RT Conference Proceedings
T1 Digital twin based simulation platform for heavy duty hybrid electric vehicles
A1 Otaola, Eneko
A1 Arteta, Beñat
A1 Pérez, Joshué
A1 Sierra-Gonzalez, Andres
A1 Prieto, Pablo
AB The 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.
PB Institute of Electrical and Electronics Engineers Inc.
SN 9798350311143
SN 1550-2252
YR 2023
FD 2023
LK https://hdl.handle.net/11556/2402
UL https://hdl.handle.net/11556/2402
LA eng
NO Otaola , 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
NO conference
NO Publisher Copyright: © 2023 IEEE.
NO VII. 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.
DS TECNALIA Publications
RD 29 jul 2024