Development of Synthetic Cyber Physical Framework and Computational Intelligence Algorithm for Electric Vehicle Charging Schedules

Abstract

The increasing adoption of Electric Vehicles (EVs) presents opportunities and challenges for sustainable transportation and energy use. This study identifies core challenges such as limited charging infrastructure, high operational costs, and grid stability concerns that hinder widespread EV integration. Addressing these barriers requires strategic infrastructure planning and intelligent charging coordination to integrate EVs seamlessly into urban and suburban landscapes. To tackle these challenges, this research introduces a Synthetic Cyber-Physical Framework that optimizes EV charging station planning, scheduling, and integration. The framework leverages computational intelligence algorithms to minimize costs, reduce travel time, and enhance grid and travel efficiency. newlineThe initial focus of the study is on EV infrastructure planning to ensure adequate and strategically located charging stations. This planning phase reduces congestion, minimizes travel times, and limits queuing delays. Using the Chaotic Harris Hawk Optimization (CHHO) algorithm within a Vehicular Ad-hoc Network (VANET), the research demonstrates significant improvements in reducing travel and recharging delays. The CHHO algorithm, enhanced with chaotic maps to avoid local optima, optimizes route planning dynamically and minimizes wait times, improving service quality across networked charging stations. Simulations with 100 EVs and multiple charging stations show that the CHHO model optimizes resource allocation effectively, reducing total travel time and enhancing user experience newline