Dimension-Reduction Many-Objective Optimization Design of Multimode Double-Stator Permanent Magnet Motor

With the increasing complexity of modern transportation, the design requirements for electric vehicle (EV) motors are getting more demanding and diverse. It results in an increase in the number of optimization objectives and poses significant computational challenges in exploring the trade-offs between different optimization objectives. In this article, considering the relatively large dimensions of design parameters and optimization objectives, a dimension-reduction many-objective optimization design approach is proposed. And a multimode double-stator permanent magnet (DSPM) motor is proposed as an optimization design example. In the proposed many-objective optimization approach, on the one hand, in order to improve motor optimization efficiency, with the assistance of aggregation tree (AT) analysis and correlation analysis, reduced dimensions of optimization objectives and design parameters are realized simultaneously. On the other hand, design requirements of multimode operation for the DSPM motor are considered comprehensively, so improved output torque, reduced torque ripple, and wider speed range are obtained at the same time. Finally, the prototype motor and experimental platform are built. Corresponding experiments are carried out to prove the effectiveness of the proposed dimension-reduction many-objective optimization design approach and the validity of the multimode DSPM motor.