Torque ripple reduction for switched reluctance motors using global optimization algorithm

Global optimization algorithms are widely used to effectively suppress the torque ripple in switched reluctance motors (SRMs). In this paper, an improved velocity-controllable particle swarm optimization algorithm (VCPSO) is proposed to optimize the turn-off angle of an SRM under the current chopping control (CCC) method. In addition, the performances of three global optimization algorithms are compared and analyzed. The specific steps are outlined as follows. First, the static non-linear inductance-current-position and torque-current-position curves of the SRM are obtained through finite-element calculations, and a non-linear model of the SRM is established on this basis. Second, the turn-off angle optimization method based on the VCPSO is proposed. Finally, the performances of the simulated annealing algorithm (SA), the genetic algorithm (GA), and the proposed algorithm are compared and analyzed in terms of torque ripple suppression. The obtained results show that the proposed VCPSO has the advantages of a low number of iterations, low torque ripple, and small peak current in the torque ripple reduction issue of the SRM.