4. Proof of adaptive algorithm

IP <sup>¼</sup> CPV\_ <sup>P</sup> <sup>þ</sup>

IN � IP <sup>¼</sup> CNV\_ <sup>N</sup> <sup>þ</sup>

VP ¼ VB þ VN,

VN RN þ IP

> VN RN

VN RN þ VP RP

<sup>¼</sup> ð Þ CN <sup>þ</sup> CP <sup>V</sup>\_ <sup>N</sup> <sup>þ</sup> CPV\_ <sup>B</sup> <sup>þ</sup>

RPð Þ CN þ CP

VN þ

R Cð Þ <sup>N</sup> þ CP

XT <sup>¼</sup> �VN VG �VB �V\_ <sup>B</sup>

where the parametric vector includes all the resistance and capacitance values that must be known and the variable vector is composed of the variables that can be evaluated from all the

such that the dynamics of the insulation monitoring system are formulated as follows:

<sup>þ</sup> CPV\_ <sup>P</sup> <sup>þ</sup>

VP RP

<sup>þ</sup> CP <sup>V</sup>\_ <sup>B</sup> <sup>þ</sup> <sup>V</sup>\_ <sup>N</sup> 

> VN RN þ VP RP ,

1 CN þ CP IN � CP CN þ CP

RPð Þ CN þ CP

CP CN þ CP

VG � <sup>1</sup>

1 RPð Þ CN þ CP

(5)

<sup>V</sup>\_ <sup>N</sup> <sup>¼</sup> <sup>X</sup><sup>T</sup>θ, (6)

V\_ B

VB

(3)

(4)

VP þ

1 R Cð Þ <sup>N</sup> þ CP

Substituting Eq. (1) into Eq. (2), together with

46 New Trends in Electrical Vehicle Powertrains

which can be rewritten as follows:

<sup>V</sup>\_ <sup>N</sup> ¼ � <sup>1</sup>

¼ � <sup>1</sup> RN þ 1 R þ 1 RP 1

<sup>θ</sup><sup>T</sup> <sup>¼</sup> <sup>1</sup>

and the variable vector as:

� CP CN þ CP

Let us define the parametric vector as follows:

CN þ CP

¼ ½ � θ<sup>1</sup> θ<sup>2</sup> θ<sup>3</sup> θ<sup>4</sup>

measurements in the system, i.e., VG, VN, and VP.

RNð Þ CN þ CP

V\_ B

1 R þ 1 RN þ 1 RP 1

IN <sup>¼</sup> CNV\_ <sup>N</sup> <sup>þ</sup>

<sup>¼</sup> CNV\_ <sup>N</sup> <sup>þ</sup>

<sup>¼</sup> CNV\_ <sup>N</sup> <sup>þ</sup>

VN � <sup>1</sup>

CN þ CP

yields:

VP RP

> VN RN

(1)

(2)

If we suppose all the actual parameter values and the voltage VN are unknown, we can write the dynamic model in an estimated formation as follows:

$$
\dot{\hat{V}}\_N = \left[ -\hat{V}\_N V\_G - V\_B - \dot{V}\_B \right] \hat{\theta} + u\_\prime \tag{7}
$$

where Yb denotes the estimation of Y, and u is one part of the adaptation law that lets all the estimated values approach their true values, i.e., lim<sup>t</sup>!<sup>∞</sup> <sup>V</sup><sup>b</sup> <sup>N</sup>ð Þ� <sup>t</sup> VNð Þ<sup>t</sup> � � � � � � <sup>&</sup>lt; <sup>δ</sup> and lim<sup>t</sup>!<sup>∞</sup> <sup>θ</sup>bð Þ<sup>t</sup> � � � �θk < ε. We define the estimated error for VN and the parametric vector as e ¼ VN � Vb <sup>N</sup> and θe ¼ θ � θb, respectively. If we differentiate the estimated error, we have:

$$
\dot{\varepsilon} = \dot{V}\_N - \dot{\hat{V}}\_N = \left[ -\widehat{V}\_N V\_G - V\_B - \dot{V}\_B \right] \widetilde{\boldsymbol{\theta}} - \boldsymbol{\theta}\_1 \boldsymbol{\varepsilon} - \boldsymbol{u}.\tag{8}
$$

