| dc.contributor.author | Lizaso Eguileta, Olatz | |
| dc.contributor.author | Gil, Endika | |
| dc.contributor.author | Martinez Laserna, Egoitz | |
| dc.contributor.author | RIVAS GUTIERREZ, MIKEL | |
| dc.contributor.author | Miguel, Eduardo | |
| dc.contributor.author | IRAOLA, UNAI | |
| dc.date.accessioned | 2026-06-10T11:04:04Z | |
| dc.date.available | 2026-06-10T11:04:04Z | |
| dc.date.issued | 2024 | |
| dc.identifier.issn | 0018-9545 | en |
| dc.identifier.other | https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=172000 | en |
| dc.identifier.uri | https://hdl.handle.net/20.500.11984/14523 | |
| dc.description.abstract | The monitoring and modelling of the Li-Ion Batteries behaviour is still a major technical challenge due to the non-linearities and coupled phenomena that determine their operation. These Li-Ion Batteries can consist of thousands of cells with series/parallel connections, which suffer from operating and degradation deviations. The pursuit of new, increasingly intelligent and heavier state estimation algorithms requires a significant amount of data and computational power, which can be challenging to deploy in current Battery Management System solutions. To solve this problem, this paper proposes a Digital Twin Simulation Platform that considers all the individual cells based on the Cloud to extend the computational power and data storage capacity. This work presents validated cell models, a module-level modelling approach, and an experimental validation platform is suggested. In addition, the first results obtained when implementing the Digital Twin Simulation Platform in the Cloud are presented. | en |
| dc.language.iso | eng | en |
| dc.publisher | IEEE | en |
| dc.rights | © 2024 IEEE | en |
| dc.subject | Batteries | en |
| dc.subject | Computational modeling | en |
| dc.subject | Integrated circuit modeling | en |
| dc.subject | Data models | en |
| dc.subject | Cloud computing | en |
| dc.subject | Hysteresis | en |
| dc.subject | Voltage | en |
| dc.subject | Digital twin | en |
| dc.subject | cloud computing | en |
| dc.subject | battery models | en |
| dc.subject | state of charge | en |
| dc.title | Validation and Implementation in the Cloud of Cell-Level Models of a Digital Twin Simulation Platform | en |
| dcterms.accessRights | http://purl.org/coar/access_right/c_abf2 | en |
| dcterms.source | IEEE Transactions on Vehicular Technology | en |
| local.contributor.group | Almacenamiento de Energía | es |
| local.description.peerreviewed | true | en |
| local.description.publicationfirstpage | 7489 | en |
| local.description.publicationlastpage | 7500 | en |
| local.identifier.doi | https://doi.org/10.1109/TVT.2023.3254894 | en |
| local.embargo.enddate | 2144 | |
| local.contributor.otherinstitution | https://ror.org/03hp1m080 | es |
| local.source.details | 2024 Vol. 73, Issue: 6 | en |
| oaire.format.mimetype | application/pdf | en |
| oaire.file | $DSPACE\assetstore | en |
| oaire.resourceType | http://purl.org/coar/resource_type/c_6501 | en |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | en |
| dc.unesco.tesauro | http://vocabularies.unesco.org/thesaurus/concept9508 | en |
| oaire.funderName | Gobierno Vasco | en |
| oaire.funderIdentifier | https://ror.org/00pz2fp31 / http://data.crossref.org/fundingdata/funder/10.13039/501100003086 | en |
| oaire.fundingStream | Programa Bikaintek 2019 | en |
| oaire.awardNumber | 20-AF-W2-2019-00005 | en |
| oaire.awardTitle | BIKAINTEK | en |
| dc.unesco.clasificacion | http://skos.um.es/unesco6/3322 | en |