LAPSE:2023.23048
Published Article

LAPSE:2023.23048
Electric Powertrain Topology Analysis and Design for Heavy-Duty Trucks
March 27, 2023
Abstract
Powertrain system design optimization is an unexplored territory for battery electric trucks, which only recently have been seen as a feasible solution for sustainable road transport. To investigate the potential of these vehicles, in this paper, a variety of new battery electric powertrain topologies for heavy-duty trucks is studied. Thereby, topological design considerations are analyzed related to having: (a) a central or distributed drive system (individually-driven wheels); (b) a single or a multi-speed gearbox; and finally, (c) a single or multiple electric machines. For reasons of comparison, each concurrent powertrain topology is optimized using a bilevel optimization framework, incorporating both powertrain components and control design. The results show that the combined choice of powertrain topology and number of gears in the gearbox can result in a 5.6% total-cost-of-ownership variation of the vehicle and can, significantly, influence the optimal sizing of the electric machine(s). The lowest total-cost-of-ownership is achieved by a distributed topology with two electric machines and two two-speed gearboxes. Furthermore, results show that the largest average reduction in total-cost-of-ownership is achieved by choosing a distributed drive over a central drive topology (−1.0%); followed by using a two-speed gearbox over a single speed (−0.6%); and lastly, by using two electric machines over using one for the central drive topologies (−0.3%).
Powertrain system design optimization is an unexplored territory for battery electric trucks, which only recently have been seen as a feasible solution for sustainable road transport. To investigate the potential of these vehicles, in this paper, a variety of new battery electric powertrain topologies for heavy-duty trucks is studied. Thereby, topological design considerations are analyzed related to having: (a) a central or distributed drive system (individually-driven wheels); (b) a single or a multi-speed gearbox; and finally, (c) a single or multiple electric machines. For reasons of comparison, each concurrent powertrain topology is optimized using a bilevel optimization framework, incorporating both powertrain components and control design. The results show that the combined choice of powertrain topology and number of gears in the gearbox can result in a 5.6% total-cost-of-ownership variation of the vehicle and can, significantly, influence the optimal sizing of the electric machine(s). The lowest total-cost-of-ownership is achieved by a distributed topology with two electric machines and two two-speed gearboxes. Furthermore, results show that the largest average reduction in total-cost-of-ownership is achieved by choosing a distributed drive over a central drive topology (−1.0%); followed by using a two-speed gearbox over a single speed (−0.6%); and lastly, by using two electric machines over using one for the central drive topologies (−0.3%).
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Keywords
electric vehicles, Optimization, powertrains, topology design
Subject
Suggested Citation
Verbruggen FJR, Silvas E, Hofman T. Electric Powertrain Topology Analysis and Design for Heavy-Duty Trucks. (2023). LAPSE:2023.23048
Author Affiliations
Verbruggen FJR: Department of Mechanical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands [ORCID]
Silvas E: Department of Mechanical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
Hofman T: Department of Mechanical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands [ORCID]
Silvas E: Department of Mechanical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
Hofman T: Department of Mechanical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands [ORCID]
Journal Name
Energies
Volume
13
Issue
10
Article Number
E2434
Year
2020
Publication Date
2020-05-12
ISSN
1996-1073
Version Comments
Original Submission
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PII: en13102434, Publication Type: Journal Article
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LAPSE:2023.23048
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https://doi.org/10.3390/en13102434
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