Invoking the Lyapunov stability criteria shows that the positive-definite function:

$$S = \mathfrak{e}^2 + \tilde{\overline{\theta}}^{\top} \tilde{\Sigma} \tilde{\overline{\theta}} \tag{9}$$

will approach zero for the negative semi-definite of its derivative; that is:

$$\begin{split} \dot{S} &= i\varepsilon + \dot{\tilde{\theta}}^T \Sigma \tilde{\theta} = \left[ -\widehat{V}\_N V\_G - V\_B - \dot{V}\_B \right] \varepsilon \tilde{\theta} - \theta\_1 \varepsilon^2 - \mu \varepsilon - \dot{\tilde{\theta}}^T \Sigma \tilde{\theta} \\\\ &= -(\theta\_1 + \lambda) \varepsilon^2 + \left( \left[ -\widehat{V}\_N V\_G - V\_B - \dot{V}\_B \right] \varepsilon - \dot{\tilde{\theta}}^T \Sigma^{1/2} \right) \Sigma^{1/2} \tilde{\theta} \\\\ &= -(\theta\_1 + \lambda) \varepsilon^2 < 0, \end{split} \tag{10}$$

provided that the adaptation law is as follows:

$$\dot{\hat{\theta}} = \Sigma^{-1/2} \begin{bmatrix} -\hat{V}\_N \mathcal{e} \\ V\_{\hat{G}} e \\ -V\_{B} e \\ -\dot{V}\_B e \end{bmatrix}, \boldsymbol{\mu} = \lambda e^2, \text{ and } \lambda > 0,\tag{11}$$

where Σ could be a positive diagonal matrix for design simplicity. We can compute the insulation resistance as follows:

$$\widehat{R}\_P = \frac{\widehat{\Theta}\_2}{\widehat{\Theta}\_3} R \text{ and } \widehat{R}\_N = \frac{1}{\left(\frac{\dot{\partial}\_1}{\partial\_2} - 1\right)\frac{1}{R} - \frac{1}{\widehat{R}\_P}}. \tag{12}$$

Figure 5 shows a calculation flowchart for estimating the insulation resistance. A detailed description of the process is as follows:


#### 5. Simulation and experimental results

To verify the proposed algorithm, for simplicity, we assumed a scenario in which an electric vehicle is driven on the road such that the battery and AC line voltages are Vb = 350 V and Va = 0 V, respectively. We set the initial insulation resistances at the positive and negative terminals to earth to be within the safety level, i.e., Rp(t = 0 s) = 600 kΩ and Rn(t = 0 s) = 500 kΩ, respectively. After 60 s, we degraded Rn(t = 60 s) to 100 kΩ, and after 120 s, we did the same for Rp(t = 120 s). To precisely characterize the electrical behavior of the equivalent circuit shown in Figure 4, we considered their parallel parasitic capacitances to be invariant at Cp = 0.3 uF and Cn = 0.2 uF. For the insulation resistance monitor, we selected the resistor R to be 20 kΩ, and we initially guessed the estimated values for Rp/Rn to be 350 kΩ/100 kΩ.

We constructed the circuit model and the estimation algorithm using Simulink software. The simulation estimation results for Rp and Rn are depicted in Figures 6 and 7, respectively. These figures show that the estimated Rp approaches the actual value within 20 s, but the estimated Rn converges to the actual value after 50 s. The relative error between actual and estimated values are both less than 1%. With respect to two degradation cases that sequentially occur on the negative and positive terminals, we found that either of the degradations would yield some fluctuation in the estimated value on the opposite side, particularly a case in which the degradation occurs on the positive terminal. As a consequence, it requires more time for convergence, i.e., 20 and 240 s for the degradations occurring on the negative and positive

sides, respectively. This is because the proposed circuit is directly connected to the negative terminal, which makes it more sensitive to voltage variations across the negative terminal and chassis ground. In other words, the high battery voltage Vb would attenuate the excitation signal coming from the negative side. Nevertheless, the simulation verifies that the proposed algorithm can estimate the actual insulation resistance and monitor its variation in the circuit model, while also considering the parasitic capacitance, as shown in Figure 4. To avoid false alarms due to ground fault detection when using this method, fault counting is necessary over

Adaptive Control for Estimating Insulation Resistance of High-Voltage Battery System in Electric Vehicles

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a period of time.

