Design Optimization of Switched Reluctance Machine Using Genetic Algorithm

This paper studies a design optimization procedure for switched reluctance motors (SRMs) using a Genetic Algorithm (GA). A multi-objective optimization method has been employed in the optimization of current commutation angles for priority operating points and over the entire operating range of the machine. Criteria of optimal control, which are maximizing output average torque and minimizing the root mean square value of net torque ripple, have been used in the optimization problem. A decision-making algorithm has been investigated to choose a solution from the optimal Pareto-front with finite optimal points. Five SRM design candidates have been selected and studied. The optimized motor performance at the priority operating points has been used to compare between different designs. Finally, a motor design that satisfies all design requirements has been characterized over its entire operating envelope based on turn-on and turn-off angles.