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Multi-Objective Optimal Design of SPMSM for Electric Compressor Using Analytical Method and NSGA-II Algorithm

Author

Listed:
  • Seong-Tae Jo

    (Department of Electrical Engineering, Chungnam National University, Daejeon 34134, Korea)

  • Woo-Hyeon Kim

    (Hyundai Elevator Co., Ltd., 128, Chungjusandan1-ro, Chungju-si 27329, Korea)

  • Young-Keun Lee

    (Department of Electrical Engineering, Chungnam National University, Daejeon 34134, Korea)

  • Yong-Joo Kim

    (Department of Bio-Systems and Mechanical Engineering, Chungnam National University, Daejeon 34134, Korea)

  • Jang-Young Choi

    (Department of Electrical Engineering, Chungnam National University, Daejeon 34134, Korea)

Abstract

In contrast to internal combustion engine vehicles, electric vehicles (EVs) obtain the power required for the compressor of air conditioning system from an electric source. Therefore, an optimal design for electric motor, the main component of an electric compressor, is essential for improving EV mileage. A multi-objective optimal design is required because the characteristics of the motor are in a trade-off relationship with each other. When the finite element method (FEM) is used, multi-objective optimal designs for the motor take a significant amount of time because of the diversity analyses required for the optimal-model search. To solve this problem, in this study, a multi-objective optimal design method of an SPMSM for an EVs air conditioner system compressor was proposed and applied using the NSGA-II and an analytical method. The validity of the proposed method was confirmed by comparing the characteristics of the optimal design model with those of the initially designed model.

Suggested Citation

  • Seong-Tae Jo & Woo-Hyeon Kim & Young-Keun Lee & Yong-Joo Kim & Jang-Young Choi, 2022. "Multi-Objective Optimal Design of SPMSM for Electric Compressor Using Analytical Method and NSGA-II Algorithm," Energies, MDPI, vol. 15(20), pages 1-11, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7510-:d:940050
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    Cited by:

    1. Mingyu Choi & Gilsu Choi & Gerd Bramerdorfer & Edmund Marth, 2022. "Systematic Development of a Multi-Objective Design Optimization Process Based on a Surrogate-Assisted Evolutionary Algorithm for Electric Machine Applications," Energies, MDPI, vol. 16(1), pages 1-19, December.

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