Model Predictive Current Control Improved Performance on SRM Drive for Electric Vehicles

The switched reluctance motor (SRM) is a prominent electrical machine for variable speed drive to minimize the overall cost of on-road traction motor applications. This machine has a few drawbacks: torque ripple and flux ripple. However, the classical Model Predictive Control (MPC) model using torque and flux control state variables is more complex to predict the accurate value and can minimize cost function error as a result of high torque ripple. On the other hand, the existing MPC gives high torque ripples due to the improper selection of switching states techniques of the converter, which can result in high torque ripples. Therefore, this paper proposed a Model Predictive Current Control (MPCC) using single current control variables without a weighting factor to mitigate torque ripple further. The cost function error can be minimized by selecting the converter's optimal switching state. The proposed strategy is verified and validated using MATLAB/Simulink. The detailed result discusses the response of torque, flux, and speed of SRM. The MPCC-operated SRM drive experimental results are shown to prove the effective minimization of torque ripples in the proposed MPCC.