Feature selection and data‐driven model for predicting the remaining useful life of lithium‐ion batteries
Abstract To ensure long and reliable operation of lithium‐ion battery storage workstations, accurate, fast, and stable lifetime prediction is crucial. However, due to the complex and interrelated ageing mechanisms of Li‐ion batteries, using physical model‐based methods for accurate description is ch...
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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 Energy Systems Integration |
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| Online Access: | https://doi.org/10.1049/esi2.12171 |
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| author | Yuhao Zhang Yunfei Han Tao Cai Jia Xie Shijie Cheng |
| author_facet | Yuhao Zhang Yunfei Han Tao Cai Jia Xie Shijie Cheng |
| author_sort | Yuhao Zhang |
| collection | DOAJ |
| description | Abstract To ensure long and reliable operation of lithium‐ion battery storage workstations, accurate, fast, and stable lifetime prediction is crucial. However, due to the complex and interrelated ageing mechanisms of Li‐ion batteries, using physical model‐based methods for accurate description is challenging. Therefore, building data‐driven models based on direct measurement data (voltage, current, capacity, etc.) during battery operation may be a more effective approach. This paper employs a time series analysis of discharge capacity/voltage curves to perform feature predication. The goal is to predict the state of health using a short‐term model and the remaining useful life of batteries using a long‐term iterative model. The validity of this method is verified using the open‐source MIT battery dataset. Comparisons with models reported in the literature demonstrate that this method is generalisable and ensures accuracy across a wider range of predictions. |
| format | Article |
| id | doaj-art-893dc9966fca46dc99655316bdf56517 |
| institution | Kabale University |
| issn | 2516-8401 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Energy Systems Integration |
| spelling | doaj-art-893dc9966fca46dc99655316bdf565172025-01-29T05:18:54ZengWileyIET Energy Systems Integration2516-84012024-12-016S177678810.1049/esi2.12171Feature selection and data‐driven model for predicting the remaining useful life of lithium‐ion batteriesYuhao Zhang0Yunfei Han1Tao Cai2Jia Xie3Shijie Cheng4State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan ChinaState Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan ChinaState Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan ChinaState Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan ChinaState Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan ChinaAbstract To ensure long and reliable operation of lithium‐ion battery storage workstations, accurate, fast, and stable lifetime prediction is crucial. However, due to the complex and interrelated ageing mechanisms of Li‐ion batteries, using physical model‐based methods for accurate description is challenging. Therefore, building data‐driven models based on direct measurement data (voltage, current, capacity, etc.) during battery operation may be a more effective approach. This paper employs a time series analysis of discharge capacity/voltage curves to perform feature predication. The goal is to predict the state of health using a short‐term model and the remaining useful life of batteries using a long‐term iterative model. The validity of this method is verified using the open‐source MIT battery dataset. Comparisons with models reported in the literature demonstrate that this method is generalisable and ensures accuracy across a wider range of predictions.https://doi.org/10.1049/esi2.12171data‐driven modelremaining lifetime prediction |
| spellingShingle | Yuhao Zhang Yunfei Han Tao Cai Jia Xie Shijie Cheng Feature selection and data‐driven model for predicting the remaining useful life of lithium‐ion batteries IET Energy Systems Integration data‐driven model remaining lifetime prediction |
| title | Feature selection and data‐driven model for predicting the remaining useful life of lithium‐ion batteries |
| title_full | Feature selection and data‐driven model for predicting the remaining useful life of lithium‐ion batteries |
| title_fullStr | Feature selection and data‐driven model for predicting the remaining useful life of lithium‐ion batteries |
| title_full_unstemmed | Feature selection and data‐driven model for predicting the remaining useful life of lithium‐ion batteries |
| title_short | Feature selection and data‐driven model for predicting the remaining useful life of lithium‐ion batteries |
| title_sort | feature selection and data driven model for predicting the remaining useful life of lithium ion batteries |
| topic | data‐driven model remaining lifetime prediction |
| url | https://doi.org/10.1049/esi2.12171 |
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