Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model

article; deep learning; environmental temperature; human

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Main Authors:	Hannan M.A., How D.N.T., Lipu M.S.H., Mansor M., Ker P.J., Dong Z.Y., Sahari K.S.M., Tiong S.K., Muttaqi K.M., Mahlia T.M.I., Blaabjerg F.
Other Authors:	7103014445
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Published:	Nature Research 2023
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Institution:	Universiti Tenaga Nasional

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spelling	my.uniten.dspace-258772023-05-29T17:05:23Z Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model Hannan M.A. How D.N.T. Lipu M.S.H. Mansor M. Ker P.J. Dong Z.Y. Sahari K.S.M. Tiong S.K. Muttaqi K.M. Mahlia T.M.I. Blaabjerg F. 7103014445 57212923888 36518949700 6701749037 37461740800 57221211074 57218170038 15128307800 55582332500 56997615100 7004992352 article; deep learning; environmental temperature; human Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell. � 2021, The Author(s). Final 2023-05-29T09:05:23Z 2023-05-29T09:05:23Z 2021 Article 10.1038/s41598-021-98915-8 2-s2.0-85116380133 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116380133&doi=10.1038%2fs41598-021-98915-8&partnerID=40&md5=80d54626b1e2252e049040c4220484af https://irepository.uniten.edu.my/handle/123456789/25877 11 1 19541 All Open Access, Gold, Green Nature Research Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
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country	Malaysia
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content_source	UNITEN Institutional Repository
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description	article; deep learning; environmental temperature; human
author2	7103014445
author_facet	7103014445 Hannan M.A. How D.N.T. Lipu M.S.H. Mansor M. Ker P.J. Dong Z.Y. Sahari K.S.M. Tiong S.K. Muttaqi K.M. Mahlia T.M.I. Blaabjerg F.
format	Article
author	Hannan M.A. How D.N.T. Lipu M.S.H. Mansor M. Ker P.J. Dong Z.Y. Sahari K.S.M. Tiong S.K. Muttaqi K.M. Mahlia T.M.I. Blaabjerg F.
spellingShingle	Hannan M.A. How D.N.T. Lipu M.S.H. Mansor M. Ker P.J. Dong Z.Y. Sahari K.S.M. Tiong S.K. Muttaqi K.M. Mahlia T.M.I. Blaabjerg F. Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model
author_sort	Hannan M.A.
title	Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model
title_short	Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model
title_full	Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model
title_fullStr	Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model
title_full_unstemmed	Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model
title_sort	deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model
publisher	Nature Research
publishDate	2023
_version_	1806427600963239936

Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model

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