Shortest Feasible Paths with Charging Stops for Battery Electric Vehicles

TL;DR

This paper addresses optimizing electric vehicle routes by incorporating charging stops, proposing a practical algorithm that computes optimal and high-quality routes efficiently despite the NP-hard nature of the problem.

Contribution

It extends the Constrained Shortest Path problem to include realistic charging models and introduces CHArge, an algorithm capable of solving large-scale instances efficiently.

Findings

CHArge computes optimal routes on continental scale data.

Heuristic variants produce high-quality routes in under a second.

The approach effectively handles realistic charging station models.

Abstract

We study the problem of minimizing overall trip time for battery electric vehicles in road networks. As battery capacity is limited, stops at charging stations may be inevitable. Careful route planning is crucial, since charging stations are scarce and recharging is time-consuming. We extend the Constrained Shortest Path problem for electric vehicles with realistic models of charging stops, including varying charging power and battery swapping stations. While the resulting problem is NP-hard, we propose a combination of algorithmic techniques to achieve good performance in practice. Extensive experimental evaluation shows that our approach (CHArge) enables computation of optimal solutions on realistic inputs, even of continental scale. Finally, we investigate heuristic variants of CHArge that derive high-quality routes in well below a second on sensible instances.