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A novel capacity and initial discharge electric quantity estimation method for LiFePO4 battery pack based on OCV curve partial reconstruction

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  • Sun, Jinlei
  • Tang, Yong
  • Ye, Jilei
  • Jiang, Tao
  • Chen, Saihan
  • Qiu, Shengshi

Abstract

LiFePO4 batteries are widely used in electric vehicles (EVs) and battery energy storage systems (BESSs). However, the consistencies in capacity, internal resistance, and open circuit voltage (OCV) limit the battery pack performance, which makes it difficult to evaluate the state information of the cells effectively without disassembling the pack. Therefore, this paper proposes a battery capacity and initial discharge electric quantity (DEQ) estimation method for series-connected battery packs based on partial reconstruction of the OCV curve. The aging characteristics of each cell are extracted based on incremental capacity analysis (ICA), which is taken as the transformation law for the OCV curve considering aging. And then the multipopulation genetic algorithm (MPGA) is utilized to optimize the transformation coefficients and obtain the reconstructed OCV curve of each cell in the pack. Finally, the actual capacity and initial DEQ of batteries at a low current rate are estimated according to the reconstructed complete OCV curve and optimized OCV curve transformation coefficients. The experimental results show that the estimation error of capacity is less than 5%, and the maximum estimation error of the initial DEQ is 5.56%.

Suggested Citation

  • Sun, Jinlei & Tang, Yong & Ye, Jilei & Jiang, Tao & Chen, Saihan & Qiu, Shengshi, 2022. "A novel capacity and initial discharge electric quantity estimation method for LiFePO4 battery pack based on OCV curve partial reconstruction," Energy, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:energy:v:243:y:2022:i:c:s0360544221031315
    DOI: 10.1016/j.energy.2021.122882
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    References listed on IDEAS

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    Cited by:

    1. Fan, Xinyuan & Qi, Hongfeng & Zhang, Weige & Zhang, Yanru, 2024. "Experiment-free physical hybrid neural network approach for battery pack inconsistency estimation," Applied Energy, Elsevier, vol. 358(C).
    2. Ma, Wentao & Guo, Peng & Wang, Xiaofei & Zhang, Zhiyu & Peng, Siyuan & Chen, Badong, 2022. "Robust state of charge estimation for Li-ion batteries based on cubature kalman filter with generalized maximum correntropy criterion," Energy, Elsevier, vol. 260(C).

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