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A Deep Learning Approach for State-of-Health Estimation of Lithium-Ion Batteries Based on a Multi-Feature and Attention Mechanism Collaboration

Bosong Zou, Mengyu Xiong, Huijie Wang, Wenlong Ding, Pengchang Jiang, Wei Hua Orcid Logo, Yong Zhang, Lisheng Zhang, Wentao Wang, Rui Tan Orcid Logo

Batteries, Volume: 9, Issue: 6, Start page: 329

Swansea University Author: Rui Tan Orcid Logo

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DOI (Published version): 10.3390/batteries9060329

Abstract

Safety issues are one of the main limitations for further application of lithium-ion batteries, and battery degradation is an important causative factor. However, current state-of-health (SOH) estimation methods are mostly developed for a single feature and a single operating condition as well as a...

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Published in: Batteries
ISSN: 2313-0105
Published: MDPI AG 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa67798
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spelling v2 67798 2024-09-25 A Deep Learning Approach for State-of-Health Estimation of Lithium-Ion Batteries Based on a Multi-Feature and Attention Mechanism Collaboration 774c33a0a76a9152ca86a156b5ae26ff 0009-0001-9278-7327 Rui Tan Rui Tan true false 2024-09-25 EAAS Safety issues are one of the main limitations for further application of lithium-ion batteries, and battery degradation is an important causative factor. However, current state-of-health (SOH) estimation methods are mostly developed for a single feature and a single operating condition as well as a single battery material system, which consequently makes it difficult to guarantee robustness and generalization. This paper proposes a data-driven and multi-feature collaborative SOH estimation method based on equal voltage interval discharge time, incremental capacity (IC) and differential thermal voltammetry (DTV) analysis for feature extraction. The deep learning model is constructed based on bi-directional long short-term memory (Bi-LSTM) with the addition of attention mechanism (AM) to focus on the important parts of the features. The proposed method is validated based on a NASA dataset and Oxford University dataset, and the results show that the proposed method has high accuracy and strong robustness. The estimated root mean squared error (RMSE) are below 0.7% and 0.3%, respectively. Compared to single features, the collaboration between multiple features and AM resulted in a 25% error improvement, and the capacity rebound is well captured. The proposed method has the potential to be applied online in an end-cloud collaboration system. Journal Article Batteries 9 6 329 MDPI AG 2313-0105 attention mechanism; lithium-ion battery; Bi-LSTM; multi-feature; state-of-health 19 6 2023 2023-06-19 10.3390/batteries9060329 COLLEGE NANME Engineering and Applied Sciences School COLLEGE CODE EAAS Swansea University Another institution paid the OA fee This research received no external funding. 2024-10-18T12:01:17.2103223 2024-09-25T21:27:05.7044316 Faculty of Science and Engineering School of Engineering and Applied Sciences - Chemical Engineering Bosong Zou 1 Mengyu Xiong 2 Huijie Wang 3 Wenlong Ding 4 Pengchang Jiang 5 Wei Hua 0000-0002-2047-9712 6 Yong Zhang 7 Lisheng Zhang 8 Wentao Wang 9 Rui Tan 0009-0001-9278-7327 10 67798__32636__7f7ed02ceb6740ea9f48a7271d26c55a.pdf 67798.VoR.pdf 2024-10-18T12:00:11.8941690 Output 8503473 application/pdf Version of Record true © 2023 by the authors.This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. true eng https://creativecommons.org/licenses/by/4.0/
title A Deep Learning Approach for State-of-Health Estimation of Lithium-Ion Batteries Based on a Multi-Feature and Attention Mechanism Collaboration
spellingShingle A Deep Learning Approach for State-of-Health Estimation of Lithium-Ion Batteries Based on a Multi-Feature and Attention Mechanism Collaboration
Rui Tan
title_short A Deep Learning Approach for State-of-Health Estimation of Lithium-Ion Batteries Based on a Multi-Feature and Attention Mechanism Collaboration
title_full A Deep Learning Approach for State-of-Health Estimation of Lithium-Ion Batteries Based on a Multi-Feature and Attention Mechanism Collaboration
title_fullStr A Deep Learning Approach for State-of-Health Estimation of Lithium-Ion Batteries Based on a Multi-Feature and Attention Mechanism Collaboration
title_full_unstemmed A Deep Learning Approach for State-of-Health Estimation of Lithium-Ion Batteries Based on a Multi-Feature and Attention Mechanism Collaboration
title_sort A Deep Learning Approach for State-of-Health Estimation of Lithium-Ion Batteries Based on a Multi-Feature and Attention Mechanism Collaboration
author_id_str_mv 774c33a0a76a9152ca86a156b5ae26ff
author_id_fullname_str_mv 774c33a0a76a9152ca86a156b5ae26ff_***_Rui Tan
author Rui Tan
author2 Bosong Zou
Mengyu Xiong
Huijie Wang
Wenlong Ding
Pengchang Jiang
Wei Hua
Yong Zhang
Lisheng Zhang
Wentao Wang
Rui Tan
format Journal article
container_title Batteries
container_volume 9
container_issue 6
container_start_page 329
publishDate 2023
institution Swansea University
issn 2313-0105
doi_str_mv 10.3390/batteries9060329
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Chemical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Chemical Engineering
document_store_str 1
active_str 0
description Safety issues are one of the main limitations for further application of lithium-ion batteries, and battery degradation is an important causative factor. However, current state-of-health (SOH) estimation methods are mostly developed for a single feature and a single operating condition as well as a single battery material system, which consequently makes it difficult to guarantee robustness and generalization. This paper proposes a data-driven and multi-feature collaborative SOH estimation method based on equal voltage interval discharge time, incremental capacity (IC) and differential thermal voltammetry (DTV) analysis for feature extraction. The deep learning model is constructed based on bi-directional long short-term memory (Bi-LSTM) with the addition of attention mechanism (AM) to focus on the important parts of the features. The proposed method is validated based on a NASA dataset and Oxford University dataset, and the results show that the proposed method has high accuracy and strong robustness. The estimated root mean squared error (RMSE) are below 0.7% and 0.3%, respectively. Compared to single features, the collaboration between multiple features and AM resulted in a 25% error improvement, and the capacity rebound is well captured. The proposed method has the potential to be applied online in an end-cloud collaboration system.
published_date 2023-06-19T12:01:15Z
_version_ 1813249288691515392
score 11.033506

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