Model Predictive Speed Control of a Permanent Magnet Synchronous Motor for Electric Vehicle (case study on Hyundai ATOS)

Abstract

Currently, electric vehicles (EVs) are a popular and rapidly growing solution to reduce emissions from our transportation sector. With this inspiration, this thesis presented an approach to develop model predictive speed control of a permanent magnet synchronous motor (PMSM) for the application of EV. According to the specification of the Hyundai ATOS vehicle which analyzed the different forces imposed to the vehicle, the power required to overcome these forces and the torque required to propel the vehicle. Then, due to the power and torque requirements the motor is selected. The control method model predictive control (MPC) is designed for the outer loop speed control and proportional integral (PI) for the inner loop current control based on the developed dynamic model of the PMSM drive system by considering input output constraints. The outer loop is responsible for regulating the speed of the motor while the inner loop focuses on maintaining the current or torque. To ensure the effectiveness of the proposed method, MPC is compared with linear quadratic regulator (LQR) and genetic algorithm based proportional integral (GA-PI) control approaches. And GA technique is used to tune the parameters of the PI gain and the weighting matrix of the cost function. As a simulation result the proposed MPC obtained better performance and effective than LQR and GA-PI. And also, MPC has no overshoot, better steady state performance and anti-load disturbance capability which shows the effectiveness of the proposed strategy.