Design and optimization of two-stage controller for three-phase multi-converter/multi-machine electric vehicle

Abstract

Electric vehicles (EVs) cut greenhouse gas emissions and our use of non-renewable resources, making them more attractive. EVs have lower fuel and maintenance expenses than internal combustion engine automobiles. This study proposes a multi-converter/Multi‒Machine system with two induction motors (IM) that drive a pure EV’s rear wheels. EV two-stage controllers using a simple Adaline neural network (NN) regulate Field-Oriented regulate of a three-phase IM. To control IM speed, the first controller level is a hybrid proportional–integral (PI) with a robust integral sign of error (RISE) controller. Injection torque is controlled by PI‒adaline NN in the second controller step. The simple Adaline NN improves two-stage controller performance. The Multi-Verse Optimization algorithm found the ideal RISE parameter to improve EV drive system performance. A plug-in EV’s linear speed is controlled by the Electronic Differential Controller (EDC). It uses the driver’s reference speed and steering angle to set each driving wheel’s reference speed. EDC adjusts wheel speeds to enhance traction and stability during cornering, accelerating, and decelerating. Utilizing this information, the EDC can effectively distribute power and torque to the wheels, thereby enhancing vehicle handling and overall performance. Three distinct road scenarios and the designated driving route topology have been used to act and demonstrate the resistive forces that affected the EV while it was traveling down the road. By using Matlab (Simulink), EV’s roadworthiness and efficiency will be evaluated.

Keywords

• induction motor
• d–q transformation
• edc
• electric vehicle
• mvo algorithm
• pi controller
• svpwm converter