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State-of-health estimation for lithium-ion batteries based on Kullback–Leibler divergence and a retentive network

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  • Chen, Guanxu
  • Yang, Fangfang
  • Peng, Weiwen
  • Fan, Yuqian
  • Lyu, Ximin

Abstract

Accurate state-of-health (SOH) estimation is crucial for the lithium-ion battery industry, as it underpins the safety, durability, and reliability of lithium-ion batteries. Currently, most researchers use various methods of health indicator (HI) extraction for the SOH estimation of batteries. However, these methods may require certain expertise and prior knowledge to achieve accurate modeling, being affected by measurement noise and other factors. To solve the abovementioned problems, three Kullback–Leibler (KL) divergence features based on partial voltage sequences are proposed as new HIs that are independent of prior knowledge and strongly correlated with SOH. Moreover, a modified retentive network is proposed to enhance SOH estimation accuracy and better utilize HIs than traditional deep learning methods, which have high training costs and insufficient accuracy. To ensure consistent extraction of KL divergence features across various experimental conditions and time intervals, a B-spline algorithm is utilized for interpolation. The effectiveness of the proposed method is validated through analysis of Pearson correlation coefficients and experiments conducted in four dimensions. Additionally, the potential of using the proposed method to compress data on the cloud-side is explored.

Suggested Citation

  • Chen, Guanxu & Yang, Fangfang & Peng, Weiwen & Fan, Yuqian & Lyu, Ximin, 2024. "State-of-health estimation for lithium-ion batteries based on Kullback–Leibler divergence and a retentive network," Applied Energy, Elsevier, vol. 376(PB).
    • Handle: RePEc:eee:appene:v:376:y:2024:i:pb:s0306261924016490
    DOI: 10.1016/j.apenergy.2024.124266
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    1. Xing, Xueqi & Yan, Tongtong & Xia, Min, 2025. "Adaptive shapley-embedded neural network ensemble for accurate state of health estimation using electrochemical impedance spectroscopy," Applied Energy, Elsevier, vol. 401(PC).
    2. Wang, Yaxuan & Guo, Shilong & Cui, Yue & Deng, Liang & Zhao, Lei & Li, Junfu & Wang, Zhenbo, 2025. "A comprehensive review of machine learning-based state of health estimation for lithium-ion batteries: data, features, algorithms, and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 224(C).
    3. Hou, Guolian & Zhang, Fan & Huang, Congzhi & Huang, Ting, 2025. "Joint prediction of SOH and RUL for Lithium-ion batteries by an enhanced Transformer model with physical information constraints," Energy, Elsevier, vol. 336(C).
    4. Xiong, Ran & Zhao, Pengfei & Cao, Di & Zhang, Sen & Zhan, Wei & Tang, Ming & Zhang, Yuning & Hu, Weihao, 2025. "Transfer learning with composite kernel sparse Gaussian process-aided model for probabilistic state of health estimation of lithium-ion batteries against multi-source coupled harsh scenarios," Applied Energy, Elsevier, vol. 401(PC).

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