Anti‐interference lithium‐ion battery intelligent perception for thermal fault detection and localization
Abstract Lithium‐ion batteries are widely employed in electric vehicles, power grid energy storage, and other fields. Thermal fault diagnostics for battery packs is crucial to preventing thermal runaway from impairing the safe operation and extended cycle service life of batteries. Therefore, a lith...
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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/esi2.12158 |
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| author | Luyu Tian Chaoyu Dong Rui Wang Yunfei Mu Hongjie Jia |
| author_facet | Luyu Tian Chaoyu Dong Rui Wang Yunfei Mu Hongjie Jia |
| author_sort | Luyu Tian |
| collection | DOAJ |
| description | Abstract Lithium‐ion batteries are widely employed in electric vehicles, power grid energy storage, and other fields. Thermal fault diagnostics for battery packs is crucial to preventing thermal runaway from impairing the safe operation and extended cycle service life of batteries. Therefore, a lithium‐ion battery thermal fault diagnosis model based on deep learning algorithms is presented, which includes three parts: autoencoder denoising network, coarse mask generator, and mask precise adjustment. Autoencoder denoising network can reduce data noise during thermal imaging acquisition, improve the anti‐interference ability of diagnostic models, and ensure the accuracy of thermal runaway diagnosis. A two‐stage diagnostic structure is then formulated by the coarse mask generator and mask precise adjustment, which enable quick identification, categorisation, and localisation of thermal fault battery cells. According to the test results, the segmentation boundary is more distinct and is capable of matching the original image's level. The recognition accuracy of the thermal diagnosis model for faulty batteries is close to 100%. After denoising by the autoencoder, the prediction results improved by 22% compared to non‐local mean denoising and by about 32% compared to noisy images. |
| format | Article |
| id | doaj-art-cb05fd79d6224ec9bdbd3b27ca7f0bfb |
| institution | OA Journals |
| issn | 2516-8401 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Energy Systems Integration |
| spelling | doaj-art-cb05fd79d6224ec9bdbd3b27ca7f0bfb2025-08-20T01:59:53ZengWileyIET Energy Systems Integration2516-84012024-12-016459360510.1049/esi2.12158Anti‐interference lithium‐ion battery intelligent perception for thermal fault detection and localizationLuyu Tian0Chaoyu Dong1Rui Wang2Yunfei Mu3Hongjie Jia4School of Electrical and Information Engineering Tianjin University Tianjin ChinaNanyang Technological University Singapore SingaporeCollege of Information Science and Engineering Northeastern University Shenyang ChinaSchool of Electrical and Information Engineering Tianjin University Tianjin ChinaSchool of Electrical and Information Engineering Tianjin University Tianjin ChinaAbstract Lithium‐ion batteries are widely employed in electric vehicles, power grid energy storage, and other fields. Thermal fault diagnostics for battery packs is crucial to preventing thermal runaway from impairing the safe operation and extended cycle service life of batteries. Therefore, a lithium‐ion battery thermal fault diagnosis model based on deep learning algorithms is presented, which includes three parts: autoencoder denoising network, coarse mask generator, and mask precise adjustment. Autoencoder denoising network can reduce data noise during thermal imaging acquisition, improve the anti‐interference ability of diagnostic models, and ensure the accuracy of thermal runaway diagnosis. A two‐stage diagnostic structure is then formulated by the coarse mask generator and mask precise adjustment, which enable quick identification, categorisation, and localisation of thermal fault battery cells. According to the test results, the segmentation boundary is more distinct and is capable of matching the original image's level. The recognition accuracy of the thermal diagnosis model for faulty batteries is close to 100%. After denoising by the autoencoder, the prediction results improved by 22% compared to non‐local mean denoising and by about 32% compared to noisy images.https://doi.org/10.1049/esi2.12158battery storage plantselectric vehiclesrenewable energy sources |
| spellingShingle | Luyu Tian Chaoyu Dong Rui Wang Yunfei Mu Hongjie Jia Anti‐interference lithium‐ion battery intelligent perception for thermal fault detection and localization IET Energy Systems Integration battery storage plants electric vehicles renewable energy sources |
| title | Anti‐interference lithium‐ion battery intelligent perception for thermal fault detection and localization |
| title_full | Anti‐interference lithium‐ion battery intelligent perception for thermal fault detection and localization |
| title_fullStr | Anti‐interference lithium‐ion battery intelligent perception for thermal fault detection and localization |
| title_full_unstemmed | Anti‐interference lithium‐ion battery intelligent perception for thermal fault detection and localization |
| title_short | Anti‐interference lithium‐ion battery intelligent perception for thermal fault detection and localization |
| title_sort | anti interference lithium ion battery intelligent perception for thermal fault detection and localization |
| topic | battery storage plants electric vehicles renewable energy sources |
| url | https://doi.org/10.1049/esi2.12158 |
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