Figure 5. Flowchart for online parameter estimation.

Adaptive Control for Estimating Insulation Resistance of High-Voltage Battery System in Electric Vehicles http://dx.doi.org/10.5772/intechopen.75468 49

Figure 5. Flowchart for online parameter estimation.

i. Start the online estimation at time t0. The initial values of the estimated insulation resistance can be updated with the latest value in memory for faster convergence.

iii. The estimated voltage error is computed by Eq. (7) together with the updated parameters, where the initial value of the estimated Vn can be identical to the measurement.

iv. Based on the measured voltage data and estimated Vn, the adaptive algorithm given in Eq. (11) updates the parameter. The updated parameters are sent to the previous and

v. The insulation resistances are calculated by Eq. (12) based on the updated parameters. The minimum value is used to check whether it is under the predetermined threshold. If

vi. The waiting time required for the parameters' convergence is calculated and Tm is empir-

vii. Either the insulation resistance or an alert message is displayed, depending on whether it

To verify the proposed algorithm, for simplicity, we assumed a scenario in which an electric vehicle is driven on the road such that the battery and AC line voltages are Vb = 350 V and Va = 0 V, respectively. We set the initial insulation resistances at the positive and negative terminals to earth to be within the safety level, i.e., Rp(t = 0 s) = 600 kΩ and Rn(t = 0 s) = 500 kΩ, respectively. After 60 s, we degraded Rn(t = 60 s) to 100 kΩ, and after 120 s, we did the same for Rp(t = 120 s). To precisely characterize the electrical behavior of the equivalent circuit shown in Figure 4, we considered their parallel parasitic capacitances to be invariant at Cp = 0.3 uF and Cn = 0.2 uF. For the insulation resistance monitor, we selected the resistor R to be 20 kΩ, and we

We constructed the circuit model and the estimation algorithm using Simulink software. The simulation estimation results for Rp and Rn are depicted in Figures 6 and 7, respectively. These figures show that the estimated Rp approaches the actual value within 20 s, but the estimated Rn converges to the actual value after 50 s. The relative error between actual and estimated values are both less than 1%. With respect to two degradation cases that sequentially occur on the negative and positive terminals, we found that either of the degradations would yield some fluctuation in the estimated value on the opposite side, particularly a case in which the degradation occurs on the positive terminal. As a consequence, it requires more time for convergence, i.e., 20 and 240 s for the degradations occurring on the negative and positive

viii. For continuous online monitoring, once started, this flow is an infinite loop.

initially guessed the estimated values for Rp/Rn to be 350 kΩ/100 kΩ.

ii. The voltage values are acquired from the measured Vg, Vn, and Vp values.

following steps.

48 New Trends in Electrical Vehicle Powertrains

so, it is shown in the indicator.

ically set up to avoid misjudgment.

is below the mandatory threshold.

5. Simulation and experimental results

sides, respectively. This is because the proposed circuit is directly connected to the negative terminal, which makes it more sensitive to voltage variations across the negative terminal and chassis ground. In other words, the high battery voltage Vb would attenuate the excitation signal coming from the negative side. Nevertheless, the simulation verifies that the proposed algorithm can estimate the actual insulation resistance and monitor its variation in the circuit model, while also considering the parasitic capacitance, as shown in Figure 4. To avoid false alarms due to ground fault detection when using this method, fault counting is necessary over a period of time.

To simply validate the proposed algorithm in the laboratory, we connected a variable resistor to the proposed circuit to form a left-hand side loop of the circuit shown in Figure 4, in which the resistance, as represented by Rn, is the value to be estimated. Due to the simplicity of the single loop circuit, using Kirchhoff's current law, Rn can be evaluated in a straightforward

Adaptive Control for Estimating Insulation Resistance of High-Voltage Battery System in Electric Vehicles

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51

Figure 8. Experimental results. (a) Estimated parameters θb<sup>1</sup> and θb2. (b) Estimated resistances of the two models.

manner, as follows:

Figure 6. Estimation of Rp.

Figure 7. Estimation of Rn.

To simply validate the proposed algorithm in the laboratory, we connected a variable resistor to the proposed circuit to form a left-hand side loop of the circuit shown in Figure 4, in which the resistance, as represented by Rn, is the value to be estimated. Due to the simplicity of the single loop circuit, using Kirchhoff's current law, Rn can be evaluated in a straightforward manner, as follows:

Figure 6. Estimation of Rp.

