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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorAzkue, Markel
dc.contributor.authorAizpuru, Iosu
dc.contributor.otherLucu, Mattin
dc.contributor.otherMartínez Laserna, Egoitz
dc.date.accessioned2021-09-06T14:36:54Z
dc.date.available2021-09-06T14:36:54Z
dc.date.issued2021
dc.identifier.issn2032-6653en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=164554en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5367
dc.description.abstractGetting accurate lifetime predictions for a particular cell chemistry remains a challenging process, largely dependent on time and cost-intensive experimental battery testing. This paper proposes a transfer learning (TL) method to develop LIB ageing models, which allow for the leveraging of experimental laboratory testing data previously obtained for a different cell technology. The TL method is implemented through Neural Networks models, using LiNiMnCoO2/C laboratory ageing data as a baseline model. The obtained TL model achieves an 1.01% overall error for a broad range of operating conditions, using for retraining only two experimental ageing tests of LiFePO4/C cells.en
dc.language.isoengen
dc.publisherMDPIen
dc.rights© 2021 by the authors. Licensee MDPIen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectmachine learningen
dc.subjecttransfer learningen
dc.subjectlithium-ion batteriesen
dc.subjectcalendar ageingen
dc.subjectartificial neural networken
dc.titleCalendar Ageing Model for Li-Ion Batteries Using Transfer Learning Methodsen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceWorld Electric Vehicle Journalen
local.contributor.groupAlmacenamiento de energíaes
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.3390/wevj12030145en
local.rights.publicationfeeAPCen
local.rights.publicationfeeamount920 EUR 1000 CHFen
local.contributor.otherinstitutionhttps://ror.org/03hp1m080es
local.source.detailsVol 12. N. 3. N. artículo. 145, 2021en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85en


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International