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Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter

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  • Zheng, Linfeng
  • Zhu, Jianguo
  • Wang, Guoxiu
  • Lu, Dylan Dah-Chuan
  • He, Tingting

Abstract

Accurate battery state of charge (SOC) estimation can contribute to safe and reliable utilization of the battery. However, commonly used battery model-based SOC estimation methods suffer from the lack of a universal battery model for cells in a battery pack since the model parameters of each cell are inevitably different from each other and variable with battery aging, leading to difficulties in promoting the model-based methods for real applications. To solve this problem, a differential voltage (DV) analysis based universal battery model and two associated SOC estimation algorithms using extended Kalman filter (EKF) and particle filter (PF), respectively, are proposed in this paper. By means of a natural cubic interpolation approach, a battery SOC-DV model is firstly derived from the SOC based DV curves of various cells at different aging levels. A novel battery model-based scheme is then proposed to incorporate the SOC-DV model for the estimation. The robustness of the proposed approaches against different cell aging levels is evaluated, and the promising SOC estimates with the maximum absolute error of 1.75% and the root mean square error of less than 1.10% can be achieved.

Suggested Citation

  • Zheng, Linfeng & Zhu, Jianguo & Wang, Guoxiu & Lu, Dylan Dah-Chuan & He, Tingting, 2018. "Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter," Energy, Elsevier, vol. 158(C), pages 1028-1037.
    • Handle: RePEc:eee:energy:v:158:y:2018:i:c:p:1028-1037
    DOI: 10.1016/j.energy.2018.06.113
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    6. Zhang, Zhendong & Kong, Xiangdong & Zheng, Yuejiu & Zhou, Long & Lai, Xin, 2019. "Real-time diagnosis of micro-short circuit for Li-ion batteries utilizing low-pass filters," Energy, Elsevier, vol. 166(C), pages 1013-1024.
    7. Ma, Mina & Wang, Yu & Duan, Qiangling & Wu, Tangqin & Sun, Jinhua & Wang, Qingsong, 2018. "Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis," Energy, Elsevier, vol. 164(C), pages 745-756.
    8. Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).
    9. Yao, Lei & Fang, Zhanpeng & Xiao, Yanqiu & Hou, Junjian & Fu, Zhijun, 2021. "An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine," Energy, Elsevier, vol. 214(C).
    10. Qinghe Liu & Shouzhi Liu & Haiwei Liu & Hao Qi & Conggan Ma & Lijun Zhao, 2019. "Evaluation of LFP Battery SOC Estimation Using Auxiliary Particle Filter," Energies, MDPI, vol. 12(11), pages 1-13, May.
    11. Zheng Chen & Jiapeng Xiao & Xing Shu & Shiquan Shen & Jiangwei Shen & Yonggang Liu, 2020. "Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan," Energies, MDPI, vol. 13(6), pages 1-15, March.
    12. Kong, Jin-zhen & Yang, Fangfang & Zhang, Xi & Pan, Ershun & Peng, Zhike & Wang, Dong, 2021. "Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries," Energy, Elsevier, vol. 223(C).
    13. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
    14. Wang, Shun-Li & Fernandez, Carlos & Zou, Chuan-Yun & Yu, Chun-Mei & Chen, Lei & Zhang, Li, 2019. "A comprehensive working state monitoring method for power battery packs considering state of balance and aging correction," Energy, Elsevier, vol. 171(C), pages 444-455.
    15. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    16. Victor Pizarro-Carmona & Marcelo Cortés-Carmona & Rodrigo Palma-Behnke & Williams Calderón-Muñoz & Marcos E. Orchard & Pablo A. Estévez, 2019. "An Optimized Impedance Model for the Estimation of the State-of-Charge of a Li-Ion Cell: The Case of a LiFePO 4 (ANR26650)," Energies, MDPI, vol. 12(4), pages 1-16, February.
    17. Tian, Yu & Lin, Cheng & Li, Hailong & Du, Jiuyu & Xiong, Rui, 2021. "Detecting undesired lithium plating on anodes for lithium-ion batteries – A review on the in-situ methods," Applied Energy, Elsevier, vol. 300(C).
    18. Shu, Xing & Li, Guang & Shen, Jiangwei & Lei, Zhenzhen & Chen, Zheng & Liu, Yonggang, 2020. "An adaptive multi-state estimation algorithm for lithium-ion batteries incorporating temperature compensation," Energy, Elsevier, vol. 207(C).
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    20. Xie, Yanxin & Wang, Shunli & Zhang, Gexiang & Fan, Yongcun & Fernandez, Carlos & Blaabjerg, Frede, 2023. "Optimized multi-hidden layer long short-term memory modeling and suboptimal fading extended Kalman filtering strategies for the synthetic state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 336(C).
    21. Xiong, Rui & Duan, Yanzhou & Zhang, Kaixuan & Lin, Da & Tian, Jinpeng & Chen, Cheng, 2023. "State-of-charge estimation for onboard LiFePO4 batteries with adaptive state update in specific open-circuit-voltage ranges," Applied Energy, Elsevier, vol. 349(C).

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