Sequential Subspace Optimization Design of a Dual Three-Phase Permanent Magnet Synchronous Hub Motor Based on NSGA III

Abstract

The multiobjective optimization design of dual three-phase permanent magnet synchronous hub motors (PMSHMs) is challenging due to the high dimension and huge computation cost of finite-element analysis (FEA). A new multiobjective optimization strategy is proposed for dual three-phase PMSHMs in this article. All design parameters are divided into two subspaces according to the Pearson sensitivity analysis results to improve optimization efficiency. A new training method is adopted to improve the accuracy of the approximate model. By improving a multiobjective intelligent optimization algorithm, nondominated sorting genetic algorithm (NSGA) III, a new algorithm is proposed, which will greatly shorten optimization time. It is found that the proposed optimization method can significantly improve the performance, such as smaller torque ripple and higher maximum torque for the investigated PMSHM, while the computation resources are reduced. A prototype based on the optimization results is manufactured, and experiments are conducted on the platform to verify the accuracy of the optimization results and the FEA. The effectiveness of optimization and the accuracy of the simulation are verified by the experimental results.