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Long-sequence voltage series forecasting for internal short circuit early detection of lithium-ion batteries

Accurate early detection of internal short circuits (ISCs) is indispensable for safe and reliable application of lithium-ion batteries (LiBs). However, the major challenge is finding a reliable standard to judge whether the battery suffers from ISCs. In this work, a deep learning approach with multi...

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Detalles Bibliográficos
Autores principales: Cui, Binghan, Wang, Han, Li, Renlong, Xiang, Lizhi, Du, Jiannan, Zhao, Huaian, Li, Sai, Zhao, Xinyue, Yin, Geping, Cheng, Xinqun, Ma, Yulin, Huo, Hua, Zuo, Pengjian, Han, Guokang, Du, Chunyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318363/
https://www.ncbi.nlm.nih.gov/pubmed/37409054
http://dx.doi.org/10.1016/j.patter.2023.100732
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author Cui, Binghan
Wang, Han
Li, Renlong
Xiang, Lizhi
Du, Jiannan
Zhao, Huaian
Li, Sai
Zhao, Xinyue
Yin, Geping
Cheng, Xinqun
Ma, Yulin
Huo, Hua
Zuo, Pengjian
Han, Guokang
Du, Chunyu
author_facet Cui, Binghan
Wang, Han
Li, Renlong
Xiang, Lizhi
Du, Jiannan
Zhao, Huaian
Li, Sai
Zhao, Xinyue
Yin, Geping
Cheng, Xinqun
Ma, Yulin
Huo, Hua
Zuo, Pengjian
Han, Guokang
Du, Chunyu
author_sort Cui, Binghan
collection PubMed
description Accurate early detection of internal short circuits (ISCs) is indispensable for safe and reliable application of lithium-ion batteries (LiBs). However, the major challenge is finding a reliable standard to judge whether the battery suffers from ISCs. In this work, a deep learning approach with multi-head attention and a multi-scale hierarchical learning mechanism based on encoder-decoder architecture is developed to accurately forecast voltage and power series. By using the predicted voltage without ISCs as the standard and detecting the consistency of the collected and predicted voltage series, we develop a method to detect ISCs quickly and accurately. In this way, we achieve an average percentage accuracy of 86% on the dataset, including different batteries and the equivalent ISC resistance from 1,000 Ω to 10 Ω, indicating successful application of the ISC detection method.
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spelling pubmed-103183632023-07-05 Long-sequence voltage series forecasting for internal short circuit early detection of lithium-ion batteries Cui, Binghan Wang, Han Li, Renlong Xiang, Lizhi Du, Jiannan Zhao, Huaian Li, Sai Zhao, Xinyue Yin, Geping Cheng, Xinqun Ma, Yulin Huo, Hua Zuo, Pengjian Han, Guokang Du, Chunyu Patterns (N Y) Article Accurate early detection of internal short circuits (ISCs) is indispensable for safe and reliable application of lithium-ion batteries (LiBs). However, the major challenge is finding a reliable standard to judge whether the battery suffers from ISCs. In this work, a deep learning approach with multi-head attention and a multi-scale hierarchical learning mechanism based on encoder-decoder architecture is developed to accurately forecast voltage and power series. By using the predicted voltage without ISCs as the standard and detecting the consistency of the collected and predicted voltage series, we develop a method to detect ISCs quickly and accurately. In this way, we achieve an average percentage accuracy of 86% on the dataset, including different batteries and the equivalent ISC resistance from 1,000 Ω to 10 Ω, indicating successful application of the ISC detection method. Elsevier 2023-04-18 /pmc/articles/PMC10318363/ /pubmed/37409054 http://dx.doi.org/10.1016/j.patter.2023.100732 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Cui, Binghan
Wang, Han
Li, Renlong
Xiang, Lizhi
Du, Jiannan
Zhao, Huaian
Li, Sai
Zhao, Xinyue
Yin, Geping
Cheng, Xinqun
Ma, Yulin
Huo, Hua
Zuo, Pengjian
Han, Guokang
Du, Chunyu
Long-sequence voltage series forecasting for internal short circuit early detection of lithium-ion batteries
title Long-sequence voltage series forecasting for internal short circuit early detection of lithium-ion batteries
title_full Long-sequence voltage series forecasting for internal short circuit early detection of lithium-ion batteries
title_fullStr Long-sequence voltage series forecasting for internal short circuit early detection of lithium-ion batteries
title_full_unstemmed Long-sequence voltage series forecasting for internal short circuit early detection of lithium-ion batteries
title_short Long-sequence voltage series forecasting for internal short circuit early detection of lithium-ion batteries
title_sort long-sequence voltage series forecasting for internal short circuit early detection of lithium-ion batteries
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318363/
https://www.ncbi.nlm.nih.gov/pubmed/37409054
http://dx.doi.org/10.1016/j.patter.2023.100732
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