Multi-objective optimization design of flat-wire permanent magnet synchronous motor based on IWOA-KELM

Aiming at the problem of large computational volume and low efficiency of motor finite element optimization. In this paper, a multi-objective optimization design method for flatwire permanent magnet synchronous motor combining IWOA-KELM agent model and genetic algorithm is proposed to optimize the performance of the motor. Firstly, according to the actual engineering experience, the motor structural parameters that have a greater impact on the optimization objective are selected as the optimization variables, secondly, the sample data are obtained through optimal Latin hypercubic sampling and finite element simulation, and three kinds of agent models of the optimization objective are established for the comparison of the fitting accuracy, and then the IWOA-KELM agent model that has the best fitting accuracy is selected for the multi-objective optimization with NSGA-II algorithm. Finally, the optimized results are compared with the simulation calculation results to verify the effectiveness and accuracy of the method in the field of motor optimization and design.