Remaining Useful Life Prediction of Lithium-Ion Batteries by Using a Denoising Transformer-Based Neural Network

Han, Y; Li, C; Zheng, L; Lei, G; Li, L

Remaining Useful Life Prediction of Lithium-Ion Batteries by Using a Denoising Transformer-Based Neural Network

Han, Y Li, C Zheng, L Lei, G

Li, L

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Energies, 2023, 16, (17)
Issue Date:: 2023-09-01

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Field	Value	Language
dc.contributor.author	Han, Y
dc.contributor.author	Li, C
dc.contributor.author	Zheng, L
dc.contributor.author	Lei, G https://orcid.org/0000-0002-8369-6802
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216
dc.date.accessioned	2024-03-27T01:45:41Z
dc.date.available	2024-03-27T01:45:41Z
dc.date.issued	2023-09-01
dc.identifier.citation	Energies, 2023, 16, (17)
dc.identifier.issn	1996-1073
dc.identifier.issn	1996-1073
dc.identifier.uri	http://hdl.handle.net/10453/177218
dc.description.abstract	In this study, we introduce a novel denoising transformer-based neural network (DTNN) model for predicting the remaining useful life (RUL) of lithium-ion batteries. The proposed DTNN model significantly outperforms traditional machine learning models and other deep learning architectures in terms of accuracy and reliability. Specifically, the DTNN achieved an (Formula presented.) value of 0.991, a mean absolute percentage error (MAPE) of 0.632%, and an absolute RUL error of 3.2, which are superior to other models such as Random Forest (RF), Decision Trees (DT), Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Dual-LSTM, and DeTransformer. These results highlight the efficacy of the DTNN model in providing precise and reliable predictions for battery RUL, making it a promising tool for battery management systems in various applications.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Energies
dc.relation.isbasedon	10.3390/en16176328
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	02 Physical Sciences, 09 Engineering
dc.subject.classification	33 Built environment and design
dc.subject.classification	40 Engineering
dc.subject.classification	51 Physical sciences
dc.title	Remaining Useful Life Prediction of Lithium-Ion Batteries by Using a Denoising Transformer-Based Neural Network
dc.type	Journal Article
utslib.citation.volume	16
utslib.for	02 Physical Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-27T01:45:40Z
pubs.issue	17
pubs.publication-status	Published
pubs.volume	16
utslib.citation.issue	17

Abstract:

In this study, we introduce a novel denoising transformer-based neural network (DTNN) model for predicting the remaining useful life (RUL) of lithium-ion batteries. The proposed DTNN model significantly outperforms traditional machine learning models and other deep learning architectures in terms of accuracy and reliability. Specifically, the DTNN achieved an (Formula presented.) value of 0.991, a mean absolute percentage error (MAPE) of 0.632%, and an absolute RUL error of 3.2, which are superior to other models such as Random Forest (RF), Decision Trees (DT), Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Dual-LSTM, and DeTransformer. These results highlight the efficacy of the DTNN model in providing precise and reliable predictions for battery RUL, making it a promising tool for battery management systems in various applications.

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http://hdl.handle.net/10453/177218