_o \left[ e^{\left(\frac{q\left(V\_M + I\_M N\_l R\_l\right)}{N\_r nkT}\right)} - 1 \right] - \frac{V\_M + I\_M N\_s R\_s}{N\_s R\_p} \tag{2}$$

Then, the total PV array current is given by the following criteria:

$$I\_A = N\_p I\_{irr} - I\_o \left[ e^{\left(\frac{V\_A + I\_A \frac{N\_o}{N\_p} \ell\_i}{N\_o \pi T}\right)} - 1 \right] - \frac{V\_A + I\_A \left(\frac{N}{N\_p}\right) R\_s}{\frac{N\_s}{N\_p} R\_p} \tag{3}$$

where I,IM,IA are the cell, module, and array currents (A); Iirr is the irradiance or photo current (A); Io is the diode saturation current (A); V,VM,VA are the cell, module, and array voltages (V); <sup>q</sup> <sup>¼</sup> <sup>1</sup>:<sup>6</sup> � <sup>10</sup>�<sup>19</sup> (C); <sup>K</sup> <sup>¼</sup> Boltizmann constant <sup>¼</sup> 1.3806503�10�23J/K; Rs, Rp are series and parallel resistance (Ω); Ns, Np are series and parallel cell numbers; and T is the cell temperature (°K).

The proposed station is designed to work as on-grid with 100-kW power rating supplied from the PV source in the day light (not using battery banks as storage elements), and it will be supplied from the main grid in the evening and cloud days. So, the following lines show the modeling and designing for the number of PV modules with arrays to generate the power needed.

The rated power for the station is 100 Kw, and the rated output voltage for the PV array is 300 V, if it is supposed that the SunPower module (SPR-305) types [12–14] are used with their characteristics: voltage for open circuit (VocÞ is 64.2 V, current for short circuit (IscÞ is 5.96 A, maximum power voltage and current VMPP is 54.7 V and IMPP is 5.58 A, <sup>∴</sup>Ns <sup>¼</sup> <sup>300</sup> 64:2 � � <sup>¼</sup> <sup>4</sup>:<sup>6</sup> ffi 5 modules, then the DC output terminal voltage of the PV array will be 320 V.

If the maximum power for one module equals (54.7�5.58)ffi 305 W, then the total required number of modules can be obtained as <sup>100000</sup> <sup>305</sup> � � <sup>¼</sup> 327 modules = Ns � Np; dependently, if the total numbers of the parallel modules are calculated as (327/5) ffi 66 modules, then the total required modules for the proposed system are (66�5 ¼ 330Þ, and the maximum power will be equal to PMPP=(330�305)/ 1000 = 100.65 kW.

When the dimension of one proposed module is 1.559m � <sup>1</sup>:046m <sup>¼</sup> <sup>1</sup>:63m2 [14], then, the total surface area required is equal (1.63�330ffi538m<sup>2</sup> ). This area can be saved on the station roof and used to arrange the modules in rows and columns. However, the characteristic curves of one module and one array for the V-I and V-P curves are illustrated in Figure 2 (a and b), respectively.

#### 3.2 Maximum power point tracking (MPPT) algorithm with boost converter

There are several methods proposed to achieve MPPT [15, 16]. The simplest method is the incremental conductance (IC) that depends on the base that the maximum power point is obtained when the power slope of the PV is zero that obtained from the derivative of the power due to the voltage and equating it to zero (i.e., dP/dV = 0). Figure 2a and b describes this and explores the characteristic curves of both power and current with the PV terminal voltage of one module and one array. As shown, the left side of the curve is positive and negative in the right. Due to this condition, the MPP can be found in terms of the increment in the array conductance. In Eq. (4), if the value of the error in the right hand side equal zero, this means that the change of current due to voltage equal to negative value of the

Figure 2.

51

V-I and V-P characteristics for (a) one module and (b) the PV array.

Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid…

DOI: http://dx.doi.org/10.5772/intechopen.89530

Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid… DOI: http://dx.doi.org/10.5772/intechopen.89530

Figure 2. V-I and V-P characteristics for (a) one module and (b) the PV array.

IM ¼ Iirr � Io e

Energy Storage Devices

IA ¼ NpIirr � Io e

q Vð Þ <sup>M</sup>þIMNsRs NsnkT � �

Then, the total PV array current is given by the following criteria:

0 @

and parallel cell numbers; and T is the cell temperature (°K).

modules with arrays to generate the power needed.

54.7 V and IMPP is 5.58 A, <sup>∴</sup>Ns <sup>¼</sup> <sup>300</sup>

1000 = 100.65 kW.

50

terminal voltage of the PV array will be 320 V.

q VAþIA <sup>N</sup> Np Rs � � NsnkT

� �

� 1

1 A

where I,IM,IA are the cell, module, and array currents (A); Iirr is the irradiance or photo current (A); Io is the diode saturation current (A); V,VM,VA are the cell, module, and array voltages (V); <sup>q</sup> <sup>¼</sup> <sup>1</sup>:<sup>6</sup> � <sup>10</sup>�<sup>19</sup> (C); <sup>K</sup> <sup>¼</sup> Boltizmann constant <sup>¼</sup> 1.3806503�10�23J/K; Rs, Rp are series and parallel resistance (Ω); Ns, Np are series

The proposed station is designed to work as on-grid with 100-kW power rating supplied from the PV source in the day light (not using battery banks as storage elements), and it will be supplied from the main grid in the evening and cloud days. So, the following lines show the modeling and designing for the number of PV

The rated power for the station is 100 Kw, and the rated output voltage for the PV array is 300 V, if it is supposed that the SunPower module (SPR-305) types [12–14] are used with their characteristics: voltage for open circuit (VocÞ is 64.2 V, current for short circuit (IscÞ is 5.96 A, maximum power voltage and current VMPP is

If the maximum power for one module equals (54.7�5.58)ffi 305 W, then the

Ns � Np; dependently, if the total numbers of the parallel modules are calculated as (327/5) ffi 66 modules, then the total required modules for the proposed system are

When the dimension of one proposed module is 1.559m � <sup>1</sup>:046m <sup>¼</sup> <sup>1</sup>:63m2

be saved on the station roof and used to arrange the modules in rows and columns. However, the characteristic curves of one module and one array for the V-I and V-P

