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On-Line Parameter Identification and SOC Estimation for Lithium-Ion Batteries Based on Improved Sage–Husa Adaptive EKF

Author

Listed:
  • Xuan Tang

    (School of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, China
    CRRC Times Electric Vehicle Co., Ltd., Zhuzhou 412007, China)

  • Hai Huang

    (School of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, China)

  • Xiongwu Zhong

    (CRRC Times Electric Vehicle Co., Ltd., Zhuzhou 412007, China)

  • Kunjun Wang

    (CRRC Times Electric Vehicle Co., Ltd., Zhuzhou 412007, China)

  • Fang Li

    (School of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, China)

  • Youhang Zhou

    (School of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, China)

  • Haifeng Dai

    (Clean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, China
    School of Automotive Studies, Tongji University, Shanghai 201804, China)

Abstract

For the Battery Management System (BMS) to manage and control the battery, State of Charge (SOC) is an important battery performance indicator. In order to identify the parameters of the LiFePO 4 battery, this paper employs the forgetting factor recursive least squares (FFRLS) method, which considers the computational volume and model correctness, to determine the parameters of the LiFePO 4 battery. On this basis, the two resistor-capacitor equivalent circuit model is selected for estimating the SOC of the Li-ion battery by combining the extended Kalman filter (EKF) with the Sage–Husa adaptive algorithm. The positivity is improved by modifying the system noise estimation matrix. The paper concludes with a MATLAB 2016B simulation, which serves to validate the SOC estimation algorithm. The results demonstrate that, in comparison to the conventional EKF, the enhanced EKF exhibits superior estimation precision and resilience to interference, along with enhanced convergence during the estimation process.

Suggested Citation

  • Xuan Tang & Hai Huang & Xiongwu Zhong & Kunjun Wang & Fang Li & Youhang Zhou & Haifeng Dai, 2024. "On-Line Parameter Identification and SOC Estimation for Lithium-Ion Batteries Based on Improved Sage–Husa Adaptive EKF," Energies, MDPI, vol. 17(22), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5722-:d:1521840
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    References listed on IDEAS

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    1. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    2. Li, Mingheng, 2017. "Li-ion dynamics and state of charge estimation," Renewable Energy, Elsevier, vol. 100(C), pages 44-52.
    3. Zhao, Xinze & Sun, Bingxiang & Zhang, Weige & He, Xitian & Ma, Shichang & Zhang, Junwei & Liu, Xiaopeng, 2024. "Error theory study on EKF-based SOC and effective error estimation strategy for Li-ion batteries," Applied Energy, Elsevier, vol. 353(PA).
    4. Wang, Yujie & Chen, Zonghai, 2020. "A framework for state-of-charge and remaining discharge time prediction using unscented particle filter," Applied Energy, Elsevier, vol. 260(C).
    5. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
    6. Zhu, Qiao & Xu, Mengen & Liu, Weiqun & Zheng, Mengqian, 2019. "A state of charge estimation method for lithium-ion batteries based on fractional order adaptive extended kalman filter," Energy, Elsevier, vol. 187(C).
    7. Wu, Muyao & Wang, Li & Wu, Ji, 2023. "State of health estimation of the LiFePO4 power battery based on the forgetting factor recursive Total Least Squares and the temperature correction," Energy, Elsevier, vol. 282(C).
    8. Hu, Chao & Youn, Byeng D. & Chung, Jaesik, 2012. "A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation," Applied Energy, Elsevier, vol. 92(C), pages 694-704.
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