dc.contributor.author | Costa Cortéz, Nahuel Alejandro | |
dc.contributor.author | Anseán González, David | |
dc.contributor.author | Dubarry, M. | |
dc.contributor.author | Sánchez Ramos, Luciano | |
dc.date.accessioned | 2024-02-05T09:08:37Z | |
dc.date.available | 2024-02-05T09:08:37Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Journal of Power Sources, 592 (2024); doi:10.1016/j.jpowsour.2023.233910 | |
dc.identifier.issn | 0378-7753 | |
dc.identifier.uri | https://hdl.handle.net/10651/71143 | |
dc.description.sponsorship | This 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.iso | eng | spa |
dc.relation.ispartof | Journal of Power Sources, 592 | spa |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights | © 2023 The Author(s). Published by Elsevier B.V. | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.title | ICFormer: A Deep Learning model for informed lithium-ion battery diagnosis and early knee detection | spa |
dc.type | journal article | spa |
dc.identifier.doi | 10.1016/j.jpowsour.2023.233910 | |
dc.local.notes | OA ATUO23 | |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-112726RB-I00/ES/INTELIGENCIA COMPUTACIONAL PARA LA MITIGACION DE EMISIONES: NUEVAS METODOLOGIAS DE APRENDIZAJE CON DATOS INCOMPLETOS/ | spa |
dc.relation.projectID | PID2022-141792OB-I00 | spa |
dc.relation.projectID | SV-PA-21-AYUD/2021/50994 | spa |
dc.relation.publisherversion | https://doi.org/10.1016/j.jpowsour.2023.233910 | spa |
dc.rights.accessRights | open access | spa |
dc.type.hasVersion | VoR | spa |