3.2 Maximum power point tracking (MPPT) algorithm with boost converter

There are several methods proposed to achieve MPPT [15, 16]. The simplest method is the incremental conductance (IC) that depends on the base that the maximum power point is obtained when the power slope of the PV is zero that obtained from the derivative of the power due to the voltage and equating it to zero (i.e., dP/dV = 0). Figure 2a and b describes this and explores the characteristic curves of both power and current with the PV terminal voltage of one module and one array. As shown, the left side of the curve is positive and negative in the right. Due to this condition, the MPP can be found in terms of the increment in the array conductance. In Eq. (4), if the value of the error in the right hand side equal zero, this means that the change of current due to voltage equal to negative value of the

(66�5 ¼ 330Þ, and the maximum power will be equal to PMPP=(330�305)/

[14], then, the total surface area required is equal (1.63�330ffi538m<sup>2</sup>

� � <sup>¼</sup> <sup>4</sup>:<sup>6</sup> ffi 5 modules, then the DC output

305

� � <sup>¼</sup> 327 modules =

). This area can

64:2

total required number of modules can be obtained as <sup>100000</sup>

curves are illustrated in Figure 2 (a and b), respectively.

� 1

� VM <sup>þ</sup> IMNsRs NsRp

VA þ IA <sup>N</sup>

Ns Np Rp

Np � � Rs (2)

(3)

division of current by voltage. The quotient of this division equal to the incremental conductance (IC).

$$\frac{dP\_A}{dV\_A} = \frac{d(V\_A \, I\_A)}{dV\_A} = I\_A + V\_A \frac{dI\_A}{dV\_A} = \text{error} \tag{4}$$

The IC has three states that can be presented by the following Eqs. From Eq. (5)–(7) [15].

$$\frac{\Delta I\_A}{\Delta V\_A} = -\frac{I\_A}{V\_A} = -G \tag{5}$$

switches such as MOSFET or IGBT (as shown in Figure 4). The main triggering signals are generated as train of pulses from MPPT-algorithm output when the

Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid…

3.4 Soft-switching multi-level three-phase diode-clamped inverter topology

To interconnect between the DC output of the PV and the main AC-grid, it should be used as a bi-directional converter to transfer power from DC-side to AC-one and vice versa. So, a three-level voltage-source current-controlled converter with neutral point clamped (NPC) is proposed. Figure 5 shows the circuit configuration of the NPC inverter. Each leg has four IGBTs connected in series. The applied voltage on the IGBT is one-half of the conventional two-level inverter. The bus voltage is split into two by the connection of equal series connected bus capacitors. Each leg is completed with the addition of two clamp diodes. The main advantages of this technique are its capability of handling higher voltages, lower line-to-line and common-mode voltage steps, and lower output current ripple for the same

switching frequency as that used in a two-level inverter.

Modeling of on-grid PV array with MPPT converter and inverter set.

The three-phase multi-level (with three-level) diode-clamped inverter that proposed in this research is shown in Figure 5. The main idea depends on the topology of the neutral point clamped (NPC), which was proposed by Nabae, Takahashi, and

duty-ratio varies.

DOI: http://dx.doi.org/10.5772/intechopen.89530

Akagi in 1981 [17, 18].

Figure 4.

Figure 5.

53

Three-phase three-level inverter (power circuit).

or

$$\frac{\Delta I\_A}{\Delta V\_A} > -\frac{I\_A}{V\_A} \tag{6}$$

or

$$\frac{\Delta I\_A}{\Delta V\_A} < -\frac{I\_A}{V\_A} \tag{7}$$

At MPP, the right-hand side (RHS) of the equation equals to the left-hand side (LHS; Eq. 5). If the LHS is greater than RHS (Eq. 6), the power point lies on the left. If Eq. (7) is verified, the point lies in the right of MPP. This method is similar to the Perturb and Observe method for searching the MPPT. This method has good power transient performances through the atmospheric conditions rapid changes.

The error signal (e) can be minimized by using an integral regulator, then the duty ratio at MPP (DMPPÞ is obtained as:

$$D\_{MPP} = K\_I \int\_0^{\Delta t} \left(\frac{\Delta I\_A}{\Delta V\_A} + \frac{I\_A}{V\_A}\right) dt \tag{8}$$

The final output control signal needed to trigger the IGBT boost converter switch can be obtained with the help of Matlab/Simulink as illustrated in Figure 3.

#### 3.3 Boost converter

The main function of boost converter is used to boost voltage, so it is used with PV to raise its output voltage from 320 to 540 V. The main components are a series inductor with shunt capacitor, and both are controlled through the electronic

Figure 3. Matlab/Simulink MPPT algorithm with boost-converter pulse generation.

Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid… DOI: http://dx.doi.org/10.5772/intechopen.89530

switches such as MOSFET or IGBT (as shown in Figure 4). The main triggering signals are generated as train of pulses from MPPT-algorithm output when the duty-ratio varies.

#### 3.4 Soft-switching multi-level three-phase diode-clamped inverter topology

The three-phase multi-level (with three-level) diode-clamped inverter that proposed in this research is shown in Figure 5. The main idea depends on the topology of the neutral point clamped (NPC), which was proposed by Nabae, Takahashi, and Akagi in 1981 [17, 18].

To interconnect between the DC output of the PV and the main AC-grid, it should be used as a bi-directional converter to transfer power from DC-side to AC-one and vice versa. So, a three-level voltage-source current-controlled converter with neutral point clamped (NPC) is proposed. Figure 5 shows the circuit configuration of the NPC inverter. Each leg has four IGBTs connected in series. The applied voltage on the IGBT is one-half of the conventional two-level inverter. The bus voltage is split into two by the connection of equal series connected bus capacitors. Each leg is completed with the addition of two clamp diodes. The main advantages of this technique are its capability of handling higher voltages, lower line-to-line and common-mode voltage steps, and lower output current ripple for the same switching frequency as that used in a two-level inverter.

