Towards Optimized Schedules for Charging Electric Vehicles on Austrian Highways using Genetic Algorithms

Abstract

Efficient charging of electric vehicles (EVs) on highways is important for ensuring e-mobility because of the comparably limited range of EVs and the potentially very long charging times during longer trips. For reducing the carbon footprint of EVs, matching demand with availability of renewable energy matters, i.e., the latter should be used when available. Hence, we opt for dynamic ‘anytime’ optimization of the allocation of EVs to charging sites preferably at time slots where renewable energy is predicted to be available, while taking into account charging properties of batteries as well. This paper outlines a genetic algorithm approach for this optimization task, which takes these objectives into account as well as charging station availability and the number of yet unscheduled EVs. Our algorithm integrates with Eclipse SUMO (Simulation of Urban MObility) for simulating the real-world environment. The proposed algorithm operates on a real highway network (the one in Austria) and offers efficient and sustainable solutions for reducing the environmental impact of EVs.