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Lithium-ion battery state of charge and parameters joint estimation using cubature Kalman filter and particle filter

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Abstract

Accurate estimation of the state of charge (SOC) of a lithium-ion battery is one of the most crucial issues of battery management system (BMS). Existing methods can achieve accurate estimation of the SOC under stable working conditions. However, they may result in inaccuracy under unstable working conditions such as dynamic cycles and different temperature conditions. This is due to the fact that the dynamic behaviors of battery states have not been considered by the parameter identification methods. In this paper, a SOC and parameter joint estimation method is put forward, where the battery model parameters are identified in real time by a particle filter (PF) with consideration of the battery states. Meanwhile, a cubature Kalman filter (CKF) is used to estimate SOC. Then, experiments under dynamic cycles and different temperature conditions are undertaken to assess the performance of the proposed algorithm when compared with the existing joint estimations. The results show that the proposed joint method can achieve a high accuracy and robustness for SOC estimation.

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Acknowledgements

This work was supported by the Natural Science Foundation of China (Grant No. 11172220).

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan, China

    Wei Xu & Jinli Xu

  2. SAIC-GM-Wuling Automobile Co., Ltd, Liuzhou, China

    Xiaofeng Yan

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  1. Wei Xu
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  2. Jinli Xu
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Correspondence to Jinli Xu.

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Xu, W., Xu, J. & Yan, X. Lithium-ion battery state of charge and parameters joint estimation using cubature Kalman filter and particle filter. J. Power Electron. 20, 292–307 (2020). https://doi.org/10.1007/s43236-019-00023-4

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  • DOI: https://doi.org/10.1007/s43236-019-00023-4

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