#### Figure 4.

division of current by voltage. The quotient of this division equal to the incremental

The IC has three states that can be presented by the following Eqs. From

¼ � IA VA

> <sup>&</sup>gt; � IA VA

<sup>&</sup>lt; � IA VA

At MPP, the right-hand side (RHS) of the equation equals to the left-hand side (LHS; Eq. 5). If the LHS is greater than RHS (Eq. 6), the power point lies on the left. If Eq. (7) is verified, the point lies in the right of MPP. This method is similar to the Perturb and Observe method for searching the MPPT. This method has good power

The error signal (e) can be minimized by using an integral regulator, then the

ΔIA ΔVA þ IA VA

� �

ΔIA ΔVA

ΔIA ΔVA

transient performances through the atmospheric conditions rapid changes.

ð<sup>Δ</sup><sup>t</sup> 0

The final output control signal needed to trigger the IGBT boost converter switch can be obtained with the help of Matlab/Simulink as illustrated in Figure 3.

The main function of boost converter is used to boost voltage, so it is used with PV to raise its output voltage from 320 to 540 V. The main components are a series inductor with shunt capacitor, and both are controlled through the electronic

DMPP ¼ KI

Matlab/Simulink MPPT algorithm with boost-converter pulse generation.

¼ IA þ VA

dIA dVA

¼ error (4)

dt (8)

(6)

(7)

¼ �G (5)

conductance (IC).

Energy Storage Devices

Eq. (5)–(7) [15].

or

or

dPA dVA

duty ratio at MPP (DMPPÞ is obtained as:

3.3 Boost converter

Figure 3.

52

<sup>¼</sup> d Vð Þ <sup>A</sup>:IA dVA

> ΔIA ΔVA

> > Modeling of on-grid PV array with MPPT converter and inverter set.

Figure 5. Three-phase three-level inverter (power circuit).

This converter can produce three voltage levels on the output: the DC bus plus voltage (VpÞ, zero voltage ð Þ V<sup>0</sup> , and DC bus negative voltage. For a one phase (phase A) operation, when IGBTs Q<sup>1</sup> and Q<sup>2</sup> are turned on, the output is connected to Vp; when Q<sup>2</sup> and Q<sup>3</sup> are turned on, the output is connected to V0; and when Q<sup>3</sup> and Q<sup>4</sup> are turned on, the output is connected to Vn [19].

the air gap between the primary and secondary winding, cost of the magnetic material for the core, weight of the core, eddy current losses in the core, operating frequency, and sensitivity to misalignment between primary and secondary windings. Due to the large air gap for the transformer, this configuration leads to a large leakage inductance and low mutual coupling that involves a large magnetizing current. Because of CPT transformer with air core has a lightweight with core losses, it is selected in this study for both windings. The coupling coefficient, k, affects directly on the capability of the power transferred of the ICPT. This coeffi-

Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid…

cients of the primary and secondary coils, and M is the mutual inductance between them. Figure 7a shows the schematic diagram of the transformer, and Figure 7b illustrates the equivalent circuit of the model, where Lp and Ls are the leakage inductance of both primary and secondary windings of the ACT. The powertransfer capability to the batteries can be improved by compensation to eliminate the effect of induction. This can be achieved by connecting a capacitor in both sides

of the transformer. It is called series-series (SS) compensation as shown in Figure 7c. This combination makes the primary capacitance independent of both

Modeling of an ICPT air-core transformer with SS compensation. (a) Schematic diagram. (b) Equivalent

<sup>p</sup> , where L<sup>1</sup> and L<sup>2</sup> are the self-inductance coeffi-

M L1L<sup>2</sup>

cient can be computed as k ¼ ffiffiffiffiffiffiffi

DOI: http://dx.doi.org/10.5772/intechopen.89530

Figure 7.

55

circuit. (c) Equivalent circuit with SS-compensation.

#### 3.5 Voltage-source DC/AC converter/inverter control

The boost-converter output should be constant and equal or greater than the peak value of the AC supply voltage for the grid. To do this, a robust controller should be used. The proposed technique uses PI-smart controller with self-tuning adaptive (Kp,KiÞ parameters by using bacteria-foraging optimization algorithm (BFO). The voltage error signal ev is the difference between the actual and reference DC-link voltage ev <sup>¼</sup> <sup>V</sup> <sup>∗</sup> dc � Vdc � ). This value should be minimized to a small or zero [8]. The DC-link voltage can be computed as the value that equals to peak value of the line-to-line voltage (380 ffiffi 2 <sup>p</sup> ffi540 V). In the same time, the inverter power factor (P.F.) is equal to unity. This can be achieved by using another PIsmart controller for the current and making I <sup>∗</sup> qs ¼ 0. This controller is used to minimize the current error signal that is the difference between the direct reference and the actual current components ei <sup>¼</sup> <sup>I</sup> <sup>∗</sup> ds � Ids � ). Figure 6 illustrates the overall control algorithm and the Matlab/Simulink blocks. The switching frequency for the converter is equal to 30 KHz to give 50 Hz voltage output.

#### 3.6 Modeling of contactless power-transfer air-core transformer (ACT)

One of the more reliable and safe ways to transfer power is un-pugged but connected wireless method. This method depends on the inductively coupling aircore transformer with a large air gap. The power is energized from the floor of the station to the primary winding, and then by the effect of induction, it is transferred to the secondary winding that puts under the vehicle through a wide air gap. So, this transformer needs special construction to design the primary and secondary windings and its air-gap. There are several configurations for this transformer such as the core of coil: some have air core, and others have iron core. Also, several factors should be taken into consideration through modeling and designing of ACT such as

Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid… DOI: http://dx.doi.org/10.5772/intechopen.89530

the air gap between the primary and secondary winding, cost of the magnetic material for the core, weight of the core, eddy current losses in the core, operating frequency, and sensitivity to misalignment between primary and secondary windings. Due to the large air gap for the transformer, this configuration leads to a large leakage inductance and low mutual coupling that involves a large magnetizing current. Because of CPT transformer with air core has a lightweight with core losses, it is selected in this study for both windings. The coupling coefficient, k, affects directly on the capability of the power transferred of the ICPT. This coefficient can be computed as k ¼ ffiffiffiffiffiffiffi M L1L<sup>2</sup> <sup>p</sup> , where L<sup>1</sup> and L<sup>2</sup> are the self-inductance coefficients of the primary and secondary coils, and M is the mutual inductance between them. Figure 7a shows the schematic diagram of the transformer, and Figure 7b illustrates the equivalent circuit of the model, where Lp and Ls are the leakage inductance of both primary and secondary windings of the ACT. The powertransfer capability to the batteries can be improved by compensation to eliminate the effect of induction. This can be achieved by connecting a capacitor in both sides of the transformer. It is called series-series (SS) compensation as shown in Figure 7c. This combination makes the primary capacitance independent of both

Figure 7.

