SOC Estimation of Lithium-Ion Batteries Utilizing EIS Technology with SHAP–ASO–LightGBM
Accurate State of Charge (SOC) estimation is critical for optimizing the performance and longevity of lithium-ion batteries (LIBs), which are widely used in applications ranging from electric vehicles to renewable energy storage. Traditional SOC estimation methods, such as Coulomb counting and open-...
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MDPI AG
2025-07-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/11/7/272 |
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| author | Panpan Hu Chun Yin Li Chi Chung Lee |
| author_facet | Panpan Hu Chun Yin Li Chi Chung Lee |
| author_sort | Panpan Hu |
| collection | DOAJ |
| description | Accurate State of Charge (SOC) estimation is critical for optimizing the performance and longevity of lithium-ion batteries (LIBs), which are widely used in applications ranging from electric vehicles to renewable energy storage. Traditional SOC estimation methods, such as Coulomb counting and open-circuit voltage measurement, suffer from cumulative errors and slow response times. This paper proposes a novel machine learning-based approach for SOC estimation by integrating Electrochemical Impedance Spectroscopy (EIS) with the SHapley Additive exPlanations (SHAP) method, Atom Search Optimization (ASO), and Light Gradient Boosting Machine (LightGBM). This study focuses on large-capacity lithium iron phosphate (LFP) batteries (3.2 V, 104 Ah), addressing a gap in existing research. EIS data collected at various SOC levels and temperatures were processed using SHAP for feature extraction (FE), and the ASO–LightGBM model was employed for SOC prediction. Experimental results demonstrate that the proposed SHAP–ASO–LightGBM method significantly improves estimation accuracy, achieving an RMSE of 3.3%, MAE of 1.86%, and R<sup>2</sup> of 0.99, outperforming traditional methods like LSTM and DNN. The findings highlight the potential of EIS and machine learning (ML) for robust SOC estimation in large-capacity LIBs. |
| format | Article |
| id | doaj-art-0598dc43635f4bc4bf34df295bcffbe7 |
| institution | DOAJ |
| issn | 2313-0105 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj-art-0598dc43635f4bc4bf34df295bcffbe72025-08-20T02:45:37ZengMDPI AGBatteries2313-01052025-07-0111727210.3390/batteries11070272SOC Estimation of Lithium-Ion Batteries Utilizing EIS Technology with SHAP–ASO–LightGBMPanpan Hu0Chun Yin Li1Chi Chung Lee2School of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR, ChinaSchool of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR, ChinaSchool of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR, ChinaAccurate State of Charge (SOC) estimation is critical for optimizing the performance and longevity of lithium-ion batteries (LIBs), which are widely used in applications ranging from electric vehicles to renewable energy storage. Traditional SOC estimation methods, such as Coulomb counting and open-circuit voltage measurement, suffer from cumulative errors and slow response times. This paper proposes a novel machine learning-based approach for SOC estimation by integrating Electrochemical Impedance Spectroscopy (EIS) with the SHapley Additive exPlanations (SHAP) method, Atom Search Optimization (ASO), and Light Gradient Boosting Machine (LightGBM). This study focuses on large-capacity lithium iron phosphate (LFP) batteries (3.2 V, 104 Ah), addressing a gap in existing research. EIS data collected at various SOC levels and temperatures were processed using SHAP for feature extraction (FE), and the ASO–LightGBM model was employed for SOC prediction. Experimental results demonstrate that the proposed SHAP–ASO–LightGBM method significantly improves estimation accuracy, achieving an RMSE of 3.3%, MAE of 1.86%, and R<sup>2</sup> of 0.99, outperforming traditional methods like LSTM and DNN. The findings highlight the potential of EIS and machine learning (ML) for robust SOC estimation in large-capacity LIBs.https://www.mdpi.com/2313-0105/11/7/272lithium-ion batteriesstate of chargeelectrochemical impedance spectroscopySHAPLightGBMatom search optimization |
| spellingShingle | Panpan Hu Chun Yin Li Chi Chung Lee SOC Estimation of Lithium-Ion Batteries Utilizing EIS Technology with SHAP–ASO–LightGBM Batteries lithium-ion batteries state of charge electrochemical impedance spectroscopy SHAP LightGBM atom search optimization |
| title | SOC Estimation of Lithium-Ion Batteries Utilizing EIS Technology with SHAP–ASO–LightGBM |
| title_full | SOC Estimation of Lithium-Ion Batteries Utilizing EIS Technology with SHAP–ASO–LightGBM |
| title_fullStr | SOC Estimation of Lithium-Ion Batteries Utilizing EIS Technology with SHAP–ASO–LightGBM |
| title_full_unstemmed | SOC Estimation of Lithium-Ion Batteries Utilizing EIS Technology with SHAP–ASO–LightGBM |
| title_short | SOC Estimation of Lithium-Ion Batteries Utilizing EIS Technology with SHAP–ASO–LightGBM |
| title_sort | soc estimation of lithium ion batteries utilizing eis technology with shap aso lightgbm |
| topic | lithium-ion batteries state of charge electrochemical impedance spectroscopy SHAP LightGBM atom search optimization |
| url | https://www.mdpi.com/2313-0105/11/7/272 |
| work_keys_str_mv | AT panpanhu socestimationoflithiumionbatteriesutilizingeistechnologywithshapasolightgbm AT chunyinli socestimationoflithiumionbatteriesutilizingeistechnologywithshapasolightgbm AT chichunglee socestimationoflithiumionbatteriesutilizingeistechnologywithshapasolightgbm |