Sliding Model-Based Predictive Torque Control of Induction Motor for Electric Vehicle

Abstract

This article presents a sliding model-based predictive torque control (S-PTC) of an induction motor for the electric vehicle application. In this work, a battery-operated bidirectional converter fed induction machine-driven power train system is considered. In the conventional predictive torque control (C-PTC), only the machine model is used for the prediction of stator flux, current, and electromagnetic torque. Unlike the C-PTC, the presented control scheme offers a nonlinear switching feedback term in the prediction equation to overcome the model uncertainties, disturbances, and variation in motor parameters. The gains of the feedback terms are selected based on the Lyapunov stability criterion. Adaptive full order observer is used for the estimation of speed and rotor flux. This method is implemented and the speed response and torque ripple of the motor are analyzed for various operating modes of the vehicle. The robustness of the control is verified for variation in motor parameters and a comparison is shown to justify the achievement of objectives through S-PTC than with C-PTC. The control algorithm for the developed system is verified experimentally through the laboratory prototype.