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

Range and charge anxiety remain essential barriers to a faster electric vehicle market diffusion. To this end, quickly and reliably finding suitable charging stations may foster an electric vehicle uptake by mitigating drivers' anxieties. Here, existing commercial services help drivers to find available stations based on real-time availability data but struggle with data inaccuracy, e.g., due to conventional vehicles blocking the access to public charging stations. In this context, recent works have studied stochastic search methods to account for availability uncertainty in order to minimize a driver's detour until reaching an available charging station. So far, both practical and theoretical approaches ignore driver coordination enabled by charging requests centralization or sharing of data, e.g., sharing observations of charging stations' availability or visit intentions between drivers.Against this background, we study coordinated stochastic search algorithms, which help to reduce station visit conflicts and improve the drivers' charging experience. We model a multi-agent stochastic charging station search problem as a finite-horizon Markov decision process and introduce an online solution framework applicable to static and dynamic policies. In contrast to static policies, dynamic policies account for information updates during policy planning and execution. We present a hierarchical implementation of a single-agent heuristic for decentralized decision making and a rollout algorithm for centralized decision making. Extensive numerical studies show that compared to an uncoordinated setting, a decentralized setting with visit-intentions sharing decreases the system cost by 26%, which is nearly as good as the 28% cost decrease achieved in a centralized setting, and saves up to 23% of a driver's search time while increasing her search reliability.