Design Optimization of PMSM for Electric Vehicles Based on an Intelligent Surrogate Model Selection Method

Honglin Wang, Xiaokai Chen, Xiaoyu Wang, Zhengyu Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

To optimize the performance of permanent magnet synchronous motor (PMSM), a multidisciplinary design optimization (MDO) method is proposed which considers both motor structure and controller design. Surrogate models are constructs to save computing resources and accelerate the optimization process. In order to solve the problem that the selection of surrogate model depends heavily on the experience of engineers, an intelligent surrogate model selection method (ISMSM) is proposed to obtain a proper surrogate model. Based on ISMSM and the MDO method, the PMSM design parameters are optimized, and the results show a significant improvement in overall performance.

Original languageEnglish
Title of host publicationProceedings of 2024 IEEE 7th International Electrical and Energy Conference, CIEEC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2133-2137
Number of pages5
ISBN (Electronic)9798350359558
DOIs
Publication statusPublished - 2024
Event7th IEEE International Electrical and Energy Conference, CIEEC 2024 - Harbin, China
Duration: 10 May 202412 May 2024

Publication series

NameProceedings of 2024 IEEE 7th International Electrical and Energy Conference, CIEEC 2024

Conference

Conference7th IEEE International Electrical and Energy Conference, CIEEC 2024
Country/TerritoryChina
CityHarbin
Period10/05/2412/05/24

Keywords

  • electric vehicles
  • intelligent surrogate model selection method
  • multidisciplinary design optimization
  • PMSM

Fingerprint

Dive into the research topics of 'Design Optimization of PMSM for Electric Vehicles Based on an Intelligent Surrogate Model Selection Method'. Together they form a unique fingerprint.

Cite this