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dc.contributor.authorYeregui, Josu
dc.contributor.authorOca, Laura
dc.contributor.authorLopetegi, Iker
dc.contributor.authorGARAYALDE, ERIK
dc.contributor.authorAizpurua, Manex
dc.contributor.authorIRAOLA, UNAI
dc.date.accessioned2024-02-02T08:53:20Z
dc.date.available2024-02-02T08:53:20Z
dc.date.issued2023
dc.identifier.issn2352-152X
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=173387
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6245
dc.description.abstractThis paper presents a sequential model based on Physic Based Models (PBM) and Artificial Intelligence Models (AI) focused on the estimation of the State of Charge (SoC). The PBM can provide interesting information about the internal physical variables of the battery, which relate directly with the momentary SoC of the cell. This way, we can use this information to feed the AI model alongside with application measurements to obtain an accurate SoC estimation. By their nature, PBMs can potentially provide immense numbers of internal variables, some of which are irrelevant and redundant for the AI model. To solve this, a feature selection technique based on the regression score is introduced between both models. Selecting the most relevant variables we can build a model that reduces the computational cost of the model while improving the performance compared to using every feature. With this baseline, a Reduced-Order Model (ROM) with parameters from literature has been implemented in the PBM part and a Long-Short Term Memory (LSTM) network on the ML side. With this configuration and the PBM simplification the model needs little laboratory tests and low computational cost to outperform alternative solutions, which has been experimentally validated in different operating conditions of the cell.en
dc.language.isoeng
dc.publisherElsevier
dc.rights© 2023 Elsevier Ltd
dc.titleState of charge estimation combining physics-based and artificial intelligence models for Lithium-ion batteries
dcterms.accessRightshttp://purl.org/coar/access_right/c_f1cf
dcterms.sourceJournal of Energy Storage
local.contributor.groupAlmacenamiento de energía
local.contributor.groupEnergía eléctrica
local.description.peerreviewedtrue
local.identifier.doihttps://doi.org/10.1016/j.est.2023.108883
local.embargo.enddate2025-12-31
local.source.detailsVol. 73. Parte A. N. art. 108883, 2023
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa


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