A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles

Journal article, 2023

In this work, we address the problem of long-distance navigation for battery electric vehicles (BEVs), where one or more charging sessions are required to reach the intended destination. We consider the availability and performance of the charging stations to be unknown and stochastic, and develop a combinatorial semi-bandit framework for exploring the road network to learn the parameters of the queue time and charging power distributions. Within this framework, we first outline a method for transforming the road network graph into a graph of feasible paths between charging stations to handle the constrained combinatorial optimization problem in an efficient way. Then, for the feasibility graph, we use a Bayesian approach to model the stochastic edge weights, utilizing conjugate priors for the one-parameter exponential and two-parameter gamma distributions, the latter of which is novel to multi-armed bandit literature. Finally, we apply combinatorial versions of Thompson Sampling, BayesUCB and Epsilon-greedy to the problem. We demonstrate the performance of our framework on long-distance navigation problem instances in large-scale country-sized road networks, with simulation experiments in Norway, Sweden and Finland.