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State-of-charge estimation for onboard LiFePO4 batteries with adaptive state update in specific open-circuit-voltage ranges

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  • Xiong, Rui
  • Duan, Yanzhou
  • Zhang, Kaixuan
  • Lin, Da
  • Tian, Jinpeng
  • Chen, Cheng

Abstract

Accurate estimation of the state-of-charge (SOC) is crucial for efficient and safe battery applications. However, existing SOC estimation methods fail to provide accurate SOC estimation for LiFePO4 batteries that have a flat voltage-SOC relationship. The analysis of the voltage-SOC characteristics shows that the failure of the present model-based methods can be ascribed to their inability to simultaneously accommodate the differences in voltage characteristics between different open-circuit-voltage (OCV) ranges. To overcome this limitation, an adaptive recursive square root algorithm is used to online identify OCV and other battery model parameters. Then, the parameters of the extended Kalman filter are adaptively updated in different OCV ranges, which are distinguished based on the identified OCV. Additional filtering methods are employed to enhance the stability of the estimation. Large-scale experiments are conducted at different temperatures with various driving profiles for method validation. While conventional methods fail to converge, the proposed method ensures both high accuracy and stability, with a maximum absolute error of <2%. The viability of the proposed method is further verified using data collected from real battery systems. Our work lays a foundation for the reliable management of LiFePO4 batteries in electric vehicles.

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  • Xiong, Rui & Duan, Yanzhou & Zhang, Kaixuan & Lin, Da & Tian, Jinpeng & Chen, Cheng, 2023. "State-of-charge estimation for onboard LiFePO4 batteries with adaptive state update in specific open-circuit-voltage ranges," Applied Energy, Elsevier, vol. 349(C).
    • Handle: RePEc:eee:appene:v:349:y:2023:i:c:s0306261923009455
    DOI: 10.1016/j.apenergy.2023.121581
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    References listed on IDEAS

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    3. Liu, Zixi & Ruan, Guanqiang & Tian, Yupeng & Hu, Xing & Yan, Rong & Yang, Kuo, 2024. "A real-world battery state of charge prediction method based on a lightweight mixer architecture," Energy, Elsevier, vol. 311(C).

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