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State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model

Author

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  • Shifei Yuan

    (National Engineering Laboratory for Automotive Electronic Control Technology, Shanghai Jiao Tong University, 200240 Shanghai, China)

  • Hongjie Wu

    (National Engineering Laboratory for Automotive Electronic Control Technology, Shanghai Jiao Tong University, 200240 Shanghai, China)

  • Chengliang Yin

    (National Engineering Laboratory for Automotive Electronic Control Technology, Shanghai Jiao Tong University, 200240 Shanghai, China)

Abstract

State of charge (SOC) is a critical factor to guarantee that a battery system is operating in a safe and reliable manner. Many uncertainties and noises, such as fluctuating current, sensor measurement accuracy and bias, temperature effects, calibration errors or even sensor failure, etc. pose a challenge to the accurate estimation of SOC in real applications. This paper adds two contributions to the existing literature. First, the auto regressive exogenous (ARX) model is proposed here to simulate the battery nonlinear dynamics. Due to its discrete form and ease of implemention, this straightforward approach could be more suitable for real applications. Second, its order selection principle and parameter identification method is illustrated in detail in this paper. The hybrid pulse power characterization (HPPC) cycles are implemented on the 60AH LiFePO 4 battery module for the model identification and validation. Based on the proposed ARX model, SOC estimation is pursued using the extended Kalman filter. Evaluation of the adaptability of the battery models and robustness of the SOC estimation algorithm are also verified. The results indicate that the SOC estimation method using the Kalman filter based on the ARX model shows great performance. It increases the model output voltage accuracy, thereby having the potential to be used in real applications, such as EVs and HEVs.

Suggested Citation

  • Shifei Yuan & Hongjie Wu & Chengliang Yin, 2013. "State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model," Energies, MDPI, vol. 6(1), pages 1-27, January.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:1:p:444-470:d:22920
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    References listed on IDEAS

