Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
Forecasting the state of health and remaining useful life of batteries is a challenge that limits technologies such as electric vehicles. Here, the authors build an accurate battery performance forecasting system using machine learning.
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2020-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-15235-7 |
| _version_ | 1819195817264676864 |
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| author | Yunwei Zhang Qiaochu Tang Yao Zhang Jiabin Wang Ulrich Stimming Alpha A. Lee |
| author_facet | Yunwei Zhang Qiaochu Tang Yao Zhang Jiabin Wang Ulrich Stimming Alpha A. Lee |
| author_sort | Yunwei Zhang |
| collection | DOAJ |
| description | Forecasting the state of health and remaining useful life of batteries is a challenge that limits technologies such as electric vehicles. Here, the authors build an accurate battery performance forecasting system using machine learning. |
| first_indexed | 2024-12-23T02:18:47Z |
| format | Article |
| id | doaj.art-93fa48473c6748549ce982ee1772b3cf |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-23T02:18:47Z |
| publishDate | 2020-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-93fa48473c6748549ce982ee1772b3cf2022-12-21T18:03:35ZengNature PortfolioNature Communications2041-17232020-04-011111610.1038/s41467-020-15235-7Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learningYunwei Zhang0Qiaochu Tang1Yao Zhang2Jiabin Wang3Ulrich Stimming4Alpha A. Lee5Cavendish Laboratory, University of CambridgeThe Faraday InstitutionDepartment of Applied Mathematics and Theoretical Physics, University of CambridgeThe Faraday InstitutionThe Faraday InstitutionCavendish Laboratory, University of CambridgeForecasting the state of health and remaining useful life of batteries is a challenge that limits technologies such as electric vehicles. Here, the authors build an accurate battery performance forecasting system using machine learning.https://doi.org/10.1038/s41467-020-15235-7 |
| spellingShingle | Yunwei Zhang Qiaochu Tang Yao Zhang Jiabin Wang Ulrich Stimming Alpha A. Lee Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning Nature Communications |
| title | Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning |
| title_full | Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning |
| title_fullStr | Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning |
| title_full_unstemmed | Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning |
| title_short | Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning |
| title_sort | identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning |
| url | https://doi.org/10.1038/s41467-020-15235-7 |
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