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Optimization of Induction Machine Design for Electric Vehicle Powertrain

Lu, Meng LU ; Domingues-Olavarria, Gabriel ; Marquez-Fernandez, Francisco J. LU orcid ; Byden, Hannes LU and Alakula, Mats LU orcid (2023) 2023 IEEE International Electric Machines and Drives Conference, IEMDC 2023 In 2023 IEEE International Electric Machines and Drives Conference, IEMDC 2023
Abstract

This paper proposes a methodology to include induction machine (IM) in an optimization scheme for EV powertrains using Particle Swarm Optimization (PSO) as the optimization algorithm. The IM model is developed based on finite element analysis (FEA) and all the losses are estimated. The main objective is to generate a large database of base designs in a computationally efficient but accurate way. To perform the optimization, scaling methods are used. Detailed transmission and inverter models are also considered based on previous work to estimate the overall powertrain efficiency. The optimization case study shows how the optimizer evaluates a large number of alternatives and picks the optimal IM design and associated transmission and... (More)

This paper proposes a methodology to include induction machine (IM) in an optimization scheme for EV powertrains using Particle Swarm Optimization (PSO) as the optimization algorithm. The IM model is developed based on finite element analysis (FEA) and all the losses are estimated. The main objective is to generate a large database of base designs in a computationally efficient but accurate way. To perform the optimization, scaling methods are used. Detailed transmission and inverter models are also considered based on previous work to estimate the overall powertrain efficiency. The optimization case study shows how the optimizer evaluates a large number of alternatives and picks the optimal IM design and associated transmission and inverter. The proposed methodology can serve as a basis for future research on powertrain optimization for dual-motor driven vehicles.

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author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Induction machine, loss estimation, Powertrain optimization, PSO
host publication
2023 IEEE International Electric Machines and Drives Conference, IEMDC 2023
series title
2023 IEEE International Electric Machines and Drives Conference, IEMDC 2023
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2023 IEEE International Electric Machines and Drives Conference, IEMDC 2023
conference location
San Francisco, United States
conference dates
2023-05-15 - 2023-05-18
external identifiers
  • scopus:85172729906
ISBN
9798350398991
DOI
10.1109/IEMDC55163.2023.10238871
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2023 IEEE.
id
bddd7877-bb55-4cf3-89f8-02ab40081b96
date added to LUP
2024-01-08 14:45:52
date last changed
2024-02-09 10:44:29
@inproceedings{bddd7877-bb55-4cf3-89f8-02ab40081b96,
  abstract     = {{<p>This paper proposes a methodology to include induction machine (IM) in an optimization scheme for EV powertrains using Particle Swarm Optimization (PSO) as the optimization algorithm. The IM model is developed based on finite element analysis (FEA) and all the losses are estimated. The main objective is to generate a large database of base designs in a computationally efficient but accurate way. To perform the optimization, scaling methods are used. Detailed transmission and inverter models are also considered based on previous work to estimate the overall powertrain efficiency. The optimization case study shows how the optimizer evaluates a large number of alternatives and picks the optimal IM design and associated transmission and inverter. The proposed methodology can serve as a basis for future research on powertrain optimization for dual-motor driven vehicles.</p>}},
  author       = {{Lu, Meng and Domingues-Olavarria, Gabriel and Marquez-Fernandez, Francisco J. and Byden, Hannes and Alakula, Mats}},
  booktitle    = {{2023 IEEE International Electric Machines and Drives Conference, IEMDC 2023}},
  isbn         = {{9798350398991}},
  keywords     = {{Induction machine; loss estimation; Powertrain optimization; PSO}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{2023 IEEE International Electric Machines and Drives Conference, IEMDC 2023}},
  title        = {{Optimization of Induction Machine Design for Electric Vehicle Powertrain}},
  url          = {{http://dx.doi.org/10.1109/IEMDC55163.2023.10238871}},
  doi          = {{10.1109/IEMDC55163.2023.10238871}},
  year         = {{2023}},
}