Multiobjective Optimization Design of a Switched Reluctance Motor for Low-Speed Electric Vehicles With a Taguchi-CSO Algorithm

This paper proposes a novel multiobjective optimization design method for a switched reluctance motor (SRM) on low-speed electric vehicles (EVs). According to the indexes of a low-speed EVs propulsion system and the large torque ripple of the SRM, six objectives of geometric parameters optimization of the SRM are given, which are maximum speed, acceleration time (including in situ acceleration time and overtake acceleration time), maximum climbing gradient, energy usage ratio, and torque ripple factor. The rated parameters of the driving motor are given based on the basic parameters of the low-speed EVs. Based on the engineering design method, the dimension range of the SRM under the rated parameter range is confirmed. The dynamic simulation model of a low-speed pure EVs propulsion system is built in MATLAB/Simulink based on the finite element model of the SRM and the vehicle balance equation. Then, a multiobjective optimization design of the geometric parameters of the SRM is carried out by a Taguchi-chicken swarm optimization algorithm. The correctness of the finite element model is verified, and the accuracy of the multiobjective optimization is verified by the dynamic simulation results and the low-speed EV experiment.