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State of Charge Dual Estimation of a Li-ion Battery Based on Variable Forgetting Factor Recursive Least Square and Multi-Innovation Unscented Kalman Filter Algorithm

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  • Hongyuan Yuan

    (School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
    China Energy Construction Group Guangdong Electric Power Design and Research Institute Co., Ltd., Guangzhou 510700, China)

  • Youjun Han

    (School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
    Guangzhou Institute of Industrial Technology, Guangzhou 511458, China)

  • Yu Zhou

    (School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China)

  • Zongke Chen

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Juan Du

    (School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China)

  • Hailong Pei

    (School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China)

Abstract

Battery management is the key technical link for electric vehicles. A good battery management system can realize the balanced charge and discharge of batteries, reducing the capacity degradation and the loss of health caused by battery overcharge and discharge, which all depend on the real-time and accurate estimation of the battery’s state of charge (SOC). However, the battery’s SOC has highly complex nonlinear time-varying characteristics related to the complex chemical and physical state and dynamic environmental conditions, which are difficult to measure directly, and this has become a difficulty in design and research. According to the characteristics of ternary lithium-ion batteries of electric vehicles, a battery SOC dual estimation algorithm based on the Variable Forgetting Factor Recursive Least Square (VFFRLS) and Multi-Innovation Unscented Kalman Filter (MIUKF) is proposed in this paper. The VFFRLS algorithm is used to estimate battery model parameters, and the MIUKF algorithm is used to estimate the battery’s SOC in real time. The two algorithms are coupled to update battery model parameters and estimate the SOC. The experiment results show that the algorithm has high accuracy and stability.

Suggested Citation

  • Hongyuan Yuan & Youjun Han & Yu Zhou & Zongke Chen & Juan Du & Hailong Pei, 2022. "State of Charge Dual Estimation of a Li-ion Battery Based on Variable Forgetting Factor Recursive Least Square and Multi-Innovation Unscented Kalman Filter Algorithm," Energies, MDPI, vol. 15(4), pages 1-22, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1529-:d:753015
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    References listed on IDEAS

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    Cited by:

    1. Wang, Limei & Sun, Jingjing & Cai, Yingfeng & Lian, Yubo & Jin, Mengjie & Zhao, Xiuliang & Wang, Ruochen & Chen, Long & Chen, Jun, 2023. "A novel OCV curve reconstruction and update method of lithium-ion batteries at different temperatures based on cloud data," Energy, Elsevier, vol. 268(C).
    2. Suwei Zhai & Wenyun Li & Cheng Wang & Yundi Chu, 2022. "A Novel Data-Driven Estimation Method for State-of-Charge Estimation of Li-Ion Batteries," Energies, MDPI, vol. 15(9), pages 1-17, April.

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