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

TL;DR

This paper introduces a novel combinatorial semi-bandit framework for electric vehicle route planning that learns charging station performance, optimizing long-distance navigation in large road networks using Bayesian methods.

Contribution

It develops a new Bayesian semi-bandit approach with a novel gamma distribution prior for modeling stochastic charging station parameters in EV navigation.

Findings

Effective learning of charging station performance in large networks

Improved route planning accuracy with Bayesian methods

Demonstrated scalability in country-sized road networks

Abstract

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 pre-processing for the road network graph to handle the constrained combinatorial optimization problem in an efficient way. Then, for the pre-processed 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 country-sized road networks, with simulation experiments in Norway, Sweden and Finland.