SOH and RUL Estimation for Lithium-Ion Batteries Based on Partial Charging Curve Features
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 an...
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MDPI AG
2025-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/13/3248 |
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| author | Kejun Qian Yafei Li Qiheng Zou Kecai Cao Zhongpeng Li |
| author_facet | Kejun Qian Yafei Li Qiheng Zou Kecai Cao Zhongpeng Li |
| author_sort | Kejun Qian |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e1d87c53c36349cc98302b5350621441 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-e1d87c53c36349cc98302b53506214412025-08-20T03:16:42ZengMDPI AGEnergies1996-10732025-06-011813324810.3390/en18133248SOH and RUL Estimation for Lithium-Ion Batteries Based on Partial Charging Curve FeaturesKejun Qian0Yafei Li1Qiheng Zou2Kecai Cao3Zhongpeng Li4State Grid Suzhou Power Supply Company, Suzhou 215004, ChinaState Grid Suzhou Power Supply Company, Suzhou 215004, ChinaState Grid Suzhou Power Supply Company, Suzhou 215004, ChinaSchool of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaSchool of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaAccurate 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.https://www.mdpi.com/1996-1073/18/13/3248lithium-ion batteriesstate of healthremaining useful lifepartial charging curvefeature extraction |
| spellingShingle | Kejun Qian Yafei Li Qiheng Zou Kecai Cao Zhongpeng Li SOH and RUL Estimation for Lithium-Ion Batteries Based on Partial Charging Curve Features Energies lithium-ion batteries state of health remaining useful life partial charging curve feature extraction |
| title | SOH and RUL Estimation for Lithium-Ion Batteries Based on Partial Charging Curve Features |
| title_full | SOH and RUL Estimation for Lithium-Ion Batteries Based on Partial Charging Curve Features |
| title_fullStr | SOH and RUL Estimation for Lithium-Ion Batteries Based on Partial Charging Curve Features |
| title_full_unstemmed | SOH and RUL Estimation for Lithium-Ion Batteries Based on Partial Charging Curve Features |
| title_short | SOH and RUL Estimation for Lithium-Ion Batteries Based on Partial Charging Curve Features |
| title_sort | soh and rul estimation for lithium ion batteries based on partial charging curve features |
| topic | lithium-ion batteries state of health remaining useful life partial charging curve feature extraction |
| url | https://www.mdpi.com/1996-1073/18/13/3248 |
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