Multi-objective optimization of dual-stator permanent magnet motor based on composite algorithm

Dual-stator permanent magnet motors (DSPMMs) have the advantages of fast control response, high torque density, and strong overload capacity, and thus are widely used in CNC machine tools, heavy mining machinery, oil drilling machinery, large industrial conveyor belts and lifting equipment and other fields. In this paper, taking a dual-stator permanent magnet motor as an example, the multi-variable combination scanning (MVCS) method is proposed to optimize the outside air-gap length and the slot width of the external unit motor to effectively weaken the cogging torque for the problem of large cogging torque. Furthermore, under temperature and stress constraints, in order to enhance the average torque and reduce the torque ripple, firstly, the magnetic bridge height, magnetically conductive layer thickness, inside air-gap length, and the top and bottom air barrier width of the internal unit motor are analyzed for sensitivity. Then, the Taguchi method optimizes the significant variables, the genetic algorithm based on the Kriging response surface model optimizes the non-significant variables, and finally, the optimal solution is selected on the Pareto front. The simulation results show that the performance targets of the motor are significantly improved after optimization, thereby verifying the effectiveness of the proposed methods.