On Enhancing Electric Vehicle Ecosystem in Urban Scenario

This paper addresses the critical issue of air pollution caused by fossil fuel-powered vehicles by proposing a comprehensive solution centered on Electric Vehicles (EVs) and advanced charging infrastructure. While EVs present a viable alternative, challenges such as inefficient charging infrastructure and scheduling algorithms hinder their widespread adoption. To tackle these issues, our study introduces a series of models aimed at optimizing EV scheduling at charging stations, reducing overall charging time, minimizing queuing, and boosting revenue for charging facilities. The first model takes a straightforward approach, directing EVs to the nearest charging station based solely on commute time. Building on this, the second model incorporates waiting time, reducing overall delays and improving charging efficiency. In the final model, historical data on waiting times is used to calculate average delays, enabling a more informed and proactive scheduling strategy. Through extensive simulations and data analysis, we assess the effectiveness of these algorithms. Our findings show substantial improvements in charging efficiency, shorter wait times, and better resource utilization at charging stations. By offering practical solutions to EV charging challenges, this research contributes to the development of sustainable transportation infrastructure, reduces the transportation sector’s reliance on non-renewable resources, and supports global efforts to combat air pollution.