State of health estimation of individual batteries through incremental curve analysis under parameter uncertainty
Abstract The assessment of State of Health (SOH) plays a decisive role in diagnosing the health condition of Lithium‐Ion Batteries (LIBs). However, SOH estimation, particularly for individual battery cells, remains underexplored, especially under working conditions and aging patterns where battery p...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Renewable Power Generation |
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| Online Access: | https://doi.org/10.1049/rpg2.12971 |
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| _version_ | 1832579833997033472 |
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| author | Yue Zhao Qian Li Xiaohui Li Ge Zhang Hang Shi Qinghua Li |
| author_facet | Yue Zhao Qian Li Xiaohui Li Ge Zhang Hang Shi Qinghua Li |
| author_sort | Yue Zhao |
| collection | DOAJ |
| description | Abstract The assessment of State of Health (SOH) plays a decisive role in diagnosing the health condition of Lithium‐Ion Batteries (LIBs). However, SOH estimation, particularly for individual battery cells, remains underexplored, especially under working conditions and aging patterns where battery parameters cannot be fully determined. This research conducted a comparative analysis of the parameter sensitivity among three methods and proposed a novel approach to estimate the SOH in large‐capacity batteries. The proposed method integrates multi‐feature extraction with artificial intelligence techniques. Specifically, various Health Index sets (HIs) reflecting Incremental Capacity morphological features are extracted from the charging curves of LIBs. Subsequently, a method is proposed to fuse these HIs using an artificial neural network to achieve precise SOH estimation. The effectiveness of the proposed method is validated through extensive long‐term degradation experiments on Lithium Cobalt Oxide batteries. The results confirm significant attributes of the method, including high estimation accuracy, reliability, and robustness against small‐scale inconsistencies. |
| format | Article |
| id | doaj-art-f25f43b232c04f9291d9e66773e68b30 |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-f25f43b232c04f9291d9e66773e68b302025-01-30T12:15:53ZengWileyIET Renewable Power Generation1752-14161752-14242024-12-0118163582359210.1049/rpg2.12971State of health estimation of individual batteries through incremental curve analysis under parameter uncertaintyYue Zhao0Qian Li1Xiaohui Li2Ge Zhang3Hang Shi4Qinghua Li5Electric Power Research Institute of Tianjin Power System Tianjin ChinaElectric Power Research Institute of Tianjin Power System Tianjin ChinaState Grid Tianjin Electric Power Company Marketing Service Center Tianjin ChinaState Grid Tianjin Chengdong Electric Power Supply Branch Tianjin ChinaState Grid Tianjin Chengdong Electric Power Supply Branch Tianjin ChinaNational Engineering Laboratory for Electric Vehicles School of Mechanical Engineering Beijing Institute of Technology Beijing ChinaAbstract The assessment of State of Health (SOH) plays a decisive role in diagnosing the health condition of Lithium‐Ion Batteries (LIBs). However, SOH estimation, particularly for individual battery cells, remains underexplored, especially under working conditions and aging patterns where battery parameters cannot be fully determined. This research conducted a comparative analysis of the parameter sensitivity among three methods and proposed a novel approach to estimate the SOH in large‐capacity batteries. The proposed method integrates multi‐feature extraction with artificial intelligence techniques. Specifically, various Health Index sets (HIs) reflecting Incremental Capacity morphological features are extracted from the charging curves of LIBs. Subsequently, a method is proposed to fuse these HIs using an artificial neural network to achieve precise SOH estimation. The effectiveness of the proposed method is validated through extensive long‐term degradation experiments on Lithium Cobalt Oxide batteries. The results confirm significant attributes of the method, including high estimation accuracy, reliability, and robustness against small‐scale inconsistencies.https://doi.org/10.1049/rpg2.12971battery management systemsenergy storage |
| spellingShingle | Yue Zhao Qian Li Xiaohui Li Ge Zhang Hang Shi Qinghua Li State of health estimation of individual batteries through incremental curve analysis under parameter uncertainty IET Renewable Power Generation battery management systems energy storage |
| title | State of health estimation of individual batteries through incremental curve analysis under parameter uncertainty |
| title_full | State of health estimation of individual batteries through incremental curve analysis under parameter uncertainty |
| title_fullStr | State of health estimation of individual batteries through incremental curve analysis under parameter uncertainty |
| title_full_unstemmed | State of health estimation of individual batteries through incremental curve analysis under parameter uncertainty |
| title_short | State of health estimation of individual batteries through incremental curve analysis under parameter uncertainty |
| title_sort | state of health estimation of individual batteries through incremental curve analysis under parameter uncertainty |
| topic | battery management systems energy storage |
| url | https://doi.org/10.1049/rpg2.12971 |
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