RT Conference Proceedings
T1 Fuel consumption analysis tool based on hybrid electric vehicle models
A1 Otaola, Eneko
A1 Arteta, Beñat
A1 Pérez, Joshué
A1 Sierra-Gonzalez, Andres
A1 Prieto, Pablo
AB The reduction of fuel consumption and Greenhouse Gases is of key importance during the last decades. Heavy-duty vehicles have been extensively researched due to the significant pollution produced. This work presents a novel simulation platform for different heavy-duty powertrain topologies: mainly combustion and parallel hybrid. This work was developed in the framework of a LONGRUN project, where a platform was implemented based on forward-looking formulation. This allows the assessment of hybrid control systems development, analysing the impact of the hybrid and electric topologies. We compared the results with a commercial tool (VECTO) adopted by the European Commission to validate the platform components over combustion powertrain analysis. In this paper, we provide a modular platform for heavy-duty vehicles, in a widely used software inside the automotive industry. Moreover, our tool has the possibility to implement hybrid electric vehicles powertrain topologies for the assessment of their control strategies and the analysis of fuel consumption and Greenhouse Gases emissions. The results show proposing results in terms of performance of the model used and fuel saving.
PB Institute of Electrical and Electronics Engineers Inc.
SN 9798350397420
YR 2023
FD 2023
LK https://hdl.handle.net/11556/2769
UL https://hdl.handle.net/11556/2769
LA eng
NO Otaola , E , Arteta , B , Pérez , J , Sierra-Gonzalez , A & Prieto , P 2023 , Fuel consumption analysis tool based on hybrid electric vehicle models . in 2023 IEEE Transportation Electrification Conference and Expo, ITEC 2023 . 2023 IEEE Transportation Electrification Conference and Expo, ITEC 2023 , Institute of Electrical and Electronics Engineers Inc. , 2023 IEEE Transportation Electrification Conference and Expo, ITEC 2023 , Detroit , United States , 21/06/23 . https://doi.org/10.1109/ITEC55900.2023.10187119
NO conference
NO Publisher Copyright: © 2023 IEEE.
NO 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