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

Abstract 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 (...

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Autores principales:	M. A. Hannan, D. N. T. How, M. S. Hossain Lipu, M. Mansor, Pin Jern Ker, Z. Y. Dong, K. S. M. Sahari, S. K. Tiong, K. M. Muttaqi, T. M. Indra Mahlia, F. Blaabjerg
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/37125291cee749d5afdfff954166db3d
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spelling	oai:doaj.org-article:37125291cee749d5afdfff954166db3d2021-12-02T17:17:40ZDeep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model10.1038/s41598-021-98915-82045-2322https://doaj.org/article/37125291cee749d5afdfff954166db3d2021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98915-8https://doaj.org/toc/2045-2322Abstract 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.M. A. HannanD. N. T. HowM. S. Hossain LipuM. MansorPin Jern KerZ. Y. DongK. S. M. SahariS. K. TiongK. M. MuttaqiT. M. Indra MahliaF. BlaabjergNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q M. A. Hannan D. N. T. How M. S. Hossain Lipu M. Mansor Pin Jern Ker Z. Y. Dong K. S. M. Sahari S. K. Tiong K. M. Muttaqi T. M. Indra Mahlia F. Blaabjerg Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model
description	Abstract 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.
format	article
author	M. A. Hannan D. N. T. How M. S. Hossain Lipu M. Mansor Pin Jern Ker Z. Y. Dong K. S. M. Sahari S. K. Tiong K. M. Muttaqi T. M. Indra Mahlia F. Blaabjerg
author_facet	M. A. Hannan D. N. T. How M. S. Hossain Lipu M. Mansor Pin Jern Ker Z. Y. Dong K. S. M. Sahari S. K. Tiong K. M. Muttaqi T. M. Indra Mahlia F. Blaabjerg
author_sort	M. A. Hannan
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 Portfolio
publishDate	2021
url	https://doaj.org/article/37125291cee749d5afdfff954166db3d
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Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model

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