Multi-objective optimization of a switched reluctance motor for light electric traction applications

This paper proposes a solution for the optimization of a full-bridge three-phase inverter-fed switched reluctance motor (SRM) simultaneously accounting for the geometry of the motor as well as for its switching strategy. The optimization method proposed for this is Particle Swarm Optimization (PSO), allowing a multi-objective optimization. By means of the Permeance Network Analysis (PNA), magnetic phenomena and control scheme are both accurately modeled. The Pareto-front provides the compromise between the two objective functions imposed by the application, i.e. the average torque and torque ripple.