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An estimated value compensation method for state of charge estimation of lithium battery based on open circuit voltage change rate

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  • Wang, Luxiao
  • Duan, Jiandong
  • Fan, Shaogui
  • Zhao, Ke

Abstract

The state of charge(SOC) estimation precision of the algorithms based on equivalent circuit model(ECM) is deeply affected by the open circuit voltage(OCV)-SOC relationship, especially in the voltage error existing condition. In this article, an estimated value compensation method for SOC estimation of lithium battery based on OCV change rate is proposed. Firstly, extended Kalman filter(EKF) and unscented Kalman filter(UKF) algorithms are used to estimate SOC of LiFePO4 and LiCoO2 batteries in Beijing bus dynamic stress test(BBDST) condition. The results show that the root mean square errors of SOC are within 2 %. Then, the distribution characteristics of SOC estimation errors for the two batteries under different voltage errors are explored. In addition, the relationship between SOC errors and OCV change rate is analyzed. Secondly, the SOC estimation result of ampere-hour integral(AHI) method is set as an online reference. The compensation factors that related to the OCV change rate are used to compensate the estimation results of EKF and UKF algorithms. Thirdly, the validity of the proposed method is verified under different operating conditions. The experimental results show that the SOC estimation accuracy of EKF and UKF algorithms can be greatly improved by the proposed method under large voltage error existing condition.

Suggested Citation

  • Wang, Luxiao & Duan, Jiandong & Fan, Shaogui & Zhao, Ke, 2024. "An estimated value compensation method for state of charge estimation of lithium battery based on open circuit voltage change rate," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224038970
    DOI: 10.1016/j.energy.2024.134119
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    1. Hongzhao Li & Hongsheng Jia & Ping Xiao & Haojie Jiang & Yang Chen, 2025. "Research Progress on State of Charge Estimation Methods for Power Batteries in New Energy Intelligent Connected Vehicles," Energies, MDPI, vol. 18(9), pages 1-30, April.
    2. Wang, Yun & Li, Yuhao & Zhang, Ziyang & Yu, Peihua & Li, Yifen & Liu, Bo & Zou, Runmin, 2025. "Bidirectional Mamba network with multi-scale feature fusion and sparse-channel mixture of experts for battery state of charge estimation," Energy, Elsevier, vol. 340(C).
    3. Wu, Chen & Liang, Jiaqi & Wang, Yan & Li, Boliang, 2025. "Online state-of-charge estimation for lithium-ion batteries via a high-degree-of-freedom robust observer with model parameter identification," Energy, Elsevier, vol. 334(C).
    4. Zou, Yuanru & Shi, Haotian & Cao, Wen & Wang, Shunli & Nie, Shiliang & Chen, Dan, 2025. "A high-speed recurrent state network with noise reduction for multi-temperature state of energy estimation of electric vehicles lithium-ion batteries," Energy, Elsevier, vol. 322(C).
    5. Liu, Wei & Teh, Jiashen & Alharbi, Bader, 2025. "An asynchronous electro-thermal coupling modeling method of lithium-ion batteries under dynamic operating conditions," Energy, Elsevier, vol. 324(C).

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