SOH Estimation Method for Lithium-Ion Batteries Using Partial Discharge Curves Based on CGKAN
Accurate estimation of the state of health (SOH) of lithium-ion batteries is essential to ensure the safe and stable operation of equipment such as electric vehicles. To address the limitations in the accuracy and robustness of existing methods under complex operating conditions, a CNN-BiGRU-KAN (CG...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/11/5/167 |
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| author | Shengfeng He Wenhu Qin Zhonghua Yun Chao Wu Chongbin Sun |
| author_facet | Shengfeng He Wenhu Qin Zhonghua Yun Chao Wu Chongbin Sun |
| author_sort | Shengfeng He |
| collection | DOAJ |
| description | Accurate estimation of the state of health (SOH) of lithium-ion batteries is essential to ensure the safe and stable operation of equipment such as electric vehicles. To address the limitations in the accuracy and robustness of existing methods under complex operating conditions, a CNN-BiGRU-KAN (CGKAN) method for SOH estimation based on partial discharge curves is proposed. Firstly, random forest analysis is applied to extract features highly correlated with battery health from the partial discharge curve data. Next, a SOH estimation framework based on the CGKAN model is developed, where 1-Dimensional-Convolutional Neural Networks (1D-CNN) are used to extract deep features from the original data, Bidirectional Gated Recurrent Unit (BiGRU) captures the bidirectional dependencies of the time series, and Kolmogorov–Arnold Networks (KAN) enhances the modeling of complex nonlinear features through its nonlinear mapping capabilities, thereby improving the accuracy of SOH estimation. Finally, multiple experiments under different conditions are conducted, and the results demonstrate that the proposed CGKAN method, by integrating the individual advantages of 1D-CNN, BiGRU, and KAN, efficiently captures complex nonlinear patterns in battery health features and maintains stable performance across various operating conditions. |
| format | Article |
| id | doaj-art-cc2bc1281cef4b429bcdfaa6d3a25a5a |
| institution | Kabale University |
| issn | 2313-0105 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj-art-cc2bc1281cef4b429bcdfaa6d3a25a5a2025-08-20T03:47:48ZengMDPI AGBatteries2313-01052025-04-0111516710.3390/batteries11050167SOH Estimation Method for Lithium-Ion Batteries Using Partial Discharge Curves Based on CGKANShengfeng He0Wenhu Qin1Zhonghua Yun2Chao Wu3Chongbin Sun4School of Instrument Science and Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, ChinaSchool of Instrument Science and Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, ChinaSchool of Intelligent Science and Technology, Xinjiang University, No. 777 Huarui Road, Urumqi 830046, ChinaSchool of Instrument Science and Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, ChinaSchool of Instrument Science and Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, ChinaAccurate estimation of the state of health (SOH) of lithium-ion batteries is essential to ensure the safe and stable operation of equipment such as electric vehicles. To address the limitations in the accuracy and robustness of existing methods under complex operating conditions, a CNN-BiGRU-KAN (CGKAN) method for SOH estimation based on partial discharge curves is proposed. Firstly, random forest analysis is applied to extract features highly correlated with battery health from the partial discharge curve data. Next, a SOH estimation framework based on the CGKAN model is developed, where 1-Dimensional-Convolutional Neural Networks (1D-CNN) are used to extract deep features from the original data, Bidirectional Gated Recurrent Unit (BiGRU) captures the bidirectional dependencies of the time series, and Kolmogorov–Arnold Networks (KAN) enhances the modeling of complex nonlinear features through its nonlinear mapping capabilities, thereby improving the accuracy of SOH estimation. Finally, multiple experiments under different conditions are conducted, and the results demonstrate that the proposed CGKAN method, by integrating the individual advantages of 1D-CNN, BiGRU, and KAN, efficiently captures complex nonlinear patterns in battery health features and maintains stable performance across various operating conditions.https://www.mdpi.com/2313-0105/11/5/167state of healthrandom forestBiGRUKANCGKAN |
| spellingShingle | Shengfeng He Wenhu Qin Zhonghua Yun Chao Wu Chongbin Sun SOH Estimation Method for Lithium-Ion Batteries Using Partial Discharge Curves Based on CGKAN Batteries state of health random forest BiGRU KAN CGKAN |
| title | SOH Estimation Method for Lithium-Ion Batteries Using Partial Discharge Curves Based on CGKAN |
| title_full | SOH Estimation Method for Lithium-Ion Batteries Using Partial Discharge Curves Based on CGKAN |
| title_fullStr | SOH Estimation Method for Lithium-Ion Batteries Using Partial Discharge Curves Based on CGKAN |
| title_full_unstemmed | SOH Estimation Method for Lithium-Ion Batteries Using Partial Discharge Curves Based on CGKAN |
| title_short | SOH Estimation Method for Lithium-Ion Batteries Using Partial Discharge Curves Based on CGKAN |
| title_sort | soh estimation method for lithium ion batteries using partial discharge curves based on cgkan |
| topic | state of health random forest BiGRU KAN CGKAN |
| url | https://www.mdpi.com/2313-0105/11/5/167 |
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