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Research on Parameter Self-Learning Unscented Kalman Filtering Algorithm and Its Application in Battery Charge of State Estimation

Author

Listed:
  • Fang Liu

    (School of Computer Science & Technology, Tiangong University, Tianjin 300387, China)

  • Jie Ma

    (School of Computer Science & Technology, Tiangong University, Tianjin 300387, China)

  • Weixing Su

    (School of Computer Science & Technology, Tiangong University, Tianjin 300387, China
    State Key Laboratory of Process Automation in Mining & Metallurgy, Beijing 100160, China
    Beijing Key Laboratory of Process Automation in Mining & Metallurgy, Beijing 100160, China)

  • Hanning Chen

    (School of Computer Science & Technology, Tiangong University, Tianjin 300387, China)

  • Maowei He

    (School of Computer Science & Technology, Tiangong University, Tianjin 300387, China)

Abstract

A novel state estimation algorithm based on the parameters of a self-learning unscented Kalman filter (UKF) with a model parameter identification method based on a collaborative optimization mechanism is proposed in this paper. This algorithm can realize the dynamic self-learning and self-adjustment of the parameters in the UKF algorithm and the automatic optimization setting Sigma points without human participation. In addition, the multi-algorithm collaborative optimization mechanism unifies a variety of algorithms, so that the identification method has the advantages of member algorithms while avoiding the disadvantages of them. We apply the combination algorithm proposed in this paper for state of charge (SoC) estimation of power batteries and compare it with other model parameter identification algorithms and SoC estimation methods. The results showed that the proposed algorithm outperformed the other model parameter identification algorithms in terms of estimation accuracy and robustness.

Suggested Citation

  • Fang Liu & Jie Ma & Weixing Su & Hanning Chen & Maowei He, 2020. "Research on Parameter Self-Learning Unscented Kalman Filtering Algorithm and Its Application in Battery Charge of State Estimation," Energies, MDPI, vol. 13(7), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1679-:d:340803
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    References listed on IDEAS

    as
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    3. Shulin Liu & Naxin Cui & Chenghui Zhang, 2017. "An Adaptive Square Root Unscented Kalman Filter Approach for State of Charge Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 10(9), pages 1-14, September.
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    5. Guo, Feng & Hu, Guangdi & Xiang, Shun & Zhou, Pengkai & Hong, Ru & Xiong, Neng, 2019. "A multi-scale parameter adaptive method for state of charge and parameter estimation of lithium-ion batteries using dual Kalman filters," Energy, Elsevier, vol. 178(C), pages 79-88.
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    Full references (including those not matched with items on IDEAS)

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