Evolutionary Multiobjective Optimization of a System-Level Motor Drive Design

Benjamin Cheong, Paolo Giangrande, Xiaochen Zhang, Michael Galea, Pericle Zanchetta, Patrick Wheeler

Research output: Journal PublicationArticlepeer-review

4 Citations (Scopus)


The use of optimization algorithms to design motor drive components is increasingly common. To account for component interactions, complex system-level models with many input parameters and constraints are needed, along with advanced optimization techniques. This article explores the system-level optimization of a motor drive design, using advanced evolutionary multiobjective optimization (EMO) algorithms. Practical aspects of their application to a motor drive design optimization are discussed, considering various modelling, search space definition, performance space mapping, and constraints handling techniques. Further, for illustration purposes, a motor drive design optimization case study is performed, and visualization plots for the design variables and constrained performances are proposed to aid analysis of the optimization results. With the increasing availability and capability of modern computing, this article shows the significant advantages of optimization-based designs with EMO algorithms as compared to traditional design approaches, in terms of flexibility and engineering time.

Original languageEnglish
Article number9166700
Pages (from-to)6904-6913
Number of pages10
JournalIEEE Transactions on Industry Applications
Issue number6
Publication statusPublished - 1 Nov 2020


  • AC-AC power conversion
  • motor drives
  • optimization methods
  • permanent magnet machines
  • system analysis and design

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


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