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Realistic fault detection of li-ion battery via dynamical deep learning
Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell failures, facilitate battery deployment, and promote low-carbon economies. Despite the recent progress in artificial intelligence, anomaly detection methods are not customized for or validated in realistic batte...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517941/ https://www.ncbi.nlm.nih.gov/pubmed/37741826 http://dx.doi.org/10.1038/s41467-023-41226-5 |
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author | Zhang, Jingzhao Wang, Yanan Jiang, Benben He, Haowei Huang, Shaobo Wang, Chen Zhang, Yang Han, Xuebing Guo, Dongxu He, Guannan Ouyang, Minggao |
author_facet | Zhang, Jingzhao Wang, Yanan Jiang, Benben He, Haowei Huang, Shaobo Wang, Chen Zhang, Yang Han, Xuebing Guo, Dongxu He, Guannan Ouyang, Minggao |
author_sort | Zhang, Jingzhao |
collection | PubMed |
description | Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell failures, facilitate battery deployment, and promote low-carbon economies. Despite the recent progress in artificial intelligence, anomaly detection methods are not customized for or validated in realistic battery settings due to the complex failure mechanisms and the lack of real-world testing frameworks with large-scale datasets. Here, we develop a realistic deep-learning framework for electric vehicle (EV) LiB anomaly detection. It features a dynamical autoencoder tailored for dynamical systems and configured by social and financial factors. We test our detection algorithm on released datasets comprising over 690,000 LiB charging snippets from 347 EVs. Our model overcomes the limitations of state-of-the-art fault detection models, including deep learning ones. Moreover, it reduces the expected direct EV battery fault and inspection costs. Our work highlights the potential of deep learning in improving LiB safety and the significance of social and financial information in designing deep learning models. |
format | Online Article Text |
id | pubmed-10517941 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105179412023-09-25 Realistic fault detection of li-ion battery via dynamical deep learning Zhang, Jingzhao Wang, Yanan Jiang, Benben He, Haowei Huang, Shaobo Wang, Chen Zhang, Yang Han, Xuebing Guo, Dongxu He, Guannan Ouyang, Minggao Nat Commun Article Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell failures, facilitate battery deployment, and promote low-carbon economies. Despite the recent progress in artificial intelligence, anomaly detection methods are not customized for or validated in realistic battery settings due to the complex failure mechanisms and the lack of real-world testing frameworks with large-scale datasets. Here, we develop a realistic deep-learning framework for electric vehicle (EV) LiB anomaly detection. It features a dynamical autoencoder tailored for dynamical systems and configured by social and financial factors. We test our detection algorithm on released datasets comprising over 690,000 LiB charging snippets from 347 EVs. Our model overcomes the limitations of state-of-the-art fault detection models, including deep learning ones. Moreover, it reduces the expected direct EV battery fault and inspection costs. Our work highlights the potential of deep learning in improving LiB safety and the significance of social and financial information in designing deep learning models. Nature Publishing Group UK 2023-09-23 /pmc/articles/PMC10517941/ /pubmed/37741826 http://dx.doi.org/10.1038/s41467-023-41226-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhang, Jingzhao Wang, Yanan Jiang, Benben He, Haowei Huang, Shaobo Wang, Chen Zhang, Yang Han, Xuebing Guo, Dongxu He, Guannan Ouyang, Minggao Realistic fault detection of li-ion battery via dynamical deep learning |
title | Realistic fault detection of li-ion battery via dynamical deep learning |
title_full | Realistic fault detection of li-ion battery via dynamical deep learning |
title_fullStr | Realistic fault detection of li-ion battery via dynamical deep learning |
title_full_unstemmed | Realistic fault detection of li-ion battery via dynamical deep learning |
title_short | Realistic fault detection of li-ion battery via dynamical deep learning |
title_sort | realistic fault detection of li-ion battery via dynamical deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517941/ https://www.ncbi.nlm.nih.gov/pubmed/37741826 http://dx.doi.org/10.1038/s41467-023-41226-5 |
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