  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.
