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Data-Driven Methods for Predicting the State of Health, Sta..:
Kim, Eunsong
;
Kim, Minseon
;
Kim, Juo
...
International Journal of Precision Engineering and Manufacturing. 24 (2023) 7 - p. 1281-1304 , 2023
Link:
https://doi.org/10.1007/s12541-023-00832-5
RT Journal T1 <Title>Data-Driven Methods for Predicting the State of Health, State of Charge, and Remaining Useful Life of Li-Ion Batteries: A Comprehensive Review</Title> UL https://suche.suub.uni-bremen.de/peid=cr-10.1007_s12541-023-00832-5&Exemplar=1&LAN=DE A1 Kim, Eunsong A1 Kim, Minseon A1 Kim, Juo A1 Kim, Joonchul A1 Park, Jung-Hwan A1 Kim, Kyoung-Tak A1 Park, Joung-Hu A1 Kim, Taesic A1 Min, Kyoungmin PB Springer Science and Business Media LLC YR 2023 SN 2234-7593 SN 2005-4602 JF International Journal of Precision Engineering and Manufacturing VO 24 IS 7 SP 1281 OP 1304 LK http://dx.doi.org/https://doi.org/10.1007/s12541-023-00832-5 DO https://doi.org/10.1007/s12541-023-00832-5 SF ELIB - SuUB Bremen
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