Performance Evaluation of Electric Vehicles Using AHA Based Optimal Control Methodology

Abstract

In this paper an optimal control methodology for electric vehicles using Artificial Hummingbird Algorithm is proposed. The main objective is to improve the performance of EV in terms of different critical parameters to meet the increasing demand for efficient and intelligent control systems in automotive industry. The proposed control strategy uses an AHA tuned Proportional-Integral controller to optimize the controller parameters for the best performance. The performance indicators such as vehicle speed, drive cycle, distance traveled, overall vehicle efficiency, State of Charge, and torque are evaluated on a test case in MATLAB/Simulink environment. To validate the proposed approach, its performance is benchmarked with a Particle Swarm Optimization algorithm. Results show that the AHA tuned PI controller performs better than the PSO algorithm. The AHA based strategy shows better efficiency, better SOC management, and better responsiveness in acceleration and torque delivery than PSO based control strategy. The results of this study indicate that the Artificial Hummingbird Algorithm could be a very powerful tool to optimize EV control systems to make electric vehicles more efficient, reliable, and high performance.