Design of Optimal PID Controller for Electric Vehicle Based on Particle Swarm and Multi-Verse Optimization algorithms

Abstract

The automotive industry is moving toward more environmentally friendly automobiles with greater range and performance than traditional vehicles as the effects of global warming worsen. Because of the positive impact, electric vehicles can have on reducing harmful emissions from the transportation sector, scientists have grown increasingly interested in the possibility of analyzing and simulating electric vehicles. In this study, we develop a non-linear dynamic concept of an electric vehicle by fusing kinetic and electrical components. Then we create a proportional Integral derivative (PID) controller to help it stay on course. To obtain optimal parameters for this controller by minimizing the error between the desired and actual output, Particle Swarm Optimization (PSO), and Multi-Verse Optimization (MVO) algorithm are used. The proposed controllers tested with linear and nonlinear trajectories to represent the electric vehicle's speed. The computation findings show that the proposed controller works perfectly, keeping up with the electric vehicle's speed quickly and precisely. In particular, the MVO-based proportional-integral-derivative (PID) controller is superior to the proportional-integral-derivative (PID) -based PSO method in terms of no steady-state error and smallest overshoot (0.05% with MVO while 0.25% with PSO) prevention for electric vehicle (EV) speed despite the better results of settling time and rising time obtained in PSO(0.767 And 0.211 s) respectively while these values were (0.807 and 0.215 s), respectively, in MVO. All works are performed in MATLAB (R2020a) /Simulink environment.