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    2. Yong Tian & Chaoren Chen & Bizhong Xia & Wei Sun & Zhihui Xu & Weiwei Zheng, 2014. "An Adaptive Gain Nonlinear Observer for State of Charge Estimation of Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 7(9), pages 1-18, September.
    3. Kiarash Movassagh & Arif Raihan & Balakumar Balasingam & Krishna Pattipati, 2021. "A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries," Energies, MDPI, vol. 14(14), pages 1-33, July.
    4. Shifei Yuan & Hongjie Wu & Xuerui Ma & Chengliang Yin, 2015. "Stability Analysis for Li-Ion Battery Model Parameters and State of Charge Estimation by Measurement Uncertainty Consideration," Energies, MDPI, vol. 8(8), pages 1-23, July.
    5. Chen, Lin & Lin, Weilong & Li, Junzi & Tian, Binbin & Pan, Haihong, 2016. "Prediction of lithium-ion battery capacity with metabolic grey model," Energy, Elsevier, vol. 106(C), pages 662-672.
    6. Miquel Martí-Florences & Andreu Cecilia & Ramon Costa-Castelló, 2023. "Modelling and Estimation in Lithium-Ion Batteries: A Literature Review," Energies, MDPI, vol. 16(19), pages 1-36, September.
    7. Groenewald, Jakobus & Grandjean, Thomas & Marco, James, 2017. "Accelerated energy capacity measurement of lithium-ion cells to support future circular economy strategies for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 98-111.
    8. Xia, Bizhong & Cui, Deyu & Sun, Zhen & Lao, Zizhou & Zhang, Ruifeng & Wang, Wei & Sun, Wei & Lai, Yongzhi & Wang, Mingwang, 2018. "State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network," Energy, Elsevier, vol. 153(C), pages 694-705.
    9. Ines Baccouche & Sabeur Jemmali & Bilal Manai & Noshin Omar & Najoua Essoukri Ben Amara, 2017. "Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter," Energies, MDPI, vol. 10(6), pages 1-22, May.
    10. Yigeng Huangfu & Jiani Xu & Dongdong Zhao & Yuntian Liu & Fei Gao, 2018. "A Novel Battery State of Charge Estimation Method Based on a Super-Twisting Sliding Mode Observer," Energies, MDPI, vol. 11(5), pages 1-21, May.
    11. Zhichao He & Geng Yang & Languang Lu, 2016. "A Parameter Identification Method for Dynamics of Lithium Iron Phosphate Batteries Based on Step-Change Current Curves and Constant Current Curves," Energies, MDPI, vol. 9(6), pages 1-24, June.
    12. Yun Zhang & Yunlong Shang & Naxin Cui & Chenghui Zhang, 2017. "Parameters Identification and Sensitive Characteristics Analysis for Lithium-Ion Batteries of Electric Vehicles," Energies, MDPI, vol. 11(1), pages 1-15, December.
    13. Yin Hua & Min Xu & Mian Li & Chengbin Ma & Chen Zhao, 2015. "Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles," Energies, MDPI, vol. 8(5), pages 1-22, April.
    14. Bizhong Xia & Haiqing Wang & Yong Tian & Mingwang Wang & Wei Sun & Zhihui Xu, 2015. "State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter," Energies, MDPI, vol. 8(6), pages 1-21, June.
    15. Bizhong Xia & Wenhui Zheng & Ruifeng Zhang & Zizhou Lao & Zhen Sun, 2017. "A Novel Observer for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles Based on a Second-Order Equivalent Circuit Model," Energies, MDPI, vol. 10(8), pages 1-20, August.
    16. Van Quan Dao & Minh-Chau Dinh & Chang Soon Kim & Minwon Park & Chil-Hoon Doh & Jeong Hyo Bae & Myung-Kwan Lee & Jianyong Liu & Zhiguo Bai, 2021. "Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network," Energies, MDPI, vol. 14(9), pages 1-20, May.
    17. Zhihao Yu & Ruituo Huai & Linjing Xiao, 2015. "State-of-Charge Estimation for Lithium-Ion Batteries Using a Kalman Filter Based on Local Linearization," Energies, MDPI, vol. 8(8), pages 1-20, July.
    18. Ngoc-Tham Tran & Abdul Basit Khan & Woojin Choi, 2017. "State of Charge and State of Health Estimation of AGM VRLA Batteries by Employing a Dual Extended Kalman Filter and an ARX Model for Online Parameter Estimation," Energies, MDPI, vol. 10(1), pages 1-18, January.
    19. Yuechen Liu & Linjing Zhang & Jiuchun Jiang & Shaoyuan Wei & Sijia Liu & Weige Zhang, 2017. "A Data-Driven Learning-Based Continuous-Time Estimation and Simulation Method for Energy Efficiency and Coulombic Efficiency of Lithium Ion Batteries," Energies, MDPI, vol. 10(5), pages 1-15, April.
    20. Yong Tian & Bizhong Xia & Mingwang Wang & Wei Sun & Zhihui Xu, 2014. "Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 7(12), pages 1-19, December.
    21. Deyu Cui & Bizhong Xia & Ruifeng Zhang & Zhen Sun & Zizhou Lao & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang, 2018. "A Novel Intelligent Method for the State of Charge Estimation of Lithium-Ion Batteries Using a Discrete Wavelet Transform-Based Wavelet Neural Network," Energies, MDPI, vol. 11(4), pages 1-18, April.
    22. Daniele Gallo & Carmine Landi & Mario Luiso & Rosario Morello, 2013. "Optimization of Experimental Model Parameter Identification for Energy Storage Systems," Energies, MDPI, vol. 6(9), pages 1-19, September.
    23. Zhibing Zeng & Jindong Tian & Dong Li & Yong Tian, 2018. "An Online State of Charge Estimation Algorithm for Lithium-Ion Batteries Using an Improved Adaptive Cubature Kalman Filter," Energies, MDPI, vol. 11(1), pages 1-16, January.

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