In an EV charging facility, with multiple vehicles requesting charge simultaneously, scheduling becomes crucial to provide adequate service under vehicle sojourn time constraints. However, these departure times may not be known accurately, and typical policies such as Earliest-Deadline- First or Least-Laxity-First are affected by this uncertainty in information. In this paper, we analyze the performance of these policies under uncertain deadlines, using a meanfield approach. We characterize the deviation in individual attained service as a function of the uncertainty. Since incentives appear to under-report deadlines in order to be prioritized, we analyze a simple modification of the policies to enforce incentive compatibility. Simulation experiments are carried out with a practical data set.@en

We consider an EV charging infrastructure where scheduling algorithms determine the service priority, based on the departure deadline. The focus is on the Earliest Deadline First algorithm, and the impact on its performance stemming from uncertainty in the deadlines. Through numerical simulations we evaluate the resource allocation achieved by the algorithm in two versions, depending on whether or not vehicles are curtailed after their declared deadline expires. Results are compared with a theoretical analysis of the idealized EDF algorithm. Experiments with real-world traces are also included.@en