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State estimation of LiFePO4 battery using a Linear Regression Analysis
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dc.contributor.authorLim, Hee Sung-
dc.contributor.authorYun, Jin Shik-
dc.contributor.authorLee, Kyo Beum-
dc.date.issued2022-02-01-
dc.identifier.issn2287-4364-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/32585-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85126325674&origin=inward-
dc.description.abstractThis paper proposes a SOH Estimation of LiFePO4 battery management systems using a Linear Regression Analysis. Among the methods of machine learning, supervised learning learns the relationship between the input data(battery characteristic) and the output data(failure data) to find a model that is expressed as a rule or function. Unsupervised learning performs failure diagnosis and prediction by discovering patterns inherent in changing battery characteristics data during use. The algorithm estimates DCIR according to the input parameters using linear regression analysis of supervised learning, and clustering of data to confirm association with failure causes. The validity of the proposed machine learning algorithm is verified by experiment.-
dc.language.isoeng-
dc.publisherKorean Institute of Electrical Engineers-
dc.subject.meshBMS-
dc.subject.meshDCIR-
dc.subject.meshFailure data-
dc.subject.meshInput datas-
dc.subject.meshLearn+-
dc.subject.meshLFP-
dc.subject.meshLinear regression analysis-
dc.subject.meshOutput data-
dc.subject.meshSOC-
dc.subject.meshSOH-
dc.titleState estimation of LiFePO4 battery using a Linear Regression Analysis-
dc.typeArticle-
dc.citation.endPage372-
dc.citation.number2-
dc.citation.startPage366-
dc.citation.titleTransactions of the Korean Institute of Electrical Engineers-
dc.citation.volume71-
dc.identifier.bibliographicCitationTransactions of the Korean Institute of Electrical Engineers, Vol.71 No.2, pp.366-372-
dc.identifier.doi10.5370/kiee.2022.71.2.366-
dc.identifier.scopusid2-s2.0-85126325674-
dc.identifier.urlhttp://journal.auric.kr/kiee/XmlViewer/f411932-
dc.subject.keywordBattery-
dc.subject.keywordBMS-
dc.subject.keywordDCIR-
dc.subject.keywordLFP-
dc.subject.keywordLiFePO4-
dc.subject.keywordLinear Regression-
dc.subject.keywordSOC-
dc.subject.keywordSOH-
dc.type.otherArticle-
dc.identifier.pissn1975-8359-
dc.description.isoafalse-
dc.subject.subareaElectrical and Electronic Engineering-
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Lee, Kyo-Beum이교범
Department of Electrical and Computer Engineering
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