50 New Trends in Electrical Vehicle Powertrains

Figure 7. Estimation of Rn.

Figure 8. Experimental results. (a) Estimated parameters θb<sup>1</sup> and θb2. (b) Estimated resistances of the two models.

$$R\_n = \frac{R}{\frac{V\_G}{V\_N} - 1} \tag{13}$$

EV electric vehicle

SoC state of charge SoH state of health

Author details

References

MCU micro-control unit

PWM pulse-width modulation

UPS uninterrupted power supply

Yi-Hsien Chiang and Wu-Yang Sean\*

ment and Instrument. 2009;23(11):8

Measurement Technology. 2009;32(2):76-78

Technical Paper; 2013

\*Address all correspondence to: wuyangsean@gmail.com

Transactions on Electrical Insulation. 1990;25(6):1097-1103

Department of Environmental Engineering, Chung Yuan Christian University, Taiwan

[1] Stimper K. Physical fundamentals of insulation design for low-voltage equipment. IEEE

Adaptive Control for Estimating Insulation Resistance of High-Voltage Battery System in Electric Vehicles

http://dx.doi.org/10.5772/intechopen.75468

53

[2] Baldwin T, Renovich F, Saunders LF. Direction ground-fault indicator for high-resistance grounded systems. IEEE Transactions on Industry Applications. 2003;39(2):325-332 [3] Huang H-H, Quan C, Huang J. Development of distributed DC grounding detecting system based on differential current detecting method. Journal of Electronic Measure-

[4] Li L-W, Liu X-F, Liu B. Distributed on-line grounding monitoring system for DC system

[5] Guo H-Y, Jiang J-C, Wen J-P, Wang J-Y. New method of insulation detection for electrical vehicle. Journal of Electronic Measurement and Instrument. 2011;25(3):253-257

[6] Kota O, Balasubramanian G. High voltage safety concepts for power electronic units. SAE

[7] Li J-X, Fan Y-Q, Jiang J-C, Chen H. An approach to on-line monitoring on insulation

[8] Li L, Jiang J-C. Research on battery insulation detection for electric vehicle. Electronic

based on field bus. Electric Power Automation Equipment. 2006;26(12):55-58

resistance in electric vehicle. Automotive Engineering. 2006;28(10):884-887

PRBS pseudo random binary sequence

On the other hand, we modify the estimated model to yield:

Rn <sup>¼</sup> <sup>θ</sup>b<sup>1</sup> θb2 R: (14)

The experimental results are shown in Figure 8. In Figure 8(a), the two estimated parameters converge after 25 s. In Figure 8(b), we depict the online estimated resistances based on the straight evaluation of Eq. (8) and the proposed method in Eq. (9). It is realized that the estimated value by using the straight evaluation varies roughly 10% between its maximum and minimum values. This may be due to either measurement noise or the dynamic uncertainty of the parasitic capacitance. However, the proposed method shows a steadier and more exact estimation after the convergence of the model parameters.

#### 6. Conclusions

In this chapter, to improve existing techniques for enhancing the safety and reliability of highvoltage systems, we proposed a new insulation resistance online monitoring method for EV high-voltage DC lines, which takes into account the parasitic capacitance effect. The estimation scheme based on an adaptive control algorithm guarantees the asymptotical convergence of the parameters in the circuit model. Hence, as demonstrated in our simulation and experimental results, this method can steadily and accurately track the insulation resistance even when the parasitic capacitance is unknown. Due to the simplicity of the proposed algorithm and circuit, they can be easily implemented via electronic circuit design in real cases. According to the results, the estimated Rp and Rn converge to the actual value in 50 s. The relative error between actual and estimated values are both less than 1%. With respect to two degradation cases that sequentially occur on the negative and positive terminals, it requires more time for convergence, i.e., 20 and 240 s for the degradations occurring on the negative and positive sides, respectively.

