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dc.contributor.authorZhang, Yunwei
dc.contributor.authorTang, Qiaochu
dc.contributor.authorZhang, Yao
dc.contributor.authorWang, Jiabin
dc.contributor.authorStimming, Ulrich
dc.contributor.authorLee, Alpha A
dc.date.accessioned2020-05-07T00:23:32Z
dc.date.available2020-05-07T00:23:32Z
dc.date.issued2020-04-06
dc.identifier.issn2041-1723
dc.identifier.otherPMC7136228
dc.identifier.other32249782
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/305099
dc.description.abstractForecasting the state of health and remaining useful life of Li-ion batteries is an unsolved challenge that limits technologies such as consumer electronics and electric vehicles. Here, we build an accurate battery forecasting system by combining electrochemical impedance spectroscopy (EIS)-a real-time, non-invasive and information-rich measurement that is hitherto underused in battery diagnosis-with Gaussian process machine learning. Over 20,000 EIS spectra of commercial Li-ion batteries are collected at different states of health, states of charge and temperatures-the largest dataset to our knowledge of its kind. Our Gaussian process model takes the entire spectrum as input, without further feature engineering, and automatically determines which spectral features predict degradation. Our model accurately predicts the remaining useful life, even without complete knowledge of past operating conditions of the battery. Our results demonstrate the value of EIS signals in battery management systems.
dc.languageeng
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceessn: 2041-1723
dc.sourcenlmid: 101528555
dc.titleIdentifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning.
dc.typeArticle
dc.date.updated2020-05-07T00:23:32Z
prism.issueIdentifier1
prism.publicationNameNature communications
prism.volume11
dc.identifier.doi10.17863/CAM.52181
rioxxterms.versionofrecord10.1038/s41467-020-15235-7
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidZhang, Yunwei [0000-0001-7856-9190]
dc.contributor.orcidZhang, Yao [0000-0003-3780-9711]
pubs.funder-project-idRCUK | Engineering and Physical Sciences Research Council (EPSRC) (EP/S003053/1)


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International