Multi-objective Optimization Design of High Power Density Embedded V-Type Permanent Magnet Synchronous Motor

In order to improve the motor torque as much as possible and reduce the motor torque ripple, the multi-objective optimization of the motor is carried out to achieve the best results. Because the traditional genetic algorithm is easy to cause local convergence, poor population diversity and slow convergence rate in the later period, this paper adopts genetic algorithm and simulated annealing (GSA) algorithm to optimize a 130KW high power density motor, which is a hairpin motor with built-in V-shaped rotor structure and parallel slot stator structure. In this paper, the stator and rotor structural parameters are taken as optimization variables, and the sensitivity of each variable to the output torque and torque ripple of the motor is analyzed. The Pareto plane is built and the optimization motor parameters are verified by simulation. The structure shows that the torque of the motor is increased by about 7% and the torque ripple is reduced to 54.4%.