Digital twin based simulation platform for heavy duty hybrid electric vehicles

Eneko Otaola; Beñat Arteta; Joshué Pérez; Andres Sierra-Gonzalez; Pablo Prieto

doi:10.1109/VTC2023-Spring57618.2023.10200353

Digital twin based simulation platform for heavy duty hybrid electric vehicles

Eneko Otaola^*
, Beñat Arteta
, Joshué Pérez
, Andres Sierra-Gonzalez
, Pablo Prieto

^*Corresponding author for this work

Basque Research and Technology Alliance (BRTA)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350311143
DOIs	https://doi.org/10.1109/VTC2023-Spring57618.2023.10200353
Publication status	Published - 2023
Event	97th IEEE Vehicular Technology Conference, VTC 2023-Spring - Florence, Italy Duration: 20 Jun 2023 → 23 Jun 2023

Publication series

Name	IEEE Vehicular Technology Conference
Volume	2023-June
ISSN (Print)	1550-2252

Conference

Conference	97th IEEE Vehicular Technology Conference, VTC 2023-Spring
Country/Territory	Italy
City	Florence
Period	20/06/23 → 23/06/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure
SDG 13 Climate Action

Keywords

hybrid control strategies
hybrid electric vehicle system architectures
vehicle simulation platform

Access to Document

10.1109/VTC2023-Spring57618.2023.10200353

Cite this

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.. https://doi.org/10.1109/VTC2023-Spring57618.2023.10200353

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title = "Digital twin based simulation platform for heavy duty hybrid electric vehicles",

abstract = "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.",

keywords = "hybrid control strategies, hybrid electric vehicle system architectures, vehicle simulation platform",

author = "Eneko Otaola and Be{\~n}at Arteta and Joshu{\'e} P{\'e}rez and Andres Sierra-Gonzalez and Pablo Prieto",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 97th IEEE Vehicular Technology Conference, VTC 2023-Spring ; Conference date: 20-06-2023 Through 23-06-2023",

year = "2023",

doi = "10.1109/VTC2023-Spring57618.2023.10200353",

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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

Digital twin based simulation platform for heavy duty hybrid electric vehicles. / Otaola, Eneko; Arteta, Beñat; Pérez, Joshué et al.
2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (IEEE Vehicular Technology Conference; Vol. 2023-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Digital twin based simulation platform for heavy duty hybrid electric vehicles

AU - Otaola, Eneko

AU - Arteta, Beñat

AU - Pérez, Joshué

AU - Sierra-Gonzalez, Andres

AU - Prieto, Pablo

PY - 2023

Y1 - 2023

N2 - 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.

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.

KW - hybrid control strategies

KW - hybrid electric vehicle system architectures

KW - vehicle simulation platform

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DO - 10.1109/VTC2023-Spring57618.2023.10200353

M3 - Conference contribution

AN - SCOPUS:85169823917

T3 - IEEE Vehicular Technology Conference

BT - 2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 97th IEEE Vehicular Technology Conference, VTC 2023-Spring

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ER -

Digital twin based simulation platform for heavy duty hybrid electric vehicles

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this