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State of Charge Estimation of Lithium Battery Based on Improved Correntropy Extended Kalman Filter

Author

Listed:
  • Jiandong Duan

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Peng Wang

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Wentao Ma

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Xinyu Qiu

    (NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China)

  • Xuan Tian

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Shuai Fang

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

Abstract

State of charge (SOC) estimation plays a crucial role in battery management systems. Among all the existing SOC estimation approaches, the model-driven extended Kalman filter (EKF) has been widely utilized to estimate SOC due to its simple implementation and nonlinear property. However, the traditional EKF derived from the mean square error (MSE) loss is sensitive to non-Gaussian noise which especially exists in practice, thus the SOC estimation based on the traditional EKF may result in undesirable performance. Hence, a novel robust EKF method with correntropy loss is employed to perform SOC estimation to improve the accuracy under non-Gaussian environments firstly. Secondly, a novel robust EKF, called C-WLS-EKF, is developed by combining the advantages of correntropy and weighted least squares (WLS) to improve the digital stability of the correntropy EKF (C-EKF). In addition, the convergence of the proposed algorithm is verified by the Cramér–Rao low bound. Finally, a C-WLS-EKF method based on an equivalent circuit model is designed to perform SOC estimation. The experiment results clarify that the SOC estimation error in terms of the MSE via the proposed C-WLS-EKF method can efficiently be reduced from 1.361% to 0.512% under non-Gaussian noise conditions.

Suggested Citation

  • Jiandong Duan & Peng Wang & Wentao Ma & Xinyu Qiu & Xuan Tian & Shuai Fang, 2020. "State of Charge Estimation of Lithium Battery Based on Improved Correntropy Extended Kalman Filter," Energies, MDPI, vol. 13(16), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:16:p:4197-:d:398838
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    References listed on IDEAS

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    Cited by:

    1. Deng Ma & Kai Gao & Yutao Mu & Ziqi Wei & Ronghua Du, 2022. "An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error," Energies, MDPI, vol. 15(10), pages 1-18, May.
    2. Guo, Shanshan & Ma, Liang, 2023. "A comparative study of different deep learning algorithms for lithium-ion batteries on state-of-charge estimation," Energy, Elsevier, vol. 263(PC).
    3. Xu, Cheng & Zhang, E & Jiang, Kai & Wang, Kangli, 2022. "Dual fuzzy-based adaptive extended Kalman filter for state of charge estimation of liquid metal battery," Applied Energy, Elsevier, vol. 327(C).
    4. Li, Kangqun & Zhou, Fei & Chen, Xing & Yang, Wen & Shen, Junjie & Song, Zebin, 2023. "State-of-charge estimation combination algorithm for lithium-ion batteries with Frobenius-norm-based QR decomposition modified adaptive cubature Kalman filter and H-infinity filter based on electro-th," Energy, Elsevier, vol. 263(PC).
    5. Hao Wang & Yanping Zheng & Yang Yu, 2021. "Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter," Mathematics, MDPI, vol. 9(15), pages 1-12, July.

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