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Integrated framework for battery cell state-of-health estimation in complex modules: Combining current distribution analysis and novel terminal voltage estimation L-EKF modeling

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  • Fan, Yuqian
  • Zhao, Jifei
  • Li, Yi
  • Wang, Jianping
  • Yang, Fangfang
  • Tan, Xiaojun

Abstract

In electric vehicles, the variability among individual cells within power battery modules presents formidable obstacles in determining the state-of-health (SOH). This study reveals an innovative approach for estimating the terminal voltage of battery cells in modules, with a focus on enhancing the accuracy of SOH estimation for individual cells. First, the topological structure of the current distribution within the battery module is thoroughly analyzed to establish a probabilistic model of the current distribution. Subsequently, a hybrid algorithm combining a long short-term memory (LSTM) network and an extended Kalman filter (EKF), referred to as the L-EKF, is proposed to realize terminal voltage estimation. Additionally, the use of an adversarial domain distribution probability network (AdaDDPN) further augments the accuracy of SOH estimation for individual cells. The experimental results demonstrate that the proposed method outperforms alternatives in terms of error metrics, including the MAE, RMSE, and MAXE. The battery cell terminal voltage estimation exhibited an RMSE of 6.31 mV and an MAXE of 25.01 mV, with AdaDDPN achieving MAEs below 0.02 and RMSEs below 0.025. This approach not only provides a reliable method for assessing individual cell health in battery management systems but also significantly enhances the safety and reliability of electric vehicles.

Suggested Citation

  • Fan, Yuqian & Zhao, Jifei & Li, Yi & Wang, Jianping & Yang, Fangfang & Tan, Xiaojun, 2025. "Integrated framework for battery cell state-of-health estimation in complex modules: Combining current distribution analysis and novel terminal voltage estimation L-EKF modeling," Energy, Elsevier, vol. 314(C).
    • Handle: RePEc:eee:energy:v:314:y:2025:i:c:s0360544224040362
    DOI: 10.1016/j.energy.2024.134258
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    References listed on IDEAS

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    1. Marzia Abaspour & Krishna R. Pattipati & Behnam Shahrrava & Balakumar Balasingam, 2022. "Robust Approach to Battery Equivalent-Circuit-Model Parameter Extraction Using Electrochemical Impedance Spectroscopy," Energies, MDPI, vol. 15(23), pages 1-26, December.
    2. Li, Yuanyuan & Sheng, Hanmin & Cheng, Yuhua & Stroe, Daniel-Ioan & Teodorescu, Remus, 2020. "State-of-health estimation of lithium-ion batteries based on semi-supervised transfer component analysis," Applied Energy, Elsevier, vol. 277(C).
    3. Liu, Rui & Chen, Jixin & Xun, Jingzhi & Jiao, Kui & Du, Qing, 2014. "Numerical investigation of thermal behaviors in lithium-ion battery stack discharge," Applied Energy, Elsevier, vol. 132(C), pages 288-297.
    4. Yang, Yongsong & Xu, Yuchen & Nie, Yuwei & Li, Jianming & Liu, Shizhuo & Zhao, Lijun & Yu, Quanqing & Zhang, Chengming, 2024. "Deep transfer learning enables battery state of charge and state of health estimation," Energy, Elsevier, vol. 294(C).
    5. You, Gae-won & Park, Sangdo & Oh, Dukjin, 2016. "Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach," Applied Energy, Elsevier, vol. 176(C), pages 92-103.
    6. Pan, Haihong & Lü, Zhiqiang & Wang, Huimin & Wei, Haiyan & Chen, Lin, 2018. "Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine," Energy, Elsevier, vol. 160(C), pages 466-477.
    7. Wang, Limei & Pan, Chaofeng & Liu, Liang & Cheng, Yong & Zhao, Xiuliang, 2016. "On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis," Applied Energy, Elsevier, vol. 168(C), pages 465-472.
    8. Weng, Caihao & Feng, Xuning & Sun, Jing & Peng, Huei, 2016. "State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking," Applied Energy, Elsevier, vol. 180(C), pages 360-368.
    9. Qian, Cheng & Xu, Binghui & Xia, Quan & Ren, Yi & Sun, Bo & Wang, Zili, 2023. "SOH prediction for Lithium-Ion batteries by using historical state and future load information with an AM-seq2seq model," Applied Energy, Elsevier, vol. 336(C).
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