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SOH and RUL Estimation for Lithium-Ion Batteries Based on Partial Charging Curve Features

Author

Listed:
  • Kejun Qian

    (State Grid Suzhou Power Supply Company, Suzhou 215004, China)

  • Yafei Li

    (State Grid Suzhou Power Supply Company, Suzhou 215004, China)

  • Qiheng Zou

    (State Grid Suzhou Power Supply Company, Suzhou 215004, China)

  • Kecai Cao

    (School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China)

  • Zhongpeng Li

    (School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China)

Abstract

Accurate estimation of the state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries (LiBs) is critical for ensuring battery reliability and safety in applications such as electric vehicles and energy storage systems. However, existing methods developed for estimating the SOH and RUL of LiBs often rely on full-cycle charging data, which are difficult to obtain in engineering practice. To bridge this gap, this paper proposes a novel data-driven method to estimate the SOH and RUL of LiBs only using partial charging curve features. Key health features are extracted from the constant voltage (CV) charging process and voltage relaxation, validated through Pearson correlation analysis and SHapley Additive exPlanations (SHAP) interpretability. A hybrid framework combining CatBoost for SOH estimation and particle swarm optimization-support vector regression (PSO-SVR) for RUL estimation is developed. Experimental validation on public datasets demonstrates superior performance of the methodology described above, with an SOH estimation root mean square error (RMSE) and mean absolute error (MAE) below 1.42% and 0.52% and RUL estimation relative error (RE) under 1.87%. The proposed methodology also exhibits robustness and computational efficiency, making it suitable for battery management systems (BMSs) of LiBs.

Suggested Citation

  • Kejun Qian & Yafei Li & Qiheng Zou & Kecai Cao & Zhongpeng Li, 2025. "SOH and RUL Estimation for Lithium-Ion Batteries Based on Partial Charging Curve Features," Energies, MDPI, vol. 18(13), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3248-:d:1684174
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    References listed on IDEAS

    as
    1. Jianfang Jia & Jianyu Liang & Yuanhao Shi & Jie Wen & Xiaoqiong Pang & Jianchao Zeng, 2020. "SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators," Energies, MDPI, vol. 13(2), pages 1-20, January.
    2. Li, J. & Adewuyi, K. & Lotfi, N. & Landers, R.G. & Park, J., 2018. "A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation," Applied Energy, Elsevier, vol. 212(C), pages 1178-1190.
    3. Song, Ziyou & Hou, Jun & Li, Xuefeng & Wu, Xiaogang & Hu, Xiaosong & Hofmann, Heath & Sun, Jing, 2020. "The sequential algorithm for combined state of charge and state of health estimation of lithium-ion battery based on active current injection," Energy, Elsevier, vol. 193(C).
    4. Zhang, Chaolong & Luo, Laijin & Yang, Zhong & Du, Bolun & Zhou, Ziheng & Wu, Ji & Chen, Liping, 2024. "Flexible method for estimating the state of health of lithium-ion batteries using partial charging segments," Energy, Elsevier, vol. 295(C).
    5. Cai, Nian & Que, Xiaoping & Zhang, Xu & Feng, Weiguo & Zhou, Yinghong, 2024. "A deep learning framework for the joint prediction of the SOH and RUL of lithium-ion batteries based on bimodal images," Energy, Elsevier, vol. 302(C).
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