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DC Field | Value | Language |
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dc.contributor.author | Dong, Z | - |
dc.contributor.author | Gu, S | - |
dc.contributor.author | Zhou, S | - |
dc.contributor.author | Yang, M | - |
dc.contributor.author | Lai, CS | - |
dc.contributor.author | Gao, M | - |
dc.contributor.author | Ji, X | - |
dc.date.accessioned | 2024-10-01T10:54:46Z | - |
dc.date.available | 2024-10-01T10:54:46Z | - |
dc.date.issued | 2024-08-16 | - |
dc.identifier | ORCiD: Zhekang Dong https://orcid.org/0000-0003-4639-3834 | - |
dc.identifier | ORCiD: Chun Sing Lai https://orcid.org/0000-0002-4169-4438 | - |
dc.identifier | ORCiD: Mingyu Gao https://orcid.org/0000-0002-5930-9526 | - |
dc.identifier | ORCiD: Xiaoyue Ji https://orcid.org/0000-0002-3526-5215 | - |
dc.identifier.citation | Dong, Z. et al. (2024) 'Periodic Segmentation Transformer-Based Internal Short Circuit Detection Method for Battery Packs', IEEE Transactions on Transportation Electrification, 0 (early access), pp. 1 - 11. doi: 10.1109/TTE.2024.3444453. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/29858 | - |
dc.description.abstract | With the rapid evolution of electric vehicles (EVs), assuring the security and dependability of battery packs has acquired paramount significance. Internal short circuit (ISC) within EV battery packs poses a threat to the safety and reliability of EVs. Most of existing ISC detection methods still suffer from two limitations, i.e., the dataset incompleteness and poor feature representation. To address these challenges, we develop a periodic segmentation Transformer-based ISC detection method for battery packs. Firstly, considering three different operating conditions, a comprehensive dataset encompassing three distinct ISC severity levels is constructed. Secondly, to facilitate understanding of the proposed model design, a discrete wavelet transform-based periodicity analysis module and a time-oriented segmenting module are developed. This dual-module design empowers the model to adjust the length of sliding windows adaptively, and enables the joint capture of temporal-spatial and periodic information, significantly enhancing the feature representation ability. Thirdly, experimental results show that our method outperforms the best state-of-the-art in terms of accuracy (average F1 score improved by 24.2%). Finally, robustness analysis and generalization analysis are conducted. The former one demonstrates robustness in terms of parameters within the adaptive aggregation module and input data length; the latter one demonstrates generality of feature extraction method. | en_US |
dc.description.sponsorship | National Major Scientific Research Instrument Development Project of China (Grant Number: 62227802); Fundamental Research Funds for the Provincial University of Zhejiang (Grant Number: GK229909299001-06); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62001149 and 62206062). | en_US |
dc.format.extent | 1 - 11 | - |
dc.format.medium | Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © 2024 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works (see: https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/). | - |
dc.rights.uri | https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | - |
dc.subject | internal short circuit | en_US |
dc.subject | fault detection | en_US |
dc.subject | battery packs | en_US |
dc.subject | transformer-based neural network | en_US |
dc.title | Periodic Segmentation Transformer-Based Internal Short Circuit Detection Method for Battery Packs | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/TTE.2024.3444453 | - |
dc.relation.isPartOf | IEEE Transactions on Transportation Electrification | - |
pubs.issue | early access | - |
pubs.publication-status | Published | - |
pubs.volume | 0 | - |
dc.identifier.eissn | 2332-7782 | - |
dc.rights.holder | Institute of Electrical and Electronics Engineers (IEEE) | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © 2024 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works (see: https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/). | 2.37 MB | Adobe PDF | View/Open |
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