Torque Ripple Minimization for a Switch Reluctance Motor Using the Ant Lion Optimization Algorithm

Abstract

High torque density is a significant criterion for electric vehicle applications. In the context of torque performance, firstly, the average torque of the motor should be improved, and, secondly, the torque ripple of the machine must be at a satisfactory level. Switch reluctance motors (SRMs), as an alternative, pass the first criterion. However, the torque ripple of the SRMs can be problematic for the users. Various methods have been used for torque ripple minimization, and most of them are related to structural techniques. However, these methods cannot be considered as the optimal ones. In this article, a 3-phase switch reluctance motor with 18 stator poles and 12 rotor poles (18/12) has been introduced, designed to be used in electric vehicles. The torque ripple of the motor is optimized using the Ant Lion Optimization (ALO) algorithm, which is a metaheuristic approach, and the result of the optimization problem is compared with the initial machine. The mathematical basics of the algorithm and performance parameters, such as the convergence speed, are discussed and compared with other optimization algorithms to declare the advantages and disadvantages. Lastly, the electromagnetic analyses of the proposed motor are performed using 2-D finite-element analysis.