Development of an electric vehicle charging station placement model.

Abstract

The widespread adoption of electric vehicles (EVs) required a comprehensive and accessible charging infrastructure to address range anxiety and accelerate EV adoption. This project was initiated to develop a novel electric vehicle charging station placement model using particle swarm optimization (PSO). The model incorporated key factors influencing EV demand, including traffic patterns, population density, charging station accessibility, and EV charging technology advancements. PSO, inspired by the social behavior of birds, efficiently explored the search space to identify optimal locations for charging station deployment, minimizing travel distances and maximizing accessibility for EV users. Data collection for the model utilized a multi-pronged approach, employing questionnaires, interviews with EV users and industry experts, and traffic pattern analysis. Historical EV sales data and future EV adoption projections were also integrated to ensure the model's adaptability to evolving demand. The developed model was validated through a case study in The Greater Kampala, evaluating its effectiveness in optimizing charging station placement and reducing travel distances for EV users. This project aimed to provide a robust and adaptable framework for sustainable EV infrastructure development, contributing to a cleaner and more sustainable transportation future.