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dc.contributor.authorAzkue, Markel
dc.contributor.authorOca, Laura
dc.contributor.authorIRAOLA, UNAI
dc.contributor.otherLucu, M.
dc.contributor.otherMartínez Laserna, Egoitz
dc.date.accessioned2024-04-19T09:32:55Z
dc.date.available2024-04-19T09:32:55Z
dc.date.issued2022
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=170385en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6360
dc.description.abstractThe development of State-of-Charge (SoC) algorithms for Li-ion batteries involves carrying out different laboratory tests with the money and time that this entails. Furthermore, such laboratory labours must typically be repeated for each new Li-ion cell reference. In order to minimise this issue, this work proposes a new approach for developing SoC algorithms, using a Recurrent Neural Network in combination with a Transfer Learning method. The latter technique will make possible to take advantage of the data generated for previously tested cell references and use it for the development of a SoC estimation algorithm for a new cell reference. This work provides a proof-of-concept for the proposed approach, using synthetic data generated from electrochemical models, which describes the behaviour of different Li-ion cell references.en
dc.language.isoengen
dc.rights© 2022 The Authorsen
dc.subjectMachine Learningen
dc.subjectTransfer Learningen
dc.subjectlithium-ion batteriesen
dc.subjectState-of-Chargeen
dc.subjectArtificial Neural Networken
dc.titleLi-ion Battery State-of-Charge estimation algorithm with CNN-LSTM and Transfer Learning using synthetic training dataen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.source35th International Electric Vehicle Symposium & Exhibitionen
local.contributor.groupAlmacenamiento de energíaes
local.description.peerreviewedtrueen
local.contributor.otherinstitutionhttps://ror.org/03hp1m080es
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94fen
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen


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