Particle swarm optimization in the field control of a novel electric vehicle design based on a linear induction motor

Abstract

This work aims to improve the performance of electric vehicles (EVs) based on linear induction mo- tors (LIM). The Particle Swarm Optimization (PSO) method is proposed to tune the PID regulator of the Field-Oriented Control (FOC) technique. The main objective of this study is to develop innovative solutions that maximize the efficiency and precision of electric vehicles on various paths. The LIM model is imple- mented using the d-q synchronous reference frame and takes into account the end-effect phenomenon. This phenomenon occurs due to the termination of the mo- tor’s physical structure, which leads to distortion in the magnetic field at the ends of the motor’s primary (sta- tor). It is also highly nonlinear, which increases its complexity and makes control difficult. To overcome this issue, the Field-Oriented Control (FOC) technique is suggested to achieve better efficiency, dynamic per- formance, and greater control flexibility of the motor. Furthermore, the use of the (PSO) optimization tech- nique enables the determination of optimal control pa- rameters to maximize the performance of the (FOC- LIM) system under different operating conditions, such as speed variation and disturbance load. A compari- son between the PSO-PID and conventional methods in terms of response stability, steady-state error, and rise time is conducted using MATLAB/Simulink. The results demonstrate a more efficient, precise, and high- performing electric vehicle system.