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

Lu, Meng LU ; Domingues‐olavarría, Gabriel LU ; Márquez‐fernández, Francisco J. LU orcid ; Fyhr, Pontus LU and Alaküla, Mats LU (2021) In Energies 14(11).
Abstract

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... (More)

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.

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Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Electric commercial vehicles, Electrical machine scaling, Fleet optimization, Total cost of ownership
in
Energies
volume
14
issue
11
article number
2989
publisher
MDPI AG
external identifiers
  • scopus:85106878926
ISSN
1996-1073
DOI
10.3390/en14112989
language
English
LU publication?
yes
additional info
Funding Information: Funding: This research was funded by Swedish Energy Agency, grant number 50213‐1.
id
c2c763c6-a43b-41ce-8769-70e91fad071c
date added to LUP
2021-06-11 09:54:28
date last changed
2022-04-27 02:20:05
@article{c2c763c6-a43b-41ce-8769-70e91fad071c,
  abstract     = {{<p>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.</p>}},
  author       = {{Lu, Meng and Domingues‐olavarría, Gabriel and Márquez‐fernández, Francisco J. and Fyhr, Pontus and Alaküla, Mats}},
  issn         = {{1996-1073}},
  keywords     = {{Electric commercial vehicles; Electrical machine scaling; Fleet optimization; Total cost of ownership}},
  language     = {{eng}},
  month        = {{06}},
  number       = {{11}},
  publisher    = {{MDPI AG}},
  series       = {{Energies}},
  title        = {{Electric drivetrain optimization for a commercial fleet with different degrees of electrical machine commonality}},
  url          = {{http://dx.doi.org/10.3390/en14112989}},
  doi          = {{10.3390/en14112989}},
  volume       = {{14}},
  year         = {{2021}},
}