Erregistro soila

dc.rights.licenseAttribution 4.0 International
dc.contributor.authorAzkue, Markel
dc.contributor.authorMiguel, Eduardo
dc.contributor.authorOca, Laura
dc.contributor.authorIRAOLA, UNAI
dc.contributor.otherMartínez Laserna, Egoitz
dc.date.accessioned2024-02-02T08:53:17Z
dc.date.available2024-02-02T08:53:17Z
dc.date.issued2023
dc.identifier.issn2032-6653
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=173318
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6238
dc.description.abstractCreating SoC algorithms for Li-ion batteries based on neural networks requires a large amount of training data, since it is necessary to test the batteries under different conditions so that the algorithm learns the relationship between the different inputs and the output. Obtaining such data through laboratory tests is costly and time consuming; therefore, in this article, a neural network has been trained with data generated synthetically using electrochemical models. These models allow us to obtain relevant data related to different conditions at a minimum cost over a short period of time. By means of the different training rounds carried out using these data, it has been studied how the different hyperparameters affect the behaviour of the algorithm, creating a robust and accurate algorithm. To adapt this approach to new battery references or chemistries, transfer learning techniques can be employed.en
dc.language.isoeng
dc.publisherMDPI
dc.rights© 2023 The Authors
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectcomputer intelligence
dc.subjectLi-ion battery
dc.subjectestimation algorithm
dc.subjectODS 7 Energía asequible y no contaminante
dc.subjectODS 9 Industria, innovación e infraestructura
dc.subjectstate of charge
dc.subjectsynthetic data
dc.titleCreating a Robust SoC Estimation Algorithm Based on LSTM Units and Trained with Synthetic Data
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2
dcterms.sourceWorld Electric Vehicle Journal
local.contributor.groupAlmacenamiento de energía
local.description.peerreviewedtrue
local.identifier.doihttps://doi.org/10.3390/wevj14070197
local.contributor.otherinstitutionhttps://ror.org/03hp1m080
local.source.detailsVol. 14. N. 7. N. art. 197
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
oaire.funderNameEuropean Commission
oaire.funderIdentifierhttps://ror.org/00k4n6c32 http://data.crossref.org/fundingdata/funder/10.13039/501100000780
oaire.fundingStreamH2020
oaire.awardNumber963522
oaire.awardTitleLightweight Battery System for Extended Range at Improved Safety (LIBERTY)
oaire.awardURIhttps://doi.org/10.3030/963522


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