Smart Charging Schedules for Highway Travel With Electric Vehicles

Abstract

Electric vehicles (EVs) can contribute to reducing carbon emissions and facilitate renewable integration. However, EVs are not competitive with fuel-based vehicles, particularly for long distances, because of their limited range and long charging times. We propose a smart scheduling approach for EVs to plan charging stops on a highway with limited charging infrastructure. This approach aims to minimize the total travel time for each EV based on the A* algorithm with constraint verification and a peer-to-peer scheduling system. By considering the estimated state of the charging stations, we achieve indirect coordination between EVs. We introduce a simulation framework with trips generated using a data-driven approach and support for time-varying highway parameters. Furthermore, we apply our approach to a use case for the German highway A9 from Munich to Berlin. The computation and communication requirements of the proposed solution remain moderate and privacy preserving, contributing to its applicability. Results show that the smart scheduling approach significantly reduces the total travel times. In addition, by dynamically adjusting the schedules, the proposed approach can account for changing highway conditions, for example, slow traffic on a given segment. Our approach can be generalized beyond fast charging to different technologies, such as hydrogen or battery swapping stations.