Transfer learning‐based surrogate‐assisted design optimisation of a five‐phase magnet‐shaping PMSM

Abstract

Abstract Multi‐phase permanent‐magnet synchronous machines (MPMSMs) with high reliability due to sufficient fault‐tolerant capability have considerable potential for transportation electrification applications. Here, an efficient surrogate‐assisted design optimisation method is proposed based on analytical model transfer learning for torque characteristic optimisation of a five‐phase magnet‐shaping PMSM. By employing transfer learning of the source domain analytical model data and the target domain finite element analysis (FEA) data in surrogate model training, the proposed method can achieve both high accuracy and high efficiency from the merits of FEA‐ and analytical‐based optimisations, respectively. The studied machine with five‐phases and harmonic injected surface‐mounted PMs to enable harmonic injection for torque capability improvement is introduced and the analytical model is built based on the segmented PM and the complex conformal mapping methods. Besides, the optimal Latin hypercube design (LHD) and Taguchi methods are used to form the source and target domain datasets, respectively, so that data features can be efficiently captured over a wide range of optimisation variables. An optimal design is obtained by multi‐objective optimisation using the trained surrogate model, which is prototyped and measured to validate the proposed method.