Driving without anxiety: A route planner service with range prediction for the electric vehicles

Abstract

In modern smart cities, mobility based on Electric Vehicles (EVs) is considered a key factor to reduce carbon emissions and pollution. However, despite the global interest and the investments worldwide, the user acceptance level is still low, mainly due to the lack of charging services support. This is one of the main causes for the so called “EV driver's anxiety”, and has led people to consider EV mobility suitable only for short routes. To contrast this issue, we propose here a route planner application supporting EV mobility also on medium and long routes, through prediction of range and charging stops. Our application estimates the minimal energy consumption path, by also considering the overhead to reach the charging stations along the way towards the destination. We demonstrate the optimality of the algorithm and we describe its implementation within a Web-application which connects to charging providers' services (to retrieve the locations of charging spots) and to Google services (for routing directions and real-time traffic data). Finally, we evaluate the scalability of our application, and we study its effectiveness in supporting EV routes on large-scale scenarios (e.g. the Emila-Romagna region in Italy) through immersive simulation techniques.