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Comprehensive performance comparison among different types of features in data-driven battery state of health estimation

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  • Feng, Xinhong
  • Zhang, Yongzhi
  • Xiong, Rui
  • Wang, Chun

Abstract

Battery state of health (SOH), which informs the maximal available capacity of the battery, is a key indicator of battery aging failure. Accurately estimating battery SOH is a vital function of the battery management system that remains to be addressed. In this study, a physics-informed Gaussian process regression (GPR) model is developed for battery SOH estimation, with the performance being systematically compared with that of different types of features and machine learning (ML) methods. The method performance is validated based on 58,826 cycling data units of 118 cells. Experimental results show that the ML driven by the equivalent circuit model (ECM) features generally estimates more accurate SOH than other types of features under different scenarios. The ECM features-based GPR predicts battery SOH with the errors being less than 1.1% based on 10 to 20 mins' relaxation data. And the high robustness and generalization capability of the methodology are also validated against different ratios of training and test data under unseen conditions. Results also highlight the more effective capability of knowledge transfer between different types of batteries with the ECM features and GPR. This study demonstrates the excellence of ECM features in indicating the state evolution of complex systems, and the improved indication performance of these features by combining a suitable ML method.

Suggested Citation

  • Feng, Xinhong & Zhang, Yongzhi & Xiong, Rui & Wang, Chun, 2024. "Comprehensive performance comparison among different types of features in data-driven battery state of health estimation," Applied Energy, Elsevier, vol. 369(C).
    • Handle: RePEc:eee:appene:v:369:y:2024:i:c:s0306261924009383
    DOI: 10.1016/j.apenergy.2024.123555
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    References listed on IDEAS

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    1. Wang, Yaxuan & Zhao, Zhiyong & Cui, Yue & Guo, Shilong & Deng, Liang & Zhao, Lei & Li, Junfu & Wang, Zhenbo, 2025. "A transferable multi-state estimation framework for lithium-ion batteries based on sparse electrochemical parameters," Energy, Elsevier, vol. 335(C).
    2. Yang, Simin & Zhou, Jiahua & Chen, Binbin & An, Ruifeng & Zhao, Ziyu & Fan, Yuqian & Guan, Quanxue & Tan, Xiaojun, 2025. "Deep domain adaptation for cross-chemistry battery SOH prediction with relaxation voltage features," Energy, Elsevier, vol. 339(C).
    3. Mu, Guixiang & Wei, Qingguo & Xu, Yonghong & Zhang, Hongguang & Zhang, Jian & Li, Qi, 2024. "Capacity estimation for lithium-ion batteries based on heterogeneous stacking model with feature fusion," Energy, Elsevier, vol. 313(C).
    4. Xiankun Wei & Silun Peng & Mingli Mo, 2025. "State of Health Prediction for Lithium-Ion Batteries Based on Gated Temporal Network Assisted by Improved Grasshopper Optimization," Energies, MDPI, vol. 18(14), pages 1-17, July.
    5. 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).

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