Deep Transfer Learning-Based Sizing Method of Permanent Magnet Synchronous Motors Considering Axial Leakage Flux

Abstract

The sizing process is necessary to analyze the electromagnetic characteristics according to the major shape parameters during the early design stage of permanent magnet synchronous motors (PMSMs). However, predicting the performance of PMSMs with 2-D finite element analysis (FEA) has errors due to axial leakage flux. Therefore, the axial leakage flux should be considered in the sizing process. The most accurate way to consider the axial leakage flux is to perform 3-D FEA, but it has a disadvantage of high computational cost. In this view, we propose a deep transfer learning-based surrogate modeling method to reduce the computational cost for calculating 3-D FEA-based motor parameters. The transfer learning is conducted using a large amount of 2-D FEA-based and small amount of 3-D FEA-based motor parameters. Using the proposed process, it is possible to accurately predict the motor characteristics according to size-related variables that satisfy the required specifications with small amount of 3-D FEA-based motor parameters. The proposed method was verified through 3-D FEA and experiments for pancake-type PMSMs, which is highly affected by axial leakage flux.