We study an online reservation system that allows electric vehicles (EVs) to park and charge at parking facilities equipped with electric vehicle supply equipment (EVSEs). We consider the case where EVs arrive in an online fashion and the facility coordinator must immediately make an admission or rejection decision as well as assign a specific irrevocable parking spot to each admitted EV. By means of strategic user admittance and smart charging, the objective of the facility coordinator is to maximize total user utility minus the operational costs of the facilities. We discuss an online pricing mechanism based on primal-dual methods for combinatorial auctions that functions as both an admission controller and a distributor of the facilities' limited charging resources. We analyze the online pricing mechanism's performance compared to the optimal offline solution and provide numerical results that validate the mechanism's performance for various test cases. NOTATIONN Set of arriving EVs indexed by n L Set of charging facilities indexed by M l Set of EVSEs at facility indexed by m T Set of time intervals indexed by t = 1, . . . , T Θ Set of all possible user types O n Set of schedule options that satisfy user n M l Number of EVSEs at facility C l Number of cables per EVSE at facility E l EVSE max energy output at facility s l (t) Available solar at facility at time t S lMax solar generation at facility π l (t)Grid energy price per unit at facility at time t G l (t) Max energy from grid at facility at time t θ n Arrival n's user type t − n Arrival n's reservation start time t + n Arrival n's reservation end time h n Arrival n's energy request { n } User n's preferred facilities {v n } User n's valuations for each facility c ml no (t) Cable reservation for user n in option o at EVSE m at facility at time t e ml no (t) Charge reservation for user n in option o at EVSE m at facility at time t x ml no Binary assignment variable for user n for option o at EVSE m at facility p ml no Payment from user n for option o at EVSE m at facility y ml c (t) Cables allocated at EVSE m at facility at time t y ml e (t) Energy allocated at EVSE m at facility at time t y l g (t)Total energy needed at facility at time t f l g (·)Facility 's electricity procurement cost function u n User n's utility from the EVSE reservation system p ml c (t) Cable price at EVSE m at facility at time t p ml e (t) Charging price at EVSE m at facility at time t p l g (t)Energy procurement price at facility at time t f * (·)

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In this paper, we study an online charge scheduling strategy for fleets of autonomous-mobility-on-demand electric vechicles (AMoD EVs). We consider the case where vehicles complete trips and then enter a between-ride state throughout the day, with their information becoming available to the fleet operator in an online fashion. In the between-ride state, the vehicles must be scheduled for charging and then routed to their next passenger pick-up locations. Additionally, due to the unknown daily sequences of ride requests, the problem cannot be solved by any offline approach. As such, we study an online welfare maximization heuristic based on primal-dual methods that allocates limited fleet charging resources and rebalances the vehicles while avoiding congestion at charging facilities and pick-up locations. We discuss a competitive ratio result comparing the performance of our online solution to the clairvoyant offline solution and provide numerical results highlighting the performance of our heuristic.

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In this paper, we study the potential benefits from smart charging for a fleet of electric vehicles (EVs) providing autonomous mobility-on-demand (AMoD) services. We first consider a profit-maximizing platform operator who makes decisions for routing, charging, rebalancing, and pricing for rides based on a network flow model. Clearly, each of these decisions directly influence the fleet's smart charging potential; however, it is not possible to directly characterize the effects of various system parameters on smart charging under a classical network flow model. As such, we propose a modeling variation that allows us to decouple the charging and routing problems faced by the operator. This variation allows us to provide closedform mathematical expressions relating the charging costs to the maximum battery capacity of the vehicles as well as the fleet operational costs. We show that investing in larger battery capacities and operating more vehicles for rebalancing reduces the charging costs, while increasing the fleet operational costs. Hence, we study the trade-off the operator faces, analyze the minimum cost fleet charging strategy, and provide numerical results illustrating the smart charging benefits to the operator.