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Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter

Author

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  • Bizhong Xia

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China)

  • Zizhou Lao

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China)

  • Ruifeng Zhang

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
    Sunwoda Electronic Co., Ltd., Shenzhen 518108, China)

  • Yong Tian

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China)

  • Guanghao Chen

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China)

  • Zhen Sun

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China)

  • Wei Wang

    (Sunwoda Electronic Co., Ltd., Shenzhen 518108, China)

  • Wei Sun

    (Sunwoda Electronic Co., Ltd., Shenzhen 518108, China)

  • Yongzhi Lai

    (Sunwoda Electronic Co., Ltd., Shenzhen 518108, China)

  • Mingwang Wang

    (Sunwoda Electronic Co., Ltd., Shenzhen 518108, China)

  • Huawen Wang

    (Sunwoda Electronic Co., Ltd., Shenzhen 518108, China)

Abstract

State of charge (SOC) estimation is the core of any battery management system. Most closed-loop SOC estimation algorithms are based on the equivalent circuit model with fixed parameters. However, the parameters of the equivalent circuit model will change as temperature or SOC changes, resulting in reduced SOC estimation accuracy. In this paper, two SOC estimation algorithms with online parameter identification are proposed to solve this problem based on forgetting factor recursive least squares (FFRLS) and nonlinear Kalman filter. The parameters of a Thevenin model are constantly updated by FFRLS. The nonlinear Kalman filter is used to perform the recursive operation to estimate SOC. Experiments in variable temperature environments verify the effectiveness of the proposed algorithms. A combination of four driving cycles is loaded on lithium-ion batteries to test the adaptability of the approaches to different working conditions. Under certain conditions, the average error of the SOC estimation dropped from 5.6% to 1.1% after adding the online parameters identification, showing that the estimation accuracy of proposed algorithms is greatly improved. Besides, simulated measurement noise is added to the test data to prove the robustness of the algorithms.

Suggested Citation

  • Bizhong Xia & Zizhou Lao & Ruifeng Zhang & Yong Tian & Guanghao Chen & Zhen Sun & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang & Huawen Wang, 2017. "Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter," Energies, MDPI, vol. 11(1), pages 1-23, December.
  • Handle: RePEc:gam:jeners:v:11:y:2017:i:1:p:3-:d:123818
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    References listed on IDEAS

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    Cited by:

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    4. Zizhou Lao & Bizhong Xia & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang, 2018. "A Novel Method for Lithium-Ion Battery Online Parameter Identification Based on Variable Forgetting Factor Recursive Least Squares," Energies, MDPI, vol. 11(6), pages 1-15, May.
    5. Zhengyi Bao & Jiahao Jiang & Chunxiang Zhu & Mingyu Gao, 2022. "A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery," Energies, MDPI, vol. 15(12), pages 1-16, June.
    6. Zeyan Lv & Yanghong Xia & Junwei Chai & Miao Yu & Wei Wei, 2018. "Distributed Coordination Control Based on State-of-Charge for Bidirectional Power Converters in a Hybrid AC/DC Microgrid," Energies, MDPI, vol. 11(4), pages 1-15, April.
    7. Damoon Soudbakhsh & Mehdi Gilaki & William Lynch & Peilin Zhang & Taeyoung Choi & Elham Sahraei, 2020. "Electrical Response of Mechanically Damaged Lithium-Ion Batteries," Energies, MDPI, vol. 13(17), pages 1-15, August.

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