Optimization of Torque Ripples in an Interior Permanent Magnet Synchronous Motor Based on the Orthogonal Experimental Method and MIGA and RBF Neural Networks

Abstract

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.

Optimal Design of PMa-SynRM for Electric Vehicles Exploiting Adaptive-Sampling Kriging Algorithm

“…For EV propulsion motor, the IPMSM has been widely used, as it has superior performance at the high-speed operation and has advantages of high torque and power density [28][29][30]. However, due to rising price of the rare earth magnets, the PMa-SynRM, in which ferrite magnets are inserted inside rotor core, seems to be good substitute, as it has high efficiency, low cost, and wide range of operating points [31].…”

Optimal Design of IPMSM for Fuel Cell Electric Vehicles Using Autotuning Elliptical Niching Genetic Algorithm

“…Therefore, the cogging torque is selected as the objective function, and the torque ripple and average torque are also considered. The cogging torque and torque ripple can be reduced by adjusting the topology of the rotor [17], [18]. This determines the selection of the angles and lengths of the magnets as design variables in Figure 10.…”

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

“…Many studies have aimed to reduce the computation time necessary for the design process by constructing surrogate models that can be used to accurately predict the motor characteristics for structural optimization while minimizing or eliminating the need for FEA. These papers report various methods for building surrogate models, such as the response surface method (4) (5) , radial basis function networks (6) , convolutional neural networks (7) (8) , and transfer learning (9) . However, these studies assumed only a fixedpoint driving condition and did not examine the characteristics of the wide operation ranges required for traction motors in automotive applications, which are the subject of this study.…”