A Novel SOH Estimation Method for Lithium-Ion Batteries Based on the PSO–GWO–LSSVM Prediction Model with Multi-Dimensional Health Features Extraction
Accurate State of Health (SOH) estimation of lithium-ion batteries (LIBs) is critical for ensuring the safety of electric vehicles and improving the reliability of battery management systems (BMS). However, the use of individual health features (HFs) and the selection of hyperparameters can increase...
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MDPI AG
2025-03-01
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| author | Xu He Zhengpu Wu Jinghan Bai Junchao Zhu Lu Lv Lujun Wang |
| author_facet | Xu He Zhengpu Wu Jinghan Bai Junchao Zhu Lu Lv Lujun Wang |
| author_sort | Xu He |
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| description | Accurate State of Health (SOH) estimation of lithium-ion batteries (LIBs) is critical for ensuring the safety of electric vehicles and improving the reliability of battery management systems (BMS). However, the use of individual health features (HFs) and the selection of hyperparameters can increase the data processing burden on the BMS and reduce the accuracy of data-driven models. To address the above issue, this paper proposes a novel SOH estimation method for lithium-ion batteries based on the PSO–GWO–LSSVM prediction model with multi-dimensional health feature extraction. To comprehensively capture the battery aging mechanisms, four categories of health features—time, energy, similarity, and second-order features—are extracted from the LIBs charging segments. The correlation between HFs and SOH is comprehensively evaluated through Pearson and Spearman correlation analyses, followed by Gaussian filtering and outlier detection to enhance feature quality. With strong generalization and robustness, least squares support vector machine (LSSVM) is widely applied to nonlinear computations and function approximation. To improve LSSVM model accuracy and efficiency, this paper develops a novel prediction model that uses particle swarm optimization (PSO) combined with grey wolf optimization (GWO) algorithms to optimize the LSSVM model. The generalization performance of the proposed method is validated through comparative experiments using a battery dataset provided by the Center for Advanced Life Cycle Engineering (CALCE) Research Center at the University of Maryland. Experimental results show that the coefficient of determination (R<sup>2</sup>) consistently exceeds 0.985, with the average absolute error in SOH prediction for four batteries remaining around 0.5%. The comparative experiments demonstrate that the proposed method has a certain degree of accuracy, robustness, and generalization capability. |
| format | Article |
| id | doaj-art-114d672c9cbf4a01aeec2761e9e324e8 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-114d672c9cbf4a01aeec2761e9e324e82025-08-20T03:06:31ZengMDPI AGApplied Sciences2076-34172025-03-01157359210.3390/app15073592A Novel SOH Estimation Method for Lithium-Ion Batteries Based on the PSO–GWO–LSSVM Prediction Model with Multi-Dimensional Health Features ExtractionXu He0Zhengpu Wu1Jinghan Bai2Junchao Zhu3Lu Lv4Lujun Wang5Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, ChinaHubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, ChinaHubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, ChinaHubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, ChinaHubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, ChinaPowerchina Equipment Research Institute Co., Ltd., Wuhan 430068, ChinaAccurate State of Health (SOH) estimation of lithium-ion batteries (LIBs) is critical for ensuring the safety of electric vehicles and improving the reliability of battery management systems (BMS). However, the use of individual health features (HFs) and the selection of hyperparameters can increase the data processing burden on the BMS and reduce the accuracy of data-driven models. To address the above issue, this paper proposes a novel SOH estimation method for lithium-ion batteries based on the PSO–GWO–LSSVM prediction model with multi-dimensional health feature extraction. To comprehensively capture the battery aging mechanisms, four categories of health features—time, energy, similarity, and second-order features—are extracted from the LIBs charging segments. The correlation between HFs and SOH is comprehensively evaluated through Pearson and Spearman correlation analyses, followed by Gaussian filtering and outlier detection to enhance feature quality. With strong generalization and robustness, least squares support vector machine (LSSVM) is widely applied to nonlinear computations and function approximation. To improve LSSVM model accuracy and efficiency, this paper develops a novel prediction model that uses particle swarm optimization (PSO) combined with grey wolf optimization (GWO) algorithms to optimize the LSSVM model. The generalization performance of the proposed method is validated through comparative experiments using a battery dataset provided by the Center for Advanced Life Cycle Engineering (CALCE) Research Center at the University of Maryland. Experimental results show that the coefficient of determination (R<sup>2</sup>) consistently exceeds 0.985, with the average absolute error in SOH prediction for four batteries remaining around 0.5%. The comparative experiments demonstrate that the proposed method has a certain degree of accuracy, robustness, and generalization capability.https://www.mdpi.com/2076-3417/15/7/3592lithium-ion batteriesstate of healthfeatures extractionparticle swarm optimizationgrey wolf optimizationleast squares support vector machine |
| spellingShingle | Xu He Zhengpu Wu Jinghan Bai Junchao Zhu Lu Lv Lujun Wang A Novel SOH Estimation Method for Lithium-Ion Batteries Based on the PSO–GWO–LSSVM Prediction Model with Multi-Dimensional Health Features Extraction Applied Sciences lithium-ion batteries state of health features extraction particle swarm optimization grey wolf optimization least squares support vector machine |
| title | A Novel SOH Estimation Method for Lithium-Ion Batteries Based on the PSO–GWO–LSSVM Prediction Model with Multi-Dimensional Health Features Extraction |
| title_full | A Novel SOH Estimation Method for Lithium-Ion Batteries Based on the PSO–GWO–LSSVM Prediction Model with Multi-Dimensional Health Features Extraction |
| title_fullStr | A Novel SOH Estimation Method for Lithium-Ion Batteries Based on the PSO–GWO–LSSVM Prediction Model with Multi-Dimensional Health Features Extraction |
| title_full_unstemmed | A Novel SOH Estimation Method for Lithium-Ion Batteries Based on the PSO–GWO–LSSVM Prediction Model with Multi-Dimensional Health Features Extraction |
| title_short | A Novel SOH Estimation Method for Lithium-Ion Batteries Based on the PSO–GWO–LSSVM Prediction Model with Multi-Dimensional Health Features Extraction |
| title_sort | novel soh estimation method for lithium ion batteries based on the pso gwo lssvm prediction model with multi dimensional health features extraction |
| topic | lithium-ion batteries state of health features extraction particle swarm optimization grey wolf optimization least squares support vector machine |
| url | https://www.mdpi.com/2076-3417/15/7/3592 |
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