Design and Modeling of Model Predictive speed control Permanent Magnet Synchronous Motor for Hybrid Electric Vehicle.

Abstract

Due to the fast depletion of fossil resources, pollution, and issues with the global environment caused by internal combustion vehicles, it must change to a renewable energy source. Hybrid electric vehicles (HEV) are alternative solutions. A permanent magnet synchronous motor is widely used for HEV applications due to its many advantages, including high efficiency, compactness, high power density, fast dynamics, and a high torque to inertia ratio. In field oriented control (FOC), the current in a dq rotating reference frame is controlled by proportional-integral (PI) controllers. This enables separate control of the machine's torque and magnetic field. To ensure high performance from the FOC, the proportional and integral gains Kp and Ki must be carefully tuned. Direct Torque Control (DTC), which directly controls the torque and flux with no need for the complex coordinate transformation, therefore has the merits of low reliability for parameters, fast dynamic torque response, and a simple structure. However, it suffers from certain disadvantages, such as high current and torque ripples and the difficulty of controlling torque at low speeds. Due to its notable benefits like fast dynamic response, the capacity for multi-variable control, and the versatility to include multiple constraints, FCS-MPC, a robust and promising control strategy for PMSM drives, has grown in popularity. Compared to direct torque control and field-oriented control, it has significantly fewer current ripples and torque ripples. In this regard, a finite control set model Predictive current control and a proportional integral speed controller for a PMSM motor are proposed, where the proportional integral parameters are tuned using a genetic algorithm. In order to achieve this, the vehicle dynamics and PMSM are modeled mathematically. Also, the system is simulated using the MATLAB software tool. The speed response of FCS-MPC shows that the result has better dynamic performance than the PI controller. FCS-MPC is better in overshoot, which is 0.3514%, and in rise time, which is 0.0101%, and in settling time, which is 0.0177 seconds, compared to PI's 0.0869 seconds.