Performance Evaluation of Electric Vehicles Using PSO and AHA Based Optimal Control Methodology

In this paper an optimal control methodology for electric vehicles using Artificial Hummingbird Algorithm (AHA) 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 travel, 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 (PSO) 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. By simultaneously optimizing control of three different road situations, the AHA decreases the tracking delay of conventional PSO based proportional-integral-controller by an order of 31.6 %. At the same time, AHA provides a 4 % extended driving range and a 4.8 % enhanced total energy efficiency, in comparison to particle swarm optimization (PSO).