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Torque Characteristic Analyses and Multi-Objective Optimisation of Multi-Layer Flux-Barrier Less-Rare-Earth Permanent Magnet Synchronous Machine

  • Weinan Wang
  • , Weize Gong
  • , Liangkuan Zhu
  • , Jian Wei
  • , Mingqiao Wang
  • , Yong Liu*
  • , Ping Zheng*
  • *Corresponding author for this work
  • College of Computer and Control Engineering, Northeast Forestry University
  • College of Mechanical and Electrical Engineering, Northeast Forestry University
  • School of Electrical Engineering and Automation, Harbin Institute of Technology

Research output: Contribution to journalArticlepeer-review

Abstract

This paper investigates the torque characteristic and multi-objective optimisation (MOO) of the multi-layer flux-barrier less-rare-earth permanent magnet synchronous machine (MLFB-LRE-PMSM) used for the electric vehicles (EVs). This study explores the variation of torque characteristics with current angle under different winding current conditions, and thoroughly analyses the influence of permanent magnet (PM) structure parameters on reluctance torque, PM torque and the proportion of reluctance torque in the electromagnetic torque. On this basis, the sensitivity analysis method based on Sobol sequence and joint Sobol index is adopted, which not only simplifies the traditional analysis process, but also effectively considers the interaction effect between the optimisation objectives. Finally, a cooperative optimisation strategy of improved whale optimisation algorithm (WOA) and genetic algorithm (GA) is proposed, which is successfully applied to the MOO design of MLFB-LRE-PMSM. The results show that the improved WOA algorithm shows excellent optimisation performance and can be used as a new solution in the field of motor optimisation. At the same time, the engineering practicability of the proposed collaborative optimisation scheme is verified by finite element simulation.

Original languageEnglish
Article numbere70045
JournalIET Electric Power Applications
Volume19
Issue number1
DOIs
StatePublished - 1 Jan 2025
Externally publishedYes

Keywords

  • optimisation
  • permanent magnet machines
  • sensitivity analysis

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