Modeling of an ICPT air-core transformer with SS compensation. (a) Schematic diagram. (b) Equivalent circuit. (c) Equivalent circuit with SS-compensation.

This converter can produce three voltage levels on the output: the DC bus plus

The boost-converter output should be constant and equal or greater than the peak value of the AC supply voltage for the grid. To do this, a robust controller should be used. The proposed technique uses PI-smart controller with self-tuning adaptive (Kp,KiÞ parameters by using bacteria-foraging optimization algorithm (BFO). The voltage error signal ev is the difference between the actual and reference

zero [8]. The DC-link voltage can be computed as the value that equals to peak

2

power factor (P.F.) is equal to unity. This can be achieved by using another PI-

minimize the current error signal that is the difference between the direct reference

control algorithm and the Matlab/Simulink blocks. The switching frequency for the

3.6 Modeling of contactless power-transfer air-core transformer (ACT)

One of the more reliable and safe ways to transfer power is un-pugged but connected wireless method. This method depends on the inductively coupling aircore transformer with a large air gap. The power is energized from the floor of the station to the primary winding, and then by the effect of induction, it is transferred to the secondary winding that puts under the vehicle through a wide air gap. So, this transformer needs special construction to design the primary and secondary windings and its air-gap. There are several configurations for this transformer such as the core of coil: some have air core, and others have iron core. Also, several factors should be taken into consideration through modeling and designing of ACT such as

ds � Ids

� ). This value should be minimized to a small or

<sup>p</sup> ffi540 V). In the same time, the inverter

� ). Figure 6 illustrates the overall

qs ¼ 0. This controller is used to

voltage (VpÞ, zero voltage ð Þ V<sup>0</sup> , and DC bus negative voltage. For a one phase (phase A) operation, when IGBTs Q<sup>1</sup> and Q<sup>2</sup> are turned on, the output is connected to Vp; when Q<sup>2</sup> and Q<sup>3</sup> are turned on, the output is connected to V0; and when Q<sup>3</sup>

and Q<sup>4</sup> are turned on, the output is connected to Vn [19].

3.5 Voltage-source DC/AC converter/inverter control

dc � Vdc

converter is equal to 30 KHz to give 50 Hz voltage output.

Control algorithms for DC-link voltage and current regulator for three-level inverter.

DC-link voltage ev <sup>¼</sup> <sup>V</sup> <sup>∗</sup>

Energy Storage Devices

Figure 6.

54

value of the line-to-line voltage (380 ffiffi

smart controller for the current and making I <sup>∗</sup>

and the actual current components ei <sup>¼</sup> <sup>I</sup> <sup>∗</sup>

the magnetic coupling and the load [2]. According to the resonant frequency through both primary and secondary coils, it can compute and select the values of the primary and secondary capacitances C<sup>1</sup> and C2, respectively. This maximizes the power transferred to the batteries and improves the power factor of the supply to unity.

The power transferred from the primary to the secondary is given as follows:

$$P\_2 = \frac{\alpha\_0^2}{R\_L} M^2 I\_p^2 \tag{9}$$

where ω0,Ip, and RL are the resonant frequency, primary current, and the load resistance, respectively. The value of ω<sup>0</sup> is chosen equal to both primary and secondary windings and can be computed as follows:

$$
\rho\_0 = \frac{1}{\sqrt{L\_p C\_1}} = \frac{1}{\sqrt{L\_s C\_2}} \tag{10}
$$

So, the capacitance values of both primary and secondary windings C1and C<sup>2</sup> can be obtained as follows:

$$C\_1 = \frac{1}{L\_p \alpha\_0^2} \text{ and } C\_2 = \frac{1}{L\_r \alpha\_0^2} \tag{11}$$

3.7 Resonant DC/AC high-frequency converter

The high-frequency resonant converter with SS compensation of ACT.

compensated capacitor.

Table 1.

Figure 9.

57

Parameters of ACT.

4. Second part (mobile side)

DC-link output voltage cannot be transferred directly to the AC transformer. So, a resonant AC converter is required. This converter is a single-phase bridge inverter switched at a very high-resonant frequency as shown in Figure 9. The output of the converter is fed to the primary winding of the air-core transformer through a series

Parameter Value No. of primary turns (N1) 6 No. of secondary turns ð Þ N<sup>2</sup> 4 Radius of the primary coil (r1) 9.27 mm Radius of the secondary coil (r2) 8.00 mm The primary-coil length and width (a,b) 0.9 m The secondary-coil length and width (c,d) 0.6 m Internal resistance of the primary (R1) 1.2365 mΩ Internal resistance of the secondary (R2) 2.2781 mΩ Primary self-inductance (L1Þ 0.082 mH Secondary self-inductance (L2Þ 0.041 mH Mutual inductance between coils (M) .011 mH Air gap length 175 mm The coupling coefficient, k 0.2 Voltage of secondary (V2) 500 V Resonant frequency ( f <sup>0</sup>) 20 KHz Power of secondary (P2) 100 kW Primary series compensation ðC1) 0.7722 uf Secondary series compensation ðC2) 1.5441 uf

Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid…

DOI: http://dx.doi.org/10.5772/intechopen.89530

The second part of the proposed system implies the secondary coil of the ACT that constructed inside the EV with inductive coupling with primary winding

Designing of ACT depends on the values of its parameters L1, L2, and M. This can be obtained from its physical dimensions. The coils with rectangular form are proposed to both primary and secondary windings of ACT (as shown in Figure 8). The advantage of this form is giving much better tolerance to misalignment. The flow chart illustrated in [4] illustrates the design process for the overall ACT, and the parameters of 100 kW charging station are tabulated in Table 1.

A general software package with help of the Matlab m-file is introduced for designing and simulation of the ACT. This package is validated with much accuracy for any ACT with a rectangular form for different dimensions and relative position between them.

Figure 8. Geometrical dimensions of the proposed ACT.

Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid… DOI: http://dx.doi.org/10.5772/intechopen.89530


#### Table 1.

the magnetic coupling and the load [2]. According to the resonant frequency through both primary and secondary coils, it can compute and select the values of the primary and secondary capacitances C<sup>1</sup> and C2, respectively. This maximizes the power transferred to the batteries and improves the power factor of the supply

The power transferred from the primary to the secondary is given as follows:

where ω0,Ip, and RL are the resonant frequency, primary current, and the load resistance, respectively. The value of ω<sup>0</sup> is chosen equal to both primary and sec-

> ffiffiffiffiffiffiffiffiffiffi LpC<sup>1</sup> <sup>p</sup> <sup>¼</sup> <sup>1</sup>

So, the capacitance values of both primary and secondary windings C1and C<sup>2</sup>

Designing of ACT depends on the values of its parameters L1, L2, and M. This can be obtained from its physical dimensions. The coils with rectangular form are proposed to both primary and secondary windings of ACT (as shown in

misalignment. The flow chart illustrated in [4] illustrates the design process for the overall ACT, and the parameters of 100 kW charging station are tabulated in

A general software package with help of the Matlab m-file is introduced for designing and simulation of the ACT. This package is validated with much accuracy for any ACT with a rectangular form for different dimensions and relative position

Figure 8). The advantage of this form is giving much better tolerance to

and <sup>C</sup><sup>2</sup> <sup>¼</sup> <sup>1</sup>

ffiffiffiffiffiffiffiffiffi LsC<sup>2</sup>

> Lsω<sup>2</sup> 0

<sup>p</sup> (9)

p (10)

(11)

<sup>P</sup><sup>2</sup> <sup>¼</sup> <sup>ω</sup><sup>2</sup> 0 RL M<sup>2</sup> I 2

<sup>ω</sup><sup>0</sup> <sup>¼</sup> <sup>1</sup>

<sup>C</sup><sup>1</sup> <sup>¼</sup> <sup>1</sup> Lpω<sup>2</sup> 0

ondary windings and can be computed as follows:

can be obtained as follows:

to unity.

Energy Storage Devices

Table 1.

Figure 8.

56

Geometrical dimensions of the proposed ACT.

between them.

Parameters of ACT.

Figure 9. The high-frequency resonant converter with SS compensation of ACT.

#### 3.7 Resonant DC/AC high-frequency converter

DC-link output voltage cannot be transferred directly to the AC transformer. So, a resonant AC converter is required. This converter is a single-phase bridge inverter switched at a very high-resonant frequency as shown in Figure 9. The output of the converter is fed to the primary winding of the air-core transformer through a series compensated capacitor.

#### 4. Second part (mobile side)

The second part of the proposed system implies the secondary coil of the ACT that constructed inside the EV with inductive coupling with primary winding

through the air gap. This side (as shown in Figures 1 and 10) includes single-phase uncontrolled bridge rectifier, switched-mode power buck converter with its intelligent battery-charging controller, and tracking batteries.

4.2.2 Li-ion battery

Vbat <sup>¼</sup> <sup>E</sup><sup>0</sup> � <sup>R</sup>:<sup>i</sup> � <sup>K</sup> Qi <sup>∗</sup>

DOI: http://dx.doi.org/10.5772/intechopen.89530

Vbat <sup>¼</sup> <sup>E</sup><sup>0</sup> � <sup>R</sup>:<sup>i</sup> � <sup>K</sup> Qi <sup>∗</sup>

(Ω), <sup>Q</sup> is the capacitance of the battery (Ah), it <sup>¼</sup> <sup>Ð</sup>

5. Simulation results and discussion

minimize the eddy losses [1].

from 100 kW to less than 20 kW.

59

Vbat <sup>¼</sup> <sup>E</sup><sup>0</sup> � <sup>R</sup>:<sup>i</sup> � <sup>K</sup> Q it <sup>þ</sup> <sup>i</sup>

Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid…

Discharge : Vbat <sup>¼</sup> <sup>E</sup><sup>0</sup> � <sup>R</sup>:<sup>i</sup> � <sup>K</sup> Q it <sup>þ</sup> <sup>i</sup> <sup>∗</sup> ð Þ

where Vbat is the voltage of the battery (V), E<sup>0</sup> is the battery constant voltage battery (V), K is the constant of polarization (V = Ah) or resistance of polarization

battery (Ah), R is the internal resistance of the battery (Ω), i is the current of the battery (A), i <sup>∗</sup> is the current of battery filtered (A), A is the amplitude of exponential zone (V), and <sup>B</sup> is the time-constant inverse of the exponential zone ð Þ Ah �<sup>1</sup>

A Matlab/Simulink and m-file general software package are introduced for designing 100-kW EV-battery charger of a renewable-energized public station. The data obtained in Section 3 are used to build the PV arrays. The given parameters from Table 1 are also used to simulate the ACT transformer needed for the ICPT. The ACT HV windings are proposed to be made from the twisted Litz conductors to

To test the proposed charging system, four 19-Kwh identical EV Honda batteries are charged at the same time. The charging voltage rate is 200 V. When the state of charging (SOC) reaches to 30%, the battery starts to charge. The proposed simulated data for the IGBT-rated voltage and current are 1500 V and 200 A, respectively. To test the robustness of the system against PV fluctuation, the PV-irradiance profile is assumed to vary in the range of 1000 and 250 W/m2 as shown in Figure 12a. Dependently, the PV output terminal voltages with and without using the proposed

The duty ratio of the converter with time according to the variation of irradiance

and terminal voltage is shown in Figure 12c. The power profile with irradiance variation is clearly illustrated in Figure 12d. Through time interval 0.5–0.7, the irradiance reduced from 1000 to 250, and dependently, the power also reduced

This illustrates the importance of the on-grid technique for the station. The modulation-index profile of the three-level interconnected inverter is illustrated in

smart controller are illustrated in Figures 12b and e, respectively.

