English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this work, we address the problem of long-distance navigation for batteryelectric vehicles (BEVs), where one or more charging sessions are required toreach the intended destination. We consider the availability and performance ofthe charging stations to be unknown and stochastic, and develop a combinatorialsemi-bandit framework for exploring the road network to learn the parameters ofthe queue time and charging power distributions. Within this framework, wefirst outline a pre-processing for the road network graph to handle theconstrained combinatorial optimization problem in an efficient way. Then, forthe pre-processed graph, we use a Bayesian approach to model the stochasticedge weights, utilizing conjugate priors for the one-parameter exponential andtwo-parameter gamma distributions, the latter of which is novel to multi-armedbandit literature. Finally, we apply combinatorial versions of ThompsonSampling, BayesUCB and Epsilon-greedy to the problem. We demonstrate theperformance of our framework on long-distance navigation problem instances incountry-sized road networks, with simulation experiments in Norway, Sweden andFinland.

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