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

Kejun Qian, Yafei Li (), Qiheng Zou, Kecai Cao and Zhongpeng Li
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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

Energies, 2025, vol. 18, issue 13, 1-19

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.

Keywords: lithium-ion batteries; state of health; remaining useful life; partial charging curve; feature extraction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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