ð Þ it � <sup>0</sup>:<sup>1</sup> <sup>Q</sup> � <sup>K</sup> Qit

<sup>∗</sup> ð Þ

ð Þ j j it � <sup>0</sup>:<sup>1</sup> <sup>Q</sup> � <sup>K</sup> <sup>Q</sup>:it

ð Þ <sup>Q</sup> � it <sup>þ</sup> Ae�B:it (14)

ð Þ <sup>Q</sup> � it <sup>þ</sup> Ae�B:it (15)

ð Þ <sup>Q</sup> � it <sup>þ</sup> <sup>e</sup>

ð Þ <sup>Q</sup> � it <sup>þ</sup> <sup>e</sup>

<sup>t</sup> (16)

<sup>t</sup> (17)

:

idt is the actual charge of the

Charging:

Discharge:

Charging:

4.2.3 NiMH and NiCd battery

#### 4.1 DC/DC Buck converter

An un-controlled bridge rectifier is required to convert the energized AC secondary-coil power to a DC current. The output voltage of the bridge is greater than the battery-terminal voltage. So, a buck converter (as shown in Figure 10) is needed. The main components of it are a controlled switch, diode, series reactor, and shunt capacitor. The proposed smart control circuit is proposed to adapt the battery-bank charging voltage (Figure 11).

#### 4.2 Mathematical model of EV battery

The actual mathematical dynamic model of EV battery for simulation will be obtained from Refs. [20, 21] in the following sections:

#### 4.2.1 For lead-acid battery

Charging:

$$V\_{bat} = E\_0 - R.i - K \frac{Qi^\*}{(it - 0.1 \, Q)} - K \frac{Qit}{(Q - it)} + e^t \tag{12}$$

$$\text{Discharge}: V\_{bat} = E\_0 - R.i - K \frac{Q(it + i^\*)}{(Q - i.t)} + e^t \tag{13}$$

Figure 10. Components of the electric vehicle section (secondary side).

Figure 11. The control circuit of Buck converter.

Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid… DOI: http://dx.doi.org/10.5772/intechopen.89530

#### 4.2.2 Li-ion battery

through the air gap. This side (as shown in Figures 1 and 10) includes single-phase uncontrolled bridge rectifier, switched-mode power buck converter with its

An un-controlled bridge rectifier is required to convert the energized AC secondary-coil power to a DC current. The output voltage of the bridge is greater than the battery-terminal voltage. So, a buck converter (as shown in Figure 10) is needed. The main components of it are a controlled switch, diode, series reactor, and shunt capacitor. The proposed smart control circuit is proposed to adapt the

The actual mathematical dynamic model of EV battery for simulation will be

Discharge : Vbat <sup>¼</sup> <sup>E</sup><sup>0</sup> � <sup>R</sup>:<sup>i</sup> � <sup>K</sup> Q it <sup>þ</sup> <sup>i</sup> <sup>∗</sup> ð Þ

ð Þ it � <sup>0</sup>:<sup>1</sup> <sup>Q</sup> � <sup>K</sup> Qit

ð Þ <sup>Q</sup> � it <sup>þ</sup> <sup>e</sup>

þ e

ð Þ Q � i:t

<sup>t</sup> (12)

<sup>t</sup> (13)

intelligent battery-charging controller, and tracking batteries.

4.1 DC/DC Buck converter

Energy Storage Devices

4.2.1 For lead-acid battery

Charging:

Figure 10.

Figure 11.

58

The control circuit of Buck converter.

battery-bank charging voltage (Figure 11).

4.2 Mathematical model of EV battery

Components of the electric vehicle section (secondary side).

obtained from Refs. [20, 21] in the following sections:

Vbat <sup>¼</sup> <sup>E</sup><sup>0</sup> � <sup>R</sup>:<sup>i</sup> � <sup>K</sup> Qi <sup>∗</sup>

Charging:

$$V\_{bat} = E\_0 - R.i - K \frac{Qi^\*}{(it - 0.1 \text{ Q})} - K \frac{Qit}{(Q - it)} + Ae^{-B.it} \tag{14}$$

Discharge:

$$V\_{bat} = E\_0 - R.i - K \frac{Q(it + i^\*)}{(\,\,Q - it)} + Ae^{-B.it} \tag{15}$$

4.2.3 NiMH and NiCd battery

Charging:

$$V\_{bat} = E\_0 - R.i - K \frac{Qi^\*}{(|it| - 0.1 \, Q)} - K \frac{Q.it}{(|Q - it|)} + e^t \tag{16}$$

$$\text{Discharge}: V\_{bat} = E\_0 - R.i - K \frac{Q(it + i^\*)}{(Q - it)} + \varepsilon^t \tag{17}$$

where Vbat is the voltage of the battery (V), E<sup>0</sup> is the battery constant voltage battery (V), K is the constant of polarization (V = Ah) or resistance of polarization (Ω), <sup>Q</sup> is the capacitance of the battery (Ah), it <sup>¼</sup> <sup>Ð</sup> idt is the actual charge of the battery (Ah), R is the internal resistance of the battery (Ω), i is the current of the battery (A), i <sup>∗</sup> is the current of battery filtered (A), A is the amplitude of exponential zone (V), and <sup>B</sup> is the time-constant inverse of the exponential zone ð Þ Ah �<sup>1</sup> :

#### 5. Simulation results and discussion

A Matlab/Simulink and m-file general software package are introduced for designing 100-kW EV-battery charger of a renewable-energized public station. The data obtained in Section 3 are used to build the PV arrays. The given parameters from Table 1 are also used to simulate the ACT transformer needed for the ICPT. The ACT HV windings are proposed to be made from the twisted Litz conductors to minimize the eddy losses [1].

To test the proposed charging system, four 19-Kwh identical EV Honda batteries are charged at the same time. The charging voltage rate is 200 V. When the state of charging (SOC) reaches to 30%, the battery starts to charge. The proposed simulated data for the IGBT-rated voltage and current are 1500 V and 200 A, respectively. To test the robustness of the system against PV fluctuation, the PV-irradiance profile is assumed to vary in the range of 1000 and 250 W/m2 as shown in Figure 12a. Dependently, the PV output terminal voltages with and without using the proposed smart controller are illustrated in Figures 12b and e, respectively.

The duty ratio of the converter with time according to the variation of irradiance and terminal voltage is shown in Figure 12c. The power profile with irradiance variation is clearly illustrated in Figure 12d. Through time interval 0.5–0.7, the irradiance reduced from 1000 to 250, and dependently, the power also reduced from 100 kW to less than 20 kW.

This illustrates the importance of the on-grid technique for the station. The modulation-index profile of the three-level interconnected inverter is illustrated in

Figure 12.

(a) PV-array irradiance. (b) PV-array terminal voltage profile. (c) Duty cycle for boost converter. (d) PV mean power. (e) PV-array terminal voltage (reference and actual). (f) Modulation-index variation of threelevel inverter.

