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State of power prediction joint fisher optimal segmentation and PO-BP neural network for a parallel battery pack considering cell inconsistency

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  • Peng, Simin
  • Chen, Shengdong
  • Liu, Yong
  • Yu, Quanqing
  • Kan, Jiarong
  • Li, Rui

Abstract

Accurate state of power (SOP) of battery is critical for efficient control and stable operation of electric vehicles. Due to cell inconsistency and even varying degrees of discrepancy, especially, for the cells with large inconsistencies, accurately predicting SOP for a parallel battery pack faces challenges. To resolve these problems, a SOP prediction method joint Fisher optimal segmentation (FOS) and parrot optimizer-back propagation (PO-BP) neural network is developed. First, a quantitative differentiation strategy of the cell inconsistency based on weighted cosine similarity (WCS) is presented, which can describe the variation of cell similarity coefficient with maximum current deviation in parallel branch. Secondly, according to similarity coefficient and current deviation of the cells, a determination method using FOS for the degree of cell inconsistency is developed to identify cells with large inconsistencies. Finally, a power corrector based on the PO-BP neural network is developed to compensate for the SOP differences caused by the cells with large inconsistencies, thereby improving the SOP prediction accuracy. The experimental results verify the effectiveness of the developed method under various dynamic conditions, with a decrease of approximately 60 % in both the mean absolute error and the root mean square error of the SOP for a parallel battery pack.

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  • Peng, Simin & Chen, Shengdong & Liu, Yong & Yu, Quanqing & Kan, Jiarong & Li, Rui, 2025. "State of power prediction joint fisher optimal segmentation and PO-BP neural network for a parallel battery pack considering cell inconsistency," Applied Energy, Elsevier, vol. 381(C).
    • Handle: RePEc:eee:appene:v:381:y:2025:i:c:s0306261924025145
    DOI: 10.1016/j.apenergy.2024.125130
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    References listed on IDEAS

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    Cited by:

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    3. Meng-Xiang Yan & Zhi-Hui Deng & Lianfeng Lai & Yong-Hong Xu & Liang Tong & Hong-Guang Zhang & Yi-Yang Li & Ming-Hui Gong & Guo-Ju Liu, 2025. "A Sustainable SOH Prediction Model for Lithium-Ion Batteries Based on CPO-ELM-ABKDE with Uncertainty Quantification," Sustainability, MDPI, vol. 17(11), pages 1-28, June.
    4. Mu, Guixiang & Wei, Qingguo & Xu, Yonghong & Li, Jian & Zhang, Hongguang & Yang, Fubin & Zhang, Jian & Li, Qi, 2025. "State of health estimation of lithium-ion batteries based on feature optimization and data-driven models," Energy, Elsevier, vol. 316(C).
    5. Wang, Zhenxi & Ma, Yan & Gao, Jinwu & Chen, Hong, 2025. "Remaining useful life prediction for solid-state lithium batteries based on spatial–temporal relations and neuronal ODE-assisted KAN," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    6. Peng, Simin & Wang, Yujian & Tang, Aihua & Jiang, Yuxia & Kan, Jiarong & Pecht, Michael, 2025. "State of health estimation joint improved grey wolf optimization algorithm and LSTM using partial discharging health features for lithium-ion batteries," Energy, Elsevier, vol. 315(C).

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