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dc.contributor.authorLopetegi, Iker
dc.contributor.authorOca, Laura
dc.contributor.authorIRAOLA, UNAI
dc.contributor.otherPlett, Gregory L.
dc.contributor.otherTrimboli, Michael Scott
dc.contributor.otherde Souza, Aloisio Kawakita
dc.contributor.otherMiguel, Eduardo
dc.date.accessioned2024-03-14T14:52:53Z
dc.date.available2024-03-14T14:52:53Z
dc.date.issued2024
dc.identifier.issn0013-4651en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=175875en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6287
dc.description.abstractBattery management systems (BMSs) are required to estimate many non-measurable values that describe the actual operating condition of batteries; such as the state of charge (SOC) or the state of health (SOH). In order to improve this evaluation, many physical states and parameters can be estimated using physics-based models (PBMs). These estimates could be used to improve the control and prognosis of batteries. In this series of papers we propose a new method to estimate the internal physical states, the SOC, the SOH, and the electrode-specific state of health (eSOH) parameters of a lithium-ion battery, using interconnected sigma-point Kalman filters (SPKFs) and a single-particle model with electrolyte dynamics (SPMe). This first paper focuses on state estimation for non-aged cells. To begin, we describe and validate our electrochemical model against a high-fidelity P2D model. After, the interconnected SPKF algorithm is described and the observability of our system is analyzed, showing that the interconnected estimator approach improves an observability measure of the system. Finally, the results of the estimator are discussed, comparing the estimated variables with the truth values under initialization, measurement and modeling uncertainties. The results show that the algorithm can estimate the internal battery states with high accuracy.en
dc.language.isoengen
dc.publisherIOP Publishingen
dc.rights© 2024 IOP Publishingen
dc.subjectLithium Ion Batteryen
dc.subjectPhysics-Based Model (PBM)en
dc.subjectElectrochemical Modelen
dc.subjectSingle-Particle Model with electrolyte dynamics (SPMe)en
dc.subjectState-of-Charge (SOC) Estimationen
dc.subjectSigma-Point Kalman Filter (SPKF)en
dc.subjectObservabilityen
dc.titleA New Battery SOC/SOH/eSOH Estimation Method Using a PBM and Interconnected SPKFs: Part I. SOC and Internal Variable Estimationen
dcterms.accessRightshttp://purl.org/coar/access_right/c_f1cfen
dcterms.sourceJournal of the Electrochemical Societyen
local.contributor.groupAlmacenamiento de energíaes
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1149/1945-7111/ad30d4en
local.embargo.enddate2025/03/31
local.contributor.otherinstitutionhttps://ror.org/03hp1m080en
local.contributor.otherinstitutionhttps://ror.org/054spjc55en
local.source.detailsVol. 171. N. 3. N. art. 030519, 2024
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen
dc.unesco.campohttp://skos.um.es/unesco6/33en
dc.unesco.disciplinahttp://skos.um.es/unesco6/3322en


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