Modified switching control of SRM drives for electric vehicles application with torque ripple reduction

The switched reluctance motor (SRM) offers extensive prospects, particularly within the realm of electric vehicles (EVs). Its robust construction, wide speed range, high torque density, and efficiency provide significant advantages that surpass other motors. Nonetheless, controlling these motors is more intricate when compared to conventional DC brushed or AC motors. This complexity arises due to the non-linear inductance characteristics of SRMs, resulting in undesirable effects like torque ripple, vibrations, and noise. Using the conventional full-bridge inverter, three switching approaches are highlighted for various operating modes of an EV. This aims to reduce costs and the number of switching pulses, leading to a more compact system, elimination of dead time, and switching losses. The MATLAB/Simulink platform was utilized to examine the operational effectiveness of a three-phase 6/4 poles SRM drive. Additionally, this paper focuses on mitigating torque ripple concerns by employing an adaptive neuro-fuzzy inference system (ANFIS) controller that has better efficiency and superior responses compared to conventional controllers such as fuzzy logic control (FLC) and proportional integral (PI) control. The results of the simulation encourage the practical implementation of the system, which is the next step in the author’s research.