Electric Vehicle Charging Station Search in Stochastic Environments

Electric vehicles are a central component of future mobility systems as they promise to reduce local noxious and fine dust emissions, well CO 2 if fed by clean energy sources. However, the adoption electric so far fell short expectations despite significant governmental incentives. One reason for this slow is drivers’ perceived range anxiety, especially individually owned vehicles. Here, bad user experiences (e.g., conventional cars blocking charging stations or inconsistent real-time availability data) manifest anxiety. Against background, we study stochastic search algorithms that can be readily deployed in today’s navigation order minimize detours reach an available station. We model such finite-horizon Markov decision process present comprehensive framework considers different problem variants, speedup techniques, three solution algorithms: exact labeling algorithm, heuristic rollout algorithm. Extensive numerical studies show our significantly decrease expected time find free station while increasing solution-quality robustness likelihood successful compared with myopic approaches.