Figure 12f. The figures illustrate the inverter output-voltage changes with the variation of PV irradiance.

Dependently, Figure 13 shows a comparison for both the 3-level line to line output voltage of the inverter and the grid. It can be noticed that the line voltage maximum value of both is about 540 V (380√2). Figure 14 illustrates the resonant converter output voltage with its high-frequency value that is equals to 30 KHz.

By using ACT with its turns ratio equal (10:6), when the primary voltage equals

to 1000 V, the secondary-winding output voltage equals to 600 V. Figure 19 describes the simulation results for the initial battery state of charge (SOC) that

Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid…

The EV-Honda battery-charging current profile with time is shown in Figure 20. According to switching process of the buck converter, the current varies

according to the signals of current controller.

Line voltage of both three-level inverter and the main grid.

DOI: http://dx.doi.org/10.5772/intechopen.89530

equals to 30%.

61

High-frequency inverter-output voltage.

Figure 14.

Figure 13.

So, both Figures 15 and 16 describe the high-frequency input voltage and current of the ACT-primary winding, respectively. On the other hand, the voltage and currents of the secondary winding of ACT are illustrated in both Figures 17 and 18, respectively.

Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid… DOI: http://dx.doi.org/10.5772/intechopen.89530

Figure 13. Line voltage of both three-level inverter and the main grid.

Figure 14. High-frequency inverter-output voltage.

By using ACT with its turns ratio equal (10:6), when the primary voltage equals to 1000 V, the secondary-winding output voltage equals to 600 V. Figure 19 describes the simulation results for the initial battery state of charge (SOC) that equals to 30%.

The EV-Honda battery-charging current profile with time is shown in Figure 20. According to switching process of the buck converter, the current varies according to the signals of current controller.

Figure 12f. The figures illustrate the inverter output-voltage changes with the

(a) PV-array irradiance. (b) PV-array terminal voltage profile. (c) Duty cycle for boost converter. (d) PV mean power. (e) PV-array terminal voltage (reference and actual). (f) Modulation-index variation of three-

Dependently, Figure 13 shows a comparison for both the 3-level line to line output voltage of the inverter and the grid. It can be noticed that the line voltage maximum value of both is about 540 V (380√2). Figure 14 illustrates the resonant converter output voltage with its high-frequency value that is equals to 30 KHz. So, both Figures 15 and 16 describe the high-frequency input voltage and current of the ACT-primary winding, respectively. On the other hand, the voltage and currents of the secondary winding of ACT are illustrated in both Figures 17 and 18,

variation of PV irradiance.

respectively.

60

Figure 12.

Energy Storage Devices

level inverter.

<sup>η</sup>ol <sup>¼</sup> Po Pi

power (W).

Figure 17.

Figure 18.

63

ACT-secondary current.

ACT-secondary voltage.

� <sup>100</sup> <sup>¼</sup> nvbib

DOI: http://dx.doi.org/10.5772/intechopen.89530

Pm

� <sup>100</sup> <sup>¼</sup> <sup>4</sup> � <sup>200</sup> � <sup>90</sup>

where vb and ib are the voltage and current (V,A) of the battery charging, respectively; n is the number of batteries to be charged; and Pm is the PV average

Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid…

<sup>94000</sup> ffi <sup>76</sup>:6% (18)

Figure 15. ACT-primary voltage.

Figure 16. ACT-primary current.

Also, the voltage level through battery-charging process is demonstrated in Figure 21. The value of the battery voltage is controlled to be kept constant, where the charging process is achieved at constant voltage.

However, the overall average charging efficiency can be calculated as follows:

Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid… DOI: http://dx.doi.org/10.5772/intechopen.89530

$$\eta\_{ol} = \left(\frac{P\_o}{P\_i} \times 100\right) = \left(\frac{nv\_bi\_b}{P\_m} \times 100\right) = \left(\frac{4 \times 200 \times 90}{94000}\right) \cong 76.6\% \tag{18}$$

where vb and ib are the voltage and current (V,A) of the battery charging, respectively; n is the number of batteries to be charged; and Pm is the PV average power (W).

Figure 17. ACT-secondary voltage.

Figure 18. ACT-secondary current.

Also, the voltage level through battery-charging process is demonstrated in Figure 21. The value of the battery voltage is controlled to be kept constant, where

However, the overall average charging efficiency can be calculated as follows:

the charging process is achieved at constant voltage.

Figure 15.

Figure 16.

62

ACT-primary current.

ACT-primary voltage.

Energy Storage Devices

Figure 19. Initial-battery state of charge (SOC).

6. Summary and recommendations

Battery-terminal voltage profile through charging.

DOI: http://dx.doi.org/10.5772/intechopen.89530

Figure 21.

65

The chapter of this book puts in the hands of the reader the main tools required to model and design a solar public station for EV-battery charging. This station is energized from the on-grid PV system and proposed as an off-board 100 kW rating. The proposed system includes a sophisticated un-plugged technique to overcome the problems of wiring and hazards. This technique depends on the inductively coupled power transfer through the air-core transformer with a large air gap. Two main parts are implied and classified according to the winding of the transformer: primary and secondary parts. The primary part is placed on the floor of the station, and the other part is installed at the bottom of the car. A Matlab/Simulink with mfile is powerful tool that used to model and simulate all components such as PV model, maximum power point tracking (MPPT) technique, boost inverter, threelevel inverter, series-series (SS) compensation with high-frequency resonant converter, air-core transformer, rectifier, switched-mode power converter, and battery-charging smart controller. The proposed station is tested by charging four 19-kW batteries for EV from Honda Company. Simulation results include the battery state of charge (SOC), charging voltage, and currents. Using high-frequency compensation with switched-mode power converter and smart PI-tuning controller for the charger make the charging process is so fast. The ideal overall efficiency for the proposed solar station is relatively high due to assuming ideal cases for all modeled components. Finally, due to the rapid change in the cost of all components, especially the PV panels, this chapter does not imply a visible study for the station.

Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid…

So, it is preferable to conduct a visible study for each country separately.

Figure 20. Battery-charging current.

