Performance analysis of a battery-operated electric vehicle using metaheuristic optimization

Abstract

Battery-powered electric vehicles (BEVs) are gaining significant attention due to high energy efficiency, zero emissions, and advanced control systems. This article illustrates the performance analysis of BEV, which consists of a high-voltage battery pack, a BEV controller, a motor driver, a gearbox, and a longitudinal driver. A conventional PID controller is used as a BEV controller. Several optimal algorithms are employed for tuning the PID controller, including the Ziegler-Nichols method (ZN method), particle swarm optimization (PSO), genetic algorithm (GA), grey wolf optimization (GWO), artificial bee colony algorithm (ABC), and artificial hummingbird algorithm (AHA). The proposed research framework was assessed in terms of vehicle efficiency, battery power consumption, vehicle mileage, motor speed, and battery state of charge (SOC). Metaheuristic algorithms with PID controllers outperform conventional PID and classical ZN-PID controllers. Among all algorithms, the PID-GWO controller achieves maximum vehicle mileage, low battery power consumption, improved battery SOC, and the highest vehicle efficiency.