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dc.contributor.author홍승호-
dc.date.accessioned2021-12-23T04:20:36Z-
dc.date.available2021-12-23T04:20:36Z-
dc.date.issued2021-01-
dc.identifier.citationIEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, v. 16, Issue. 2, Page. 206-214en_US
dc.identifier.issn1931-4973-
dc.identifier.issn1931-4981-
dc.identifier.urihttps://onlinelibrary.wiley.com/doi/full/10.1002/tee.23287-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/167027-
dc.description.abstractThe prognosis of time-to-failure for a battery can avoid the failure caused by battery performance loss. In this paper, a novel and effective algorithm is proposed to predict the remaining useful life of lithium-ion batteries. The extended Kalman particle filter is used to improve particle degradation problem existing in standard particle filter algorithm. In order to fit battery capacity degradation, a transformed model is proposed based on double exponential empirical degradation model. It can reduce the number of parameters and the training difficulty of parametersen_US
dc.language.isoen_USen_US
dc.publisherWILEYen_US
dc.titleRemaining Useful Life Prediction of Lithium Batteries Based on Extended Kalman Particle Filteren_US
dc.typeArticleen_US
dc.relation.no2-
dc.relation.volume16-
dc.identifier.doi10.1002/tee.23287-
dc.relation.page206-214-
dc.relation.journalIEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING-
dc.contributor.googleauthorZhang, Ning-
dc.contributor.googleauthorXu, Aidong-
dc.contributor.googleauthorWang, Kai-
dc.contributor.googleauthorHan, Xiaojia-
dc.contributor.googleauthorHong, Wenhuan-
dc.contributor.googleauthorHong, Seung Ho-
dc.relation.code2021003671-
dc.sector.campusE-
dc.sector.daehakCOLLEGE OF ENGINEERING SCIENCES[E]-
dc.sector.departmentDIVISION OF ELECTRICAL ENGINEERING-
dc.identifier.pidshhong-
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COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
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