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A Variational Bayesian and Huber-Based Robust Square Root Cubature Kalman Filter for Lithium-Ion Battery State of Charge Estimation

Author

Listed:
  • Jing Hou

    (School of Electronic and Information, Northwestern Polytechnical University, Xi’an 710072, China
    Current address: No.127, Youyi West Road, Xi’an 710072, China.)

  • He He

    (System Engineering Research Institute of CSSC, Beijing 100094, China)

  • Yan Yang

    (School of Electronic and Information, Northwestern Polytechnical University, Xi’an 710072, China)

  • Tian Gao

    (School of Electronic and Information, Northwestern Polytechnical University, Xi’an 710072, China)

  • Yifan Zhang

    (School of Electronic and Information, Northwestern Polytechnical University, Xi’an 710072, China)

Abstract

An accurate state of charge (SOC) estimation is vital for safe operation and efficient management of lithium-ion batteries. To improve the accuracy and robustness, an adaptive and robust square root cubature Kalman filter based on variational Bayesian approximation and Huber’s M-estimation (VB-HASRCKF) is proposed. The variational Bayesian (VB) approximation is used to improve the adaptivity by simultaneously estimating the measurement noise covariance and the SOC, while Huber’s M-estimation is employed to enhance the robustness with respect to the outliers in current and voltage measurements caused by adverse operating conditions. A constant-current discharge test and an urban dynamometer driving schedule (UDDS) test are performed to verify the effectiveness and superiority of the proposed algorithm by comparison with the square root cubature Kalman filter (SRCKF), the VB-based SRCKF, and the Huber-based SRCKF. The experimental results show that the proposed VB-HASRCKF algorithm outperforms the other three filters in terms of SOC estimation accuracy and robustness, with a little higher computation complexity.

Suggested Citation

  • Jing Hou & He He & Yan Yang & Tian Gao & Yifan Zhang, 2019. "A Variational Bayesian and Huber-Based Robust Square Root Cubature Kalman Filter for Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(9), pages 1-23, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1717-:d:228795
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    References listed on IDEAS

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    2. Chen, Lin & Yu, Wentao & Cheng, Guoyang & Wang, Jierui, 2023. "State-of-charge estimation of lithium-ion batteries based on fractional-order modeling and adaptive square-root cubature Kalman filter," Energy, Elsevier, vol. 271(C).

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