It should be noted that the efficiency calculated above is the approximate value because of using ideal models for most components used in the design. However, this introduced software package can help researchers and designers to model a solar battery-charging public station. Also, the visibility study for this station does not included her because of several reasons such as the cost of the PV panel is decreased with time and construction cost with other power electronic components differ from place to another in the world.

Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid… DOI: http://dx.doi.org/10.5772/intechopen.89530

Figure 21. Battery-terminal voltage profile through charging.

#### 6. Summary and recommendations

The chapter of this book puts in the hands of the reader the main tools required to model and design a solar public station for EV-battery charging. This station is energized from the on-grid PV system and proposed as an off-board 100 kW rating. The proposed system includes a sophisticated un-plugged technique to overcome the problems of wiring and hazards. This technique depends on the inductively coupled power transfer through the air-core transformer with a large air gap. Two main parts are implied and classified according to the winding of the transformer: primary and secondary parts. The primary part is placed on the floor of the station, and the other part is installed at the bottom of the car. A Matlab/Simulink with mfile is powerful tool that used to model and simulate all components such as PV model, maximum power point tracking (MPPT) technique, boost inverter, threelevel inverter, series-series (SS) compensation with high-frequency resonant converter, air-core transformer, rectifier, switched-mode power converter, and battery-charging smart controller. The proposed station is tested by charging four 19-kW batteries for EV from Honda Company. Simulation results include the battery state of charge (SOC), charging voltage, and currents. Using high-frequency compensation with switched-mode power converter and smart PI-tuning controller for the charger make the charging process is so fast. The ideal overall efficiency for the proposed solar station is relatively high due to assuming ideal cases for all modeled components. Finally, due to the rapid change in the cost of all components, especially the PV panels, this chapter does not imply a visible study for the station. So, it is preferable to conduct a visible study for each country separately.

It should be noted that the efficiency calculated above is the approximate value because of using ideal models for most components used in the design. However, this introduced software package can help researchers and designers to model a solar battery-charging public station. Also, the visibility study for this station does not included her because of several reasons such as the cost of the PV panel is decreased with time and construction cost with other power electronic components

differ from place to another in the world.

Figure 19.

Figure 20.

64

Battery-charging current.

Initial-battery state of charge (SOC).

Energy Storage Devices

Energy Storage Devices

### Author details

Essamudin Ali Ebrahim Power Electronics and Energy Conversion Department, Electronics Research Institute, Cairo, Egypt

References

TIE.2005.855672

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[2] Wang C-S, Stielau OH, Covic GA. Design considerations for a contactless electric vehicle battery charger. IEEE Transactions on Industrial Electronics. 2005;52(5):1308-1314. DOI: 10.1109/

[3] Sallán J, Villa JL, Llombart A, Fco J. Sanz: Optimal design of ICPT systems applied to electric vehicle battery charge. IEEE Transactions on Industrial Electronics. 2009;56(6):2140-2149. DOI: 10.1109/TIE.2009.2015359

[4] Villa JL, Sallán J, Llombart A, Fco Sanz J. Design of a high frequency inductively coupled power transfer system for electric vehicle battery charge. Elsevier Journal of Applied Energy. 2009;86:355-363. DOI: 10.1016/

[5] Tabari M, Yazdani A. Stability of a dc distribution system for power system integration of plug-In hybrid electric vehicles. IEEE Transactions on Smart Grid. 2014;5(5):2464-2674. DOI: 10.1109/TSG.2014.2331558

[6] Kim S, Kang F-S. Multifunctional onboard battery charger for plug-in electric vehicles. IEEE Transactions on Industrial Electronics. 2015;62(6): 3460-3472. DOI: 10.1109/TIE.2014.

[7] Janghorban S, Teixeira C, Holmes DG, McGoldrick P, Yu X. Magnetics design for a 2.5-kW battery charger. In: Proceedings of Australasian

Universities Power Engineering Conference, AUPEC 2014, Curtin

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67

\*Address all correspondence to: essamudin@eri.sci.eg; essamudin@yahoo.com

© 2019 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.

Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid… DOI: http://dx.doi.org/10.5772/intechopen.89530

#### References

[1] Ebrahim EA. A general software package for modelling a contact-less electric-vehicles battery-charging public-station fed from on-grid photovoltaic array. Elsevier Journal of Electrical Systems and Information Technology. 2018;5:271-286. DOI: 10.1016/j.jesit.2018.04.001

[2] Wang C-S, Stielau OH, Covic GA. Design considerations for a contactless electric vehicle battery charger. IEEE Transactions on Industrial Electronics. 2005;52(5):1308-1314. DOI: 10.1109/ TIE.2005.855672

[3] Sallán J, Villa JL, Llombart A, Fco J. Sanz: Optimal design of ICPT systems applied to electric vehicle battery charge. IEEE Transactions on Industrial Electronics. 2009;56(6):2140-2149. DOI: 10.1109/TIE.2009.2015359

[4] Villa JL, Sallán J, Llombart A, Fco Sanz J. Design of a high frequency inductively coupled power transfer system for electric vehicle battery charge. Elsevier Journal of Applied Energy. 2009;86:355-363. DOI: 10.1016/ j.apenergy.2008.05.009

[5] Tabari M, Yazdani A. Stability of a dc distribution system for power system integration of plug-In hybrid electric vehicles. IEEE Transactions on Smart Grid. 2014;5(5):2464-2674. DOI: 10.1109/TSG.2014.2331558

[6] Kim S, Kang F-S. Multifunctional onboard battery charger for plug-in electric vehicles. IEEE Transactions on Industrial Electronics. 2015;62(6): 3460-3472. DOI: 10.1109/TIE.2014. 2376878

[7] Janghorban S, Teixeira C, Holmes DG, McGoldrick P, Yu X. Magnetics design for a 2.5-kW battery charger. In: Proceedings of Australasian Universities Power Engineering Conference, AUPEC 2014, Curtin

University, Perth, Australia. 2014. pp. 1-6

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Author details

Energy Storage Devices

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Essamudin Ali Ebrahim

Institute, Cairo, Egypt

provided the original work is properly cited.

Power Electronics and Energy Conversion Department, Electronics Research

\*Address all correspondence to: essamudin@eri.sci.eg; essamudin@yahoo.com

© 2019 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,

Transactions on Energy Conversion. 2007;22(2):439-449. DOI: 10.1109/ TEC.2006.874230

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Advances in Energy

Storage Systems

Section 3
