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dc.contributor.authorParsa, SMen_US
dc.contributor.authorNorozpour, Fen_US
dc.contributor.authorShoeibi, Sen_US
dc.contributor.authorShahsavar, Aen_US
dc.contributor.authorAberoumand, Sen_US
dc.contributor.authorAfrand, Men_US
dc.contributor.authorSaid, Zen_US
dc.contributor.authorKarimi, Nen_US
dc.date.accessioned2023-05-19T08:20:50Z
dc.date.available2023-03-30en_US
dc.date.issued2023en_US
dc.identifier.issn1876-1070en_US
dc.identifier.otherARTN 104854en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/87740
dc.relation.ispartofJOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERSen_US
dc.rightsThis is a pre-copyedited, author-produced version accepted for publication in Journal of the Taiwan Institute of Chemical Engineers following peer review. The version of record is available https://www.sciencedirect.com/science/article/pii/S1876107023001839?via%3Dihub
dc.subjectLi-ion batteryen_US
dc.subjectThermal regulationen_US
dc.subjectArtificial neural network (ANN)en_US
dc.subjectDeep learningen_US
dc.subjectData-driven methodsen_US
dc.subjectEnergy storageen_US
dc.titleLithium-ion battery thermal management via advanced cooling parameters: State-of-the-art review on application of machine learning with exergy, economic and environmental analysisen_US
dc.typeArticle
dc.rights.holder© 2023 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
dc.identifier.doi10.1016/j.jtice.2023.104854en_US
pubs.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:001045764900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=612ae0d773dcbdba3046f6df545e9f6aen_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.volume148en_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US


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