Interior permanent magnet synchronous motors (IPMSMs) have high power densities and speed control performance, and they are widely used in the industry. The problem of reducing the torque ripple of an IPMSM is one of the hot issues in the field of electrical machine design. In order to determine the optimal combination of the geometric parameters to reduce the torque ripple of an IPMSM, a range analysis was conducted on the data from the orthogonal experiments in this study, dividing the rotor geometric parameters into two categories (important and ordinary) based on their degree of impact on the torque ripples of the IPMSM. Thereafter, an optimization of the ordinary parameters was carried out based on the results of the range analysis, whereas the optimization of the important parameters was carried out through a method that combined a multi-island genetic algorithm (MIGA) and Radial Basis Function (RBF) neural networks. The torque ripple of the IPMSM was effectively reduced without materially affecting the output power. Finally, the results of this optimization process were verified using a finite element simulation. The optimization method used in this study divided the motor geometric parameters into two categories and applied a different method of optimization to each parameter type, so it was able to efficiently optimize multiple geometric parameters for the IPMSM.INDEX TERMS Optimization, permanent magnet machines, genetic algorithms, torque.

Surface-mounted permanent magnet synchronous motors (SMPMSM) with high power density and good speed regulation are widely used in industrial applications. In order to further improve its power density, this paper studied the relationship between the thickness of the stator yoke, the thickness of the rotor yoke, the relative magnet span of the motor and the motor power density using the finite element simulation method. On this basis, a response surface model between the three parameters and the power density of the motor was established. Based on this model and a differential evolution algorithm, the motor was optimized and the power density was improve; finally, the optimization results were verified using the finite element simulation method. In addition, the optimization results showed that, when other structure parameters remain unchanged, there is an optimal combination of parameters that can maximize the motor’s power density, including the thickness of the stator yoke, the thickness of the rotor yoke and relative magnet span of the motor.

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Permanent magnet motor has become the main research direction of high power density motor, and the heat dissipation capability of the motor has become an important factor restricting the increase of motor power density. High-power density motor generates higher temperature when it works, and high temperature may cause motor insulation burnout, even irreversible demagnetization of permanent magnets. Therefore, thermal analysis and optimal design of cooling structure are very important for high power density motors. In this paper, a 3-D model of thermal simulation of permanent magnet motor is established, and the temperature field is calculated by two methods. 1. The fluid-solid coupling method is used to calculate the global temperature field of the motor and the cooling structure. 2. The fluid-solid coupling method is used to calculate the convective heat transfer coefficient of the cooling structure, then the heat transfer model is used to calculate the temperature field of the motor body. These two methods are introduced and compared in this paper, and the second method is considered to be well suited for optimal design of motor cooling structures.

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