#### Nomenclature



EV electric vehicle

Rn <sup>¼</sup> <sup>R</sup> VG

Rn <sup>¼</sup> <sup>θ</sup>b<sup>1</sup> θb2

The experimental results are shown in Figure 8. In Figure 8(a), the two estimated parameters converge after 25 s. In Figure 8(b), we depict the online estimated resistances based on the straight evaluation of Eq. (8) and the proposed method in Eq. (9). It is realized that the estimated value by using the straight evaluation varies roughly 10% between its maximum and minimum values. This may be due to either measurement noise or the dynamic uncertainty of the parasitic capacitance. However, the proposed method shows a steadier and more

In this chapter, to improve existing techniques for enhancing the safety and reliability of highvoltage systems, we proposed a new insulation resistance online monitoring method for EV high-voltage DC lines, which takes into account the parasitic capacitance effect. The estimation scheme based on an adaptive control algorithm guarantees the asymptotical convergence of the parameters in the circuit model. Hence, as demonstrated in our simulation and experimental results, this method can steadily and accurately track the insulation resistance even when the parasitic capacitance is unknown. Due to the simplicity of the proposed algorithm and circuit, they can be easily implemented via electronic circuit design in real cases. According to the results, the estimated Rp and Rn converge to the actual value in 50 s. The relative error between actual and estimated values are both less than 1%. With respect to two degradation cases that sequentially occur on the negative and positive terminals, it requires more time for convergence, i.e., 20 and 240 s for the degradations occurring on the negative and positive

On the other hand, we modify the estimated model to yield:

52 New Trends in Electrical Vehicle Powertrains

exact estimation after the convergence of the model parameters.

6. Conclusions

sides, respectively.

Nomenclature

AC alternating current

DC direct current

BMS battery management system

ECM equivalent-circuit model

DC/DC conversion of a DC source from one voltage level to another

VN � <sup>1</sup> (13)

R: (14)


## Author details

Yi-Hsien Chiang and Wu-Yang Sean\*

\*Address all correspondence to: wuyangsean@gmail.com

Department of Environmental Engineering, Chung Yuan Christian University, Taiwan

#### References


[9] Pan L, Jiang J, Li J. Development of intelligent passive grounding detection device for electric vehicle. Electrical Drive Automation. 2003;25(4):47-48

**Chapter 4**

Provisional chapter

**Estimation Techniques for State of Charge in Battery**

DOI: 10.5772/intechopen.76230

The battery state-of-charge estimation is essential in automotive industry for a successful marketing of both electric and hybrid electric vehicles. Furthermore, the state-of-charge of a battery is a critical condition parameter for battery management system. In this research work we share from the experience accumulated in control systems applications field some preliminary results, especially in modeling and state estimation techniques, very useful for state-of-charge estimation of the rechargeable batteries with different chemistries. We investigate the design and the effectiveness of three nonlinear state-of-charge estimators implemented in a real-time MATLAB environment for a particular Li-Ion battery, such as an Unscented Kalman Filter, Particle filter, and a nonlinear observer. Finally, the target to be accomplished is to find the most suitable estimator in terms of

Keywords: Li-Ion battery state-of-charge, state estimation, unscented Kalman filter estimator, particle filter estimator, nonlinear observer estimator, battery management

We are currently seeing a significant increase in global environmental pollution, with immediate repercussions on air, water and soil quality. More precisely, especially in the developed

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

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

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

Estimation Techniques for State of Charge in Battery

**Management Systems on Board of Hybrid Electric**

Management Systems on Board of Hybrid Electric

**Vehicles Implemented in a Real-Time MATLAB/**

Vehicles Implemented in a Real-Time MATLAB/

Roxana-Elena Tudoroiu, Mohammed Zaheeruddin,

Roxana-Elena Tudoroiu, Mohammed Zaheeruddin,

**SIMULINK Environment**

SIMULINK Environment

http://dx.doi.org/10.5772/intechopen.76230

Abstract

system

1. Introduction

Sorin-Mihai Radu and Nicolae Tudoroiu

Sorin-Mihai Radu and Nicolae Tudoroiu

performance accuracy and robustness.

