A network analysis approach to optimal electrical vehicle charging in smart cities

Abstract

The primary objective of this project is to develop a web-based application that enables electric vehicle (EV) users to easily pinpoint the most effective charging stations, thereby alleviating any concerns related to range anxiety. This is achieved through the creation of a prediction algorithm that takes into account a variety of factors including road networks, population density, traffic patterns, and existing charging infrastructures, such as swapping stations. This algorithm has been seamlessly integrated with a well-known navigation system to ensure a smooth transition from locating an optimal charging station to navigating to it. The project methodology entails comprehensive research into electric mobility and charging stations, as well as the collection of diverse data types, including GIS data and traffic patterns. In terms of development, the front-end framework Angular has been chosen to create an engaging and interactive web application. Furthermore, machine learning techniques were applied in the development of the prediction algorithm. The ultimate aim of the project is to provide EV drivers with crucial information about the optimal charging stations along their routes and the most efficient means of reaching them. This is intended to address the common concern of range anxiety among electric vehicle drivers.