Protocol for state-of-health prediction of lithium-ion batteries based on machine learning

Accurate estimates of State of Health (SoH) are critical for characterizing the aging of lithium-ion batteries. This protocol combines feature extraction and a representative machine learning algorithm (i.e., least-squares support vector machine) for SoH prediction of lithium-ion batteries. We detai...

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Autores principales: Shu, Xing, Shen, Shiquan, Shen, Jiangwei, Zhang, Yuanjian, Li, Guang, Chen, Zheng, Liu, YongGang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Protocol
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8987387/
https://www.ncbi.nlm.nih.gov/pubmed/35403003
http://dx.doi.org/10.1016/j.xpro.2022.101272
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author Shu, Xing
Shen, Shiquan
Shen, Jiangwei
Zhang, Yuanjian
Li, Guang
Chen, Zheng
Liu, YongGang
author_facet Shu, Xing
Shen, Shiquan
Shen, Jiangwei
Zhang, Yuanjian
Li, Guang
Chen, Zheng
Liu, YongGang
author_sort Shu, Xing
collection PubMed
description Accurate estimates of State of Health (SoH) are critical for characterizing the aging of lithium-ion batteries. This protocol combines feature extraction and a representative machine learning algorithm (i.e., least-squares support vector machine) for SoH prediction of lithium-ion batteries. We detail the step-by-step estimation process, followed by validation of the constructed model with a maximum absolute error of 1.62%. Overall, the proposed approach can efficiently track the aging trajectory and ensure precise SoH prediction. For complete details on the use and execution of this protocol, please refer to Shu et al. (2021b).
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spelling pubmed-89873872022-04-08 Protocol for state-of-health prediction of lithium-ion batteries based on machine learning Shu, Xing Shen, Shiquan Shen, Jiangwei Zhang, Yuanjian Li, Guang Chen, Zheng Liu, YongGang STAR Protoc Protocol Accurate estimates of State of Health (SoH) are critical for characterizing the aging of lithium-ion batteries. This protocol combines feature extraction and a representative machine learning algorithm (i.e., least-squares support vector machine) for SoH prediction of lithium-ion batteries. We detail the step-by-step estimation process, followed by validation of the constructed model with a maximum absolute error of 1.62%. Overall, the proposed approach can efficiently track the aging trajectory and ensure precise SoH prediction. For complete details on the use and execution of this protocol, please refer to Shu et al. (2021b). Elsevier 2022-04-04 /pmc/articles/PMC8987387/ /pubmed/35403003 http://dx.doi.org/10.1016/j.xpro.2022.101272 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Shu, Xing
Shen, Shiquan
Shen, Jiangwei
Zhang, Yuanjian
Li, Guang
Chen, Zheng
Liu, YongGang
Protocol for state-of-health prediction of lithium-ion batteries based on machine learning
title Protocol for state-of-health prediction of lithium-ion batteries based on machine learning
title_full Protocol for state-of-health prediction of lithium-ion batteries based on machine learning
title_fullStr Protocol for state-of-health prediction of lithium-ion batteries based on machine learning
title_full_unstemmed Protocol for state-of-health prediction of lithium-ion batteries based on machine learning
title_short Protocol for state-of-health prediction of lithium-ion batteries based on machine learning
title_sort protocol for state-of-health prediction of lithium-ion batteries based on machine learning
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8987387/
https://www.ncbi.nlm.nih.gov/pubmed/35403003
http://dx.doi.org/10.1016/j.xpro.2022.101272
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