Indirect predictive torque control for switched reluctance motor in EV application

Abstract

This paper presents a novel indirect predictive control method to reduce the torque ripple of switched reluctance motor drive in electric vehicle (EV) application. The proposed indirect predictive torque control algorithm includes two parts: torque inverse model and robust predictive current controller. The torque inverse model adopts the form of adding torque error compensator to the simple linear model to realize the accurate mapping from torque to current, which avoids the complex calculation of the traditional torque inverse model. Then the predictive current controller is designed to traverse all candidate switching states and use the switching state of minimizing the cost function as the optimal output. Further, the modeling error, parameter variations and sampling error are equivalent to a total disturbance, which are compensated by the developed disturbance observer to improve the robustness of the predictive control. The proposed predictive control scheme indirectly realizes the instantaneous torque control through the accurate tracking of current, which is easy to implement, and is suitable for driving electric vehicles. Simulation experiments are performed to verify the effectiveness of the proposed predictive control algorithm.

Keywords

• Switched reluctance motor
• Predictive control
• Torque sharing function
• Torque inverse model
• Error compensator