Electric Drivetrain Optimization for a Commercial Fleet with Different Degrees of Electrical Machine Commonality

Meng Lu, Gabriel Domingues-Olavarría, Francisco J. Márquez-Fernández, Pontus Fyhr and Mats Alaküla
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Meng Lu: Division of Industrial Electrical Engineering and Automation, Lund University, SE-22100 Lund, Sweden
Gabriel Domingues-Olavarría: Division of Industrial Electrical Engineering and Automation, Lund University, SE-22100 Lund, Sweden
Francisco J. Márquez-Fernández: Division of Industrial Electrical Engineering and Automation, Lund University, SE-22100 Lund, Sweden
Pontus Fyhr: Division of Industrial Electrical Engineering and Automation, Lund University, SE-22100 Lund, Sweden
Mats Alaküla: Division of Industrial Electrical Engineering and Automation, Lund University, SE-22100 Lund, Sweden

Energies, 2021, vol. 14, issue 11, 1-15

Abstract: At present, the prevalence of electric vehicles is increasing continuously. In particular, there are promising applications for commercial vehicles transferring from conventional to full electric, due to lower operating costs and stricter emission regulations. Thus, cost analysis from the fleet perspective becomes important. The study of cost competitiveness of different drivetrain designs is necessary to evaluate the fleet cost variance for different degrees of electrical machine commonality. This paper presents a methodology to find a preliminary powertrain design that minimizes the Total Cost of Ownership (TCO) for an entire fleet of electric commercial vehicles while fulfilling the performance requirements of each vehicle type. This methodology is based on scalable electric machine models, and particle swarm is used as the main optimization algorithm. The results show that the total cost penalty incurred when sharing the same electrical machine is small, therefore, there is a cost saving potential in higher degrees of electrical machine commonality.

Keywords: fleet optimization; electric commercial vehicles; total cost of ownership; electrical machine scaling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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