With the increase in adoption of Electric Vehicles (EVs), proper utilization of the charging infrastructure is an emerging challenge for service providers. Overstaying of an EV after a charging event is a key contributor to low utilization. Since overstaying is easily detectable by monitoring the power drawn from the charger, managing this problem primarily involves designing an appropriate "penalty" during the overstaying period. Higher penalties do discourage overstaying; however, due to uncertainty in parking duration, less people would find such penalties acceptable, leading to decreased utilization (and revenue). To analyze this central trade-off, we develop a novel framework that integrates models for realistic user behavior into queueing dynamics to locate the optimal penalty from the points of view of utilization and revenue, for different values of the external charging demand. Next, when the model parameters are unknown, we show how an online learning algorithm, such as UCB, can be adapted to learn the optimal penalty. Our experimental validation, based on charging data from London, shows that an appropriate penalty can increase both utilization and revenue while significantly reducing overstaying.
An expert system manages a power grid wherein charging stations are connected to the power grid, with electric vehicles connected to the charging stations, whereby the expert system selectively backfills power from connected electric vehicles to the power grid through a grid tie inverter (if present) within the charging stations. In more traditional usage, the expert system allows for electric vehicle charging, coupled with user preferences as to charge time, charge cost, and charging station capabilities, without exceeding the power grid capacity at any point. A robust yet accurate state of charge (SOC) calculation method is also presented, whereby initially an open circuit voltage (OCV) based on sampled battery voltages and currents is calculated, and then the SOC is obtained based on a mapping between a previously measured reference OCV (ROCV) and SOC. The OCV-SOC calculation method accommodates likely any battery type with any current profile. A parking locator system providing variably priced parking includes one or more parking sensors and one or more parking kiosks. The parking sensors may generate parking information identifying one or more occupied parking spaces and one or more unoccupied parking spaces within a vicinity. Promotion information including one or more discounts to parking fees may be received at the parking kiosks. The parking kiosks may display the discounted parking fees and receive payment of the same. The discounts to parking fees may expire at particular times. The size of the discounts may be set based on traffic congestion, which may be determined based on the number of occupied parking spaces within the vicinity. On-street parking, just as any publicly owned utility, is used inefficiently if access is free or priced very far from market rates. This paper introduces a novel demand management solution: using data from dedicated occupancy sensors an iteration scheme updates parking rates to better match demand. The new rates encourage parkers to avoid peak hours and peak locations and reduce congestion and underuse. The solution is deliberