Evolutionary Multiobjective Optimization of a System-Level Motor Drive Design

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.