Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning

Forecasting the state of health and remaining useful life of batteries is a challenge that limits technologies such as electric vehicles. Here, the authors build an accurate battery performance forecasting system using machine learning.

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Autores principales:	Yunwei Zhang, Qiaochu Tang, Yao Zhang, Jiabin Wang, Ulrich Stimming, Alpha A. Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/93fa48473c6748549ce982ee1772b3cf
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spelling	oai:doaj.org-article:93fa48473c6748549ce982ee1772b3cf2021-12-02T14:42:29ZIdentifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning10.1038/s41467-020-15235-72041-1723https://doaj.org/article/93fa48473c6748549ce982ee1772b3cf2020-04-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-15235-7https://doaj.org/toc/2041-1723Forecasting the state of health and remaining useful life of batteries is a challenge that limits technologies such as electric vehicles. Here, the authors build an accurate battery performance forecasting system using machine learning.Yunwei ZhangQiaochu TangYao ZhangJiabin WangUlrich StimmingAlpha A. LeeNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-6 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Yunwei Zhang Qiaochu Tang Yao Zhang Jiabin Wang Ulrich Stimming Alpha A. Lee Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
description	Forecasting the state of health and remaining useful life of batteries is a challenge that limits technologies such as electric vehicles. Here, the authors build an accurate battery performance forecasting system using machine learning.
format	article
author	Yunwei Zhang Qiaochu Tang Yao Zhang Jiabin Wang Ulrich Stimming Alpha A. Lee
author_facet	Yunwei Zhang Qiaochu Tang Yao Zhang Jiabin Wang Ulrich Stimming Alpha A. Lee
author_sort	Yunwei Zhang
title	Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
title_short	Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
title_full	Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
title_fullStr	Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
title_full_unstemmed	Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
title_sort	identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/93fa48473c6748549ce982ee1772b3cf
work_keys_str_mv	AT yunweizhang identifyingdegradationpatternsoflithiumionbatteriesfromimpedancespectroscopyusingmachinelearning AT qiaochutang identifyingdegradationpatternsoflithiumionbatteriesfromimpedancespectroscopyusingmachinelearning AT yaozhang identifyingdegradationpatternsoflithiumionbatteriesfromimpedancespectroscopyusingmachinelearning AT jiabinwang identifyingdegradationpatternsoflithiumionbatteriesfromimpedancespectroscopyusingmachinelearning AT ulrichstimming identifyingdegradationpatternsoflithiumionbatteriesfromimpedancespectroscopyusingmachinelearning AT alphaalee identifyingdegradationpatternsoflithiumionbatteriesfromimpedancespectroscopyusingmachinelearning
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Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning

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