Torque Ripple optimization of Switched Reluctance Motors in Electric Vehicles through Model Predictive Controller

This paper delves into the utilization of Switched Reluctance Motors (SRMs) in electric vehicle (EV) propulsion systems, emphasizing their inherent advantages such as a broad speed and torque range, robust construction, and cost-effectiveness. Despite these benefits, SRMs are known for their drawback - high torque ripple. To mitigate this issue, the proposed system introduces the implementation of Model Predictive Controllers (MPCs) as an effective means of reducing torque fluctuations. The MPCs are designed to forecast the future performance of the SRM, enabling dynamic optimization of control inputs to achieve minimal torque ripple. The system compares the actual motor torque with the reference torque and processes any resulting error in such a way that the cost function of Model predictive controller is minimized by regulating the current through the winding of the motor. The combination of SRMs and MPCs offers a comprehensive solution for EV applications, ensuring reliable and efficient performance while addressing the challenges associated with torque ripple. The findings highlight the significance of this integrated approach in enhancing the overall effectiveness and desirability of SRMs in the electric vehicle industry.