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ICFormer: A Deep Learning model for informed lithium-ion battery diagnosis and early knee detection

dc.contributor.authorCosta Cortéz, Nahuel Alejandro 
dc.contributor.authorAnseán González, David 
dc.contributor.authorDubarry, M.
dc.contributor.authorSánchez Ramos, Luciano 
dc.date.accessioned2024-02-05T09:08:37Z
dc.date.available2024-02-05T09:08:37Z
dc.date.issued2024
dc.identifier.citationJournal of Power Sources, 592 (2024); doi:10.1016/j.jpowsour.2023.233910
dc.identifier.issn0378-7753
dc.identifier.urihttps://hdl.handle.net/10651/71143
dc.description.sponsorshipThis work has been partially supported by the Ministry of Economy, Industry and Competitiveness (‘‘Ministerio de Economía, Industria 𝑦 Competitividad’’) from Spain/FEDER under grants PID2020-112726-RB-I00 and PID2022-141792OB-I00, and by Principado de Asturias, grant SV-PA-21-AYUD/2021/50994. M.D. was funded by the Office of Naval Research (ONR), grant number N00014-19-1-2159.spa
dc.language.isoengspa
dc.relation.ispartofJournal of Power Sources, 592spa
dc.rightsAtribución 4.0 Internacional*
dc.rights© 2023 The Author(s). Published by Elsevier B.V.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleICFormer: A Deep Learning model for informed lithium-ion battery diagnosis and early knee detectionspa
dc.typejournal articlespa
dc.identifier.doi10.1016/j.jpowsour.2023.233910
dc.relation.projectIDPID2020-112726-RB-I00spa
dc.relation.projectIDPID2022-141792OB-I00spa
dc.relation.projectIDSV-PA-21-AYUD/2021/50994spa
dc.relation.publisherversionhttps://doi.org/10.1016/j.jpowsour.2023.233910spa
dc.rights.accessRightsopen accessspa
dc.type.hasVersionVoRspa


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Atribución 4.0 Internacional
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