Nowadays, interior permanent magnet synchronous motors (IPMSMs) are widely utilized as traction motors. The permanent magnets used in IPMSMs are an important factor; thus, high-coercivity permanent magnets with lesser rare-earth elements are in development. This study investigated the performance of IPMSMs typically used in automotive applications modified to contain a strong magnet model (SMM). Rotor models with two permanent magnet arrangements, that is, a V-shaped single-layered permanent magnet structure (Type 1V) and a double-layered permanent magnet structure (Type 2D), were considered in this study. This paper discusses the characteristics of the analysis models based on the results of a two-dimensional finite element analysis. The maximum torques of Types 1V and 2D with the SMMs were approximately the same. In addition, the loss of Type 2D with the SMM was lower than that of Type 1V with the SMM at two evaluation points and under two driving schedules. Therefore, Type 2D was proved to be suitable for use with the SMM.

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.

The optimal design of interior permanent magnet synchronous motors requires a long time because finite element analysis (FEA) is performed repeatedly. To solve this problem, many researchers have used artificial intelligence to construct a prediction model that can replace FEA. However, because the training data are generated by FEA, it takes a very long time to obtain a sufficient amount of data, making it impossible to train a large-scale prediction model. Here, we propose a method for generating a large amount of data from a small number of FEA results using machine learning. An automatic design system with a deep generative model and a convolutional neural network is then constructed. With its sufficient data, the proposed system can handle three topologies and three motor parameters in a wide range of current vector regions. The proposed system was applied to multi-objective optimization design, with the optimization completed in 13-15 seconds.

Interior permanent magnet synchronous motors have become widely used as traction motors in environmentally friendly vehicles. Interior permanent magnet synchronous motors have a high degree of design freedom and time-consuming finite element analysis is required for their characteristics analysis, which results in a long design period. Here, we propose a method for fast efficiency maximization design that uses a machine-learning-based surrogate model. The surrogate model predicts motor parameters and iron loss with the same accuracy as that of finite element analysis but in a much shorter time. Furthermore, using the current and speed conditions in addition to geometry information as input to the surrogate model enables design optimization that considers motor control. The proposed method completed multi-objective multi-constraint optimization for multi-dimensional geometric parameters, which is prohibitively timeconsuming using finite element analysis, in a few hours. The proposed shapes reduced losses under a vehicle test cycle compared with the initial shape. The proposed method was applied to motors with three rotor topologies to verify its generality.INDEX TERMS finite element analysis, iron loss, machine learning, motor efficiency, multi-objective multi-constraint optimization, permanent magnet motors, XGBoost

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