Torque quality improvement of switched reluctance motor using ant colony algorithm

The switched reluctance motors (SRMs) are gaining increasing interest in many industrial applications, including electric vehicles (EVs). However, their main drawback is the high torque ripple and noise. This paper presents an optimization-based method to improve the torque quality of SRM drives. The focus is on reducing torque ripple without complicating the control algorithm. The switching angles are optimized using a multistage ant colony algorithm (MSACA). The multistage algorithm provides a better search capability that fits appropriately with the high nonlinearities of SRMs. The finite element method (FEM) is employed to calculate the magnetic characteristics of the tested 8/6 SRM prototype. These characteristics are used within the MATLAB environment in the form of lookup tables to model the machine. The performance indices are calculated within the simulation model. Series of simulation results are included to show the effectiveness of the proposed control. Besides, experimental verification is also included to verify the theoretical findings.