TY - JOUR

T1 - Electric drivetrain optimization for a commercial fleet with different degrees of electrical machine commonality

AU - Lu, Meng

AU - Domingues‐olavarría, Gabriel

AU - Márquez‐fernández, Francisco J.

AU - Fyhr, Pontus

AU - Alaküla, Mats

N1 - Funding Information:
Funding: This research was funded by Swedish Energy Agency, grant number 50213‐1.

PY - 2021/6/1

Y1 - 2021/6/1

N2 - At present, the prevalence of electric vehicles is increasing continuously. In particular, there are promising applications for commercial vehicles transfering 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.

AB - At present, the prevalence of electric vehicles is increasing continuously. In particular, there are promising applications for commercial vehicles transfering 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.

KW - Electric commercial vehicles

KW - Electrical machine scaling

KW - Fleet optimization

KW - Total cost of ownership

UR - http://www.scopus.com/inward/record.url?scp=85106878926&partnerID=8YFLogxK

U2 - 10.3390/en14112989

DO - 10.3390/en14112989

M3 - Article

AN - SCOPUS:85106878926

SN - 1996-1073

VL - 14

JO - Energies

JF - Energies

IS - 11

M1 - 2989

ER -