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

Jassim, Arkan A.; Karam, Ekhlas H.; E. Ali, Mohammed Moanes

doi:10.30684/etj.2023.135587.1279

	Design of Optimal PID Controller for Electric Vehicle Based on Particle Swarm and Multi-Verse Optimization algorithms
Engineering and Technology Journal
Article 15, Volume 41, Issue 2, 2023, Pages 446-455 PDF (959.92 K)
Document Type: Research Paper
DOI: 10.30684/etj.2023.135587.1279
Authors
Arkan A. Jassim^* ¹; Ekhlas H. Karam²; Mohammed Moanes E. Ali³
¹Bilad Alrafidain University College ,Diyala, Iraq
²Department of Computer Engineering, Mustansiriyah University, Baghdad, Iraq
³Department of Electrical Engineering , University of Technology,Iraq
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.
Highlights
A nonlinear dynamic concept of an electric vehicle was developed by fusing kinetic and electrical components. A proportional Integral derivative (PID) controller to stay the vehicle on the course was created. Particle Swarm Optimization (PSO) and Multi-Verse Optimization (MVO) algorithms are used to obtain optimal parameters for this controller. The proposed controllers were tested with linear and nonlinear trajectories to represent the speed of electric vehicles.
Keywords
Electrical vehicle; PID controller; Optimization Methods; Particle Swarm Optimization (PSO); Multi-Verse Optimization (MVO)

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