Using Machine Learning to Reduce Design Time for Permanent Magnet Volume Minimization in IPMSMs for Automotive Applications

Abstract

Interior permanent magnet synchronous motors (IPMSMs) have been widely used as traction motors in electric vehicles. Finite element analysis is commonly used to design IPMSMs but is highly time-intensive. To shorten the design period for IPMSMs, various surrogate models have been constructed to predict relevant characteristics, and they have been used in the optimization of IPMSM geometry. However, to date, no surrogate models have been able to accurately predict the characteristics over the wide speed range required for automotive applications. Herein, we propose a method for accurately predicting the speed-torque characteristics of an IPMSM by using machine learning techniques. To improve the prediction accuracy, we set the motor parameters as the prediction target of the machine learning methods. We then used the trained surrogate model and a real-coded genetic algorithm to minimize the volume of the permanent magnet and showed that the design time can be significantly reduced compared with the case where only finite element analysis is used.