Comparative Analysis of FOPID-Based Electric Vehicle Speed Control Using Heuristic Optimization Algorithms

In this study, the parameters of a Fractional Order PID (FOPID) controller used for the speed control of an electric vehicle (EV) were optimized using five different heuristic optimization algorithms: Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Harris Hawks Optimization (HHO), Particle Swarm Optimization (PSO), and Salp Swarm Algorithm (SSA). The control system was modeled in the MATLAB/Simulink environment, and the speed control performance was tested in a closed-loop configuration. In the optimization process, performance criteria such as percentage overshoot (%OS), settling time (t_s), rise time 〖(t〗_r), and mean squared error (MSE) were used. The results obtained with each algorithm were evaluated comparatively in terms of the specified performance criteria. The results revealed that the performance of different algorithms in FOPID parameter optimization varies depending on the application and performance criteria. The findings provide an important reference for the selection of appropriate algorithms to enhance speed control performance in electric vehicles.