State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Summary: Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH...

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Autores principales:	Xing Shu, Shiquan Shen, Jiangwei Shen, Yuanjian Zhang, Guang Li, Zheng Chen, Yonggang Liu
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Machine learning Energy management Energy storage Science Q
Acceso en línea:	https://doaj.org/article/d9e93810ba434890864e31059f126034
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spelling	oai:doaj.org-article:d9e93810ba434890864e31059f1260342021-11-20T05:09:04ZState of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives2589-004210.1016/j.isci.2021.103265https://doaj.org/article/d9e93810ba434890864e31059f1260342021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2589004221012347https://doaj.org/toc/2589-0042Summary: Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH prediction method based on machine learning (ML) techniques. In this paper, the conception of SOH is defined, and the state-of-the-art prediction methods are classified based on their primary implementation procedure. As an essential step in ML-based SOH algorithms, the health feature extraction methods reported in the literature are comprehensively surveyed. Next, an exhausted comparison is conducted to elaborate the development of ML-based SOH prediction techniques. Not only their advantages and disadvantages of the application in SOH prediction are reviewed but also their accuracy and execution process are fully discussed. Finally, pivotal challenges and corresponding research directions are provided for more reliable and high-fidelity SOH prediction.Xing ShuShiquan ShenJiangwei ShenYuanjian ZhangGuang LiZheng ChenYonggang LiuElsevierarticleMachine learningEnergy managementEnergy storageScienceQENiScience, Vol 24, Iss 11, Pp 103265- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Machine learning Energy management Energy storage Science Q
spellingShingle	Machine learning Energy management Energy storage Science Q Xing Shu Shiquan Shen Jiangwei Shen Yuanjian Zhang Guang Li Zheng Chen Yonggang Liu State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives
description	Summary: Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH prediction method based on machine learning (ML) techniques. In this paper, the conception of SOH is defined, and the state-of-the-art prediction methods are classified based on their primary implementation procedure. As an essential step in ML-based SOH algorithms, the health feature extraction methods reported in the literature are comprehensively surveyed. Next, an exhausted comparison is conducted to elaborate the development of ML-based SOH prediction techniques. Not only their advantages and disadvantages of the application in SOH prediction are reviewed but also their accuracy and execution process are fully discussed. Finally, pivotal challenges and corresponding research directions are provided for more reliable and high-fidelity SOH prediction.
format	article
author	Xing Shu Shiquan Shen Jiangwei Shen Yuanjian Zhang Guang Li Zheng Chen Yonggang Liu
author_facet	Xing Shu Shiquan Shen Jiangwei Shen Yuanjian Zhang Guang Li Zheng Chen Yonggang Liu
author_sort	Xing Shu
title	State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives
title_short	State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives
title_full	State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives
title_fullStr	State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives
title_full_unstemmed	State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives
title_sort	state of health prediction of lithium-ion batteries based on machine learning: advances and perspectives
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/d9e93810ba434890864e31059f126034
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State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

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