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter


#### **Estimation Techniques for State of Charge in Battery Management Systems on Board of Hybrid Electric Vehicles Implemented in a Real-Time MATLAB/ SIMULINK Environment** Estimation Techniques for State of Charge in Battery Management Systems on Board of Hybrid Electric Vehicles Implemented in a Real-Time MATLAB/ SIMULINK Environment

DOI: 10.5772/intechopen.76230

Roxana-Elena Tudoroiu, Mohammed Zaheeruddin, Sorin-Mihai Radu and Nicolae Tudoroiu Roxana-Elena Tudoroiu, Mohammed Zaheeruddin, Sorin-Mihai Radu and Nicolae Tudoroiu

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

http://dx.doi.org/10.5772/intechopen.76230

Abstract

[9] Pan L, Jiang J, Li J. Development of intelligent passive grounding detection device for

[10] Wu Z-J, Wang L-F. A novel insulation resistance monitoring device for hybrid electric

[11] Morimoto N. Ground-fault resistance measurement circuit and ground-fault detection

[12] Onnerud P, Linna JR, Warner J, Souza C. Safety and performance optimized controls for

[13] Chiang Y-H, Sean W-Y, Huang C-Y, Hsieh L-HC. Adaptive control for estimating insulation resistance of high-voltage battery system in electric vehicles. Environmental Progress

[14] Sottile TN, Tripathi AK. Best practices for implementing high-resistance grounding in mine power systems. IEEE Transactions on Industry Applications. 2015;51(6):5254-5260.

vehicle. In: IEEE Conference on Vehicle Power and Propulsion (VPPC); 2008

large scale electric vehicle battery systems. US Patent No. 20110049977A1

electric vehicle. Electrical Drive Automation. 2003;25(4):47-48

circuit. US Patent No. 7560935B2

54 New Trends in Electrical Vehicle Powertrains

DOI: 10.1109/TIA.2015.2420632

& Sustainable Energy. 2017;36(6):1882-1887

The battery state-of-charge estimation is essential in automotive industry for a successful marketing of both electric and hybrid electric vehicles. Furthermore, the state-of-charge of a battery is a critical condition parameter for battery management system. In this research work we share from the experience accumulated in control systems applications field some preliminary results, especially in modeling and state estimation techniques, very useful for state-of-charge estimation of the rechargeable batteries with different chemistries. We investigate the design and the effectiveness of three nonlinear state-of-charge estimators implemented in a real-time MATLAB environment for a particular Li-Ion battery, such as an Unscented Kalman Filter, Particle filter, and a nonlinear observer. Finally, the target to be accomplished is to find the most suitable estimator in terms of performance accuracy and robustness.

Keywords: Li-Ion battery state-of-charge, state estimation, unscented Kalman filter estimator, particle filter estimator, nonlinear observer estimator, battery management system

### 1. Introduction

We are currently seeing a significant increase in global environmental pollution, with immediate repercussions on air, water and soil quality. More precisely, especially in the developed

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

countries around the world the environmental pollution has reached scary limits. Related to this it is worth to mention the presence of a significant amount of hydrocarbons pollutants (benzene, toluene and xylene) in the emissions of the vehicles equipped with gasoline or diesel engines that are characterized by a variable toxicity depending on the chemical composition of the exhaust gases. Furthermore, these toxic substances are propagated through the air from one region of the world to another one and surrounds countries and continents becoming a global phenomenon, consisting of irreversible pollution of water, air and soil at the planetary scale.

recording continuously the main parameters of the Li-Ion battery and performing an accurate estimation of its state-of-charge (SOC). An accurate SOC estimation is a vital operation to be performed by the BMS of the HEV in order to prevent the dangerous situations when the battery is over-charged or over-discharged, and to improve considerably the battery performance [1]. More precisely, the battery SOC is an inner state of a battery that can be defined as the available capacity of a battery, as a percentage of its rated capacity [1–3]. Its estimation is an essential operational condition parameter for battery management system (BMS) but it cannot be measured directly [1]. The estimation of Li-Ion battery SOC value is based on the measurable data set of the battery parameters, mainly the current, voltage, and temperature by using several estimation strategies, implemented in real-time MATLAB/SIMULINK platform that includes many real-time features [3–11]. All SOC estimation strategies are model-based, and can be grouped in Kalman Filter (standard, extended, unscented, particles filter), as those developed and implemented in real-time in [3–11], linear, nonlinear and sliding mode observers estimators, including also fuzzy improved versions, well documented in [1, 8–10]. The environmental impact is a key issue on the enhancing the battery technologies, as is

Estimation Techniques for State of Charge in Battery Management Systems on Board of Hybrid Electric Vehicles…

