Digital twin based simulation platform for heavy duty hybrid electric vehicles

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dc.contributor.authorOtaola, Eneko
dc.contributor.authorArteta, Beñat
dc.contributor.authorPérez, Joshué
dc.contributor.authorSierra-Gonzalez, Andres
dc.contributor.authorPrieto, Pablo
dc.contributor.institutionPOWERTRAIN
dc.contributor.institutionCCAM
dc.date.accessioned2024-07-24T11:54:25Z
dc.date.available2024-07-24T11:54:25Z
dc.date.issued2023
dc.descriptionPublisher Copyright: © 2023 IEEE.
dc.description.abstractThe reduction of Greenhouse Gases is of great interest inside the industry and, especially, the road transport sector. Heavy-duty vehicles have been extensively researched due to their significant contribution. This work presents a digital twin (LONGRUN simulation platform) to analyse different heavy-duty vehicle aerodynamic designs and powertrain topologies. Based on a forward-looking formulation, this work allows the analysis of novel hybridisation and electrification control strategies impact. We validated our platform components against a commercial tool (VECTO) adopted by the European Commission for combustion powertrain analysis. In this article, we provide a modular platform for heavy-duty vehicles, in a widely used software inside the automotive industry. Furthermore, our platform offers the possibility to introduce customised control strategies for hybrid-electric vehicles. This work analyses the impact of parallel and serial hybrid powertrain topologies.en
dc.description.sponsorshipVII. ACKNOWLEDGEMENTS The present work is supported by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No 874972 and the ERABILH2 project, funded by the Basque Government’s Elkartek Research and Innovation program, under grant agreement KK-2021/00086.
dc.description.statusPeer reviewed
dc.identifier.citationOtaola , E , Arteta , B , Pérez , J , Sierra-Gonzalez , A & Prieto , P 2023 , Digital twin based simulation platform for heavy duty hybrid electric vehicles . in 2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings . IEEE Vehicular Technology Conference , vol. 2023-June , Institute of Electrical and Electronics Engineers Inc. , 97th IEEE Vehicular Technology Conference, VTC 2023-Spring , Florence , Italy , 20/06/23 . https://doi.org/10.1109/VTC2023-Spring57618.2023.10200353
dc.identifier.citationconference
dc.identifier.doi10.1109/VTC2023-Spring57618.2023.10200353
dc.identifier.isbn9798350311143
dc.identifier.issn1550-2252
dc.identifier.urihttps://hdl.handle.net/11556/2402
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85169823917&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings
dc.relation.ispartofseriesIEEE Vehicular Technology Conference
dc.relation.projectIDHorizon 2020 Framework Programme, H2020, 874972
dc.relation.projectIDEusko Jaurlaritza, KK-2021/00086
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordshybrid control strategies
dc.subject.keywordshybrid electric vehicle system architectures
dc.subject.keywordsvehicle simulation platform
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsElectrical and Electronic Engineering
dc.subject.keywordsApplied Mathematics
dc.subject.keywordsSDG 7 - Affordable and Clean Energy
dc.subject.keywordsSDG 9 - Industry, Innovation, and Infrastructure
dc.subject.keywordsSDG 13 - Climate Action
dc.titleDigital twin based simulation platform for heavy duty hybrid electric vehiclesen
dc.typeconference output
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