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57

Definitely, the selection criteria of the specific chemistry battery to be integrated in BMS structure of a HEV are the cost, the specific power and energy, cycle life, and the presence of poisonous heavy metals [2, 12]. A complete literature review on life cycle assessment (LCA) of HEVs since 1998 until 2013 has been conducted in [12]. In our research we are only focused on the technical aspects, such as battery modeling and developing the most suitable estimation

The remainder of this chapter is organized as follows. In Section 2, the widely-used 2RC-series cells Li-Ion battery equivalent model circuit (EMC) is introduced and the state space equations are derived. In Section 3, is proposed for design and implementation in real-time three nonlinear estimators, namely an Unscented Kalman Filter (UKF), Particle Filter (PF), and a

The simulation results and the performance analysis of the proposed estimators are presented

2. The continuous and the discrete time state-space Li-Ion battery model

In this section we introduce a generic model capable to describe accurately the dynamics of Li-Ion battery, based on the same set of first-order differential equations in a state-space representation. For simulation purpose, a specific Li-Ion battery model is considered to prove the effectiveness of the proposed SOC estimation strategies. This model can be obtained from the generic model by changing only the values of the model parameters in state-space equations. For our case study we choose the widely-used 2RC - series cells Li-Ion battery equivalent

model circuit (EMC) as model-based support, well documented in [1, 8, 11].

mentioned in [12].

representation

techniques of battery SOC.

nonlinear observer estimator (NOE).

in Section 4. Section 5 concludes the book chapter.

Therefore, the need to conceive and implement new environmental conservation strategies at the global scale is required. Also, a changing in the thinking of the people about a significant reduction in energy consumption without sacrificing the comfort is crucial. In these circumstances there is a real hope that with the current technology available could stop the global destruction of the environment. Moreover, the new strategies based on electrical energy consumption assure a sustainable development of each community are critical to achieve a clean and efficient urban or rural transportation. As a viable solution to the global energy shortage and growing environmental pollution is the use of the electric vehicles (EVs) [1]. Nowadays, the electric vehicles (EVs) including hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and pure battery electric vehicles (BEVs) are gaining popularity in automotive industry and will dominate soon the clean vehicle market [2]. Related, in [2] is mentioned also that by 2020, it is expected that "more than half of new vehicle sales will likely be EV models", with the batteries playing "the key and the enabling technology to this revolutionary change". They are conceived "to handle high power (up to a hundred kW) and high energy capacity (up to tens of kWh) within a limited space and weight and at an affordable price" [2]. The most advanced and promising battery technologies existing in EVs manufacturing automotive industry are the nickel-metal hydride (NiMH), lithium-ion (Li-Ion) and nickel-cadmium (NiCad) batteries considered as the most suitable for HEVs/PHEVs/EVs all over the world.

They have a great potential to reduce greenhouse and other exhaust gas emissions, and require extensive research efforts and huge investments [2].

Nevertheless, amongst them the most promising power source with a great potential to be developed and to get a wide application in the future on the EVs market is the Li-Ion battery recommended by its light weight, high energy density, tiny memory effect, and relatively low self-discharge compared to its strong competitors, namely Ni-Cad and Ni-MH batteries, as is mentioned in [1, 2].

Additionally, the newest Li-Ion batteries are safer and less toxic than the same batteries in competition. Due to the diversity and the complexity of EVs field we limit our case study only to HEVs applications since we have got some research experience in modeling, control and the estimation strategies related to this field.

Therefore, one of the main objectives of this research work is to disseminate the most relevant results obtained until now in this area and to share some interesting ideas with our readers. The Li-Ion battery is a main component integrated in the battery management system (BMS) of a HEV that is responsible for "improving the battery performance, prolonging battery life, and ensuring its safety", as is mentioned in [1, 2]. This desideratum is achieved by the BMS through recording continuously the main parameters of the Li-Ion battery and performing an accurate estimation of its state-of-charge (SOC). An accurate SOC estimation is a vital operation to be performed by the BMS of the HEV in order to prevent the dangerous situations when the battery is over-charged or over-discharged, and to improve considerably the battery performance [1]. More precisely, the battery SOC is an inner state of a battery that can be defined as the available capacity of a battery, as a percentage of its rated capacity [1–3]. Its estimation is an essential operational condition parameter for battery management system (BMS) but it cannot be measured directly [1]. The estimation of Li-Ion battery SOC value is based on the measurable data set of the battery parameters, mainly the current, voltage, and temperature by using several estimation strategies, implemented in real-time MATLAB/SIMULINK platform that includes many real-time features [3–11]. All SOC estimation strategies are model-based, and can be grouped in Kalman Filter (standard, extended, unscented, particles filter), as those developed and implemented in real-time in [3–11], linear, nonlinear and sliding mode observers estimators, including also fuzzy improved versions, well documented in [1, 8–10]. The environmental impact is a key issue on the enhancing the battery technologies, as is mentioned in [12].

countries around the world the environmental pollution has reached scary limits. Related to this it is worth to mention the presence of a significant amount of hydrocarbons pollutants (benzene, toluene and xylene) in the emissions of the vehicles equipped with gasoline or diesel engines that are characterized by a variable toxicity depending on the chemical composition of the exhaust gases. Furthermore, these toxic substances are propagated through the air from one region of the world to another one and surrounds countries and continents becoming a global phenomenon,

Therefore, the need to conceive and implement new environmental conservation strategies at the global scale is required. Also, a changing in the thinking of the people about a significant reduction in energy consumption without sacrificing the comfort is crucial. In these circumstances there is a real hope that with the current technology available could stop the global destruction of the environment. Moreover, the new strategies based on electrical energy consumption assure a sustainable development of each community are critical to achieve a clean and efficient urban or rural transportation. As a viable solution to the global energy shortage and growing environmental pollution is the use of the electric vehicles (EVs) [1]. Nowadays, the electric vehicles (EVs) including hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and pure battery electric vehicles (BEVs) are gaining popularity in automotive industry and will dominate soon the clean vehicle market [2]. Related, in [2] is mentioned also that by 2020, it is expected that "more than half of new vehicle sales will likely be EV models", with the batteries playing "the key and the enabling technology to this revolutionary change". They are conceived "to handle high power (up to a hundred kW) and high energy capacity (up to tens of kWh) within a limited space and weight and at an affordable price" [2]. The most advanced and promising battery technologies existing in EVs manufacturing automotive industry are the nickel-metal hydride (NiMH), lithium-ion (Li-Ion) and nickel-cadmium (NiCad) batteries con-

They have a great potential to reduce greenhouse and other exhaust gas emissions, and require

Nevertheless, amongst them the most promising power source with a great potential to be developed and to get a wide application in the future on the EVs market is the Li-Ion battery recommended by its light weight, high energy density, tiny memory effect, and relatively low self-discharge compared to its strong competitors, namely Ni-Cad and Ni-MH batteries, as is

Additionally, the newest Li-Ion batteries are safer and less toxic than the same batteries in competition. Due to the diversity and the complexity of EVs field we limit our case study only to HEVs applications since we have got some research experience in modeling, control and the

Therefore, one of the main objectives of this research work is to disseminate the most relevant results obtained until now in this area and to share some interesting ideas with our readers. The Li-Ion battery is a main component integrated in the battery management system (BMS) of a HEV that is responsible for "improving the battery performance, prolonging battery life, and ensuring its safety", as is mentioned in [1, 2]. This desideratum is achieved by the BMS through

consisting of irreversible pollution of water, air and soil at the planetary scale.

sidered as the most suitable for HEVs/PHEVs/EVs all over the world.

extensive research efforts and huge investments [2].

estimation strategies related to this field.

mentioned in [1, 2].

56 New Trends in Electrical Vehicle Powertrains

Definitely, the selection criteria of the specific chemistry battery to be integrated in BMS structure of a HEV are the cost, the specific power and energy, cycle life, and the presence of poisonous heavy metals [2, 12]. A complete literature review on life cycle assessment (LCA) of HEVs since 1998 until 2013 has been conducted in [12]. In our research we are only focused on the technical aspects, such as battery modeling and developing the most suitable estimation techniques of battery SOC.

The remainder of this chapter is organized as follows. In Section 2, the widely-used 2RC-series cells Li-Ion battery equivalent model circuit (EMC) is introduced and the state space equations are derived. In Section 3, is proposed for design and implementation in real-time three nonlinear estimators, namely an Unscented Kalman Filter (UKF), Particle Filter (PF), and a nonlinear observer estimator (NOE).

The simulation results and the performance analysis of the proposed estimators are presented in Section 4. Section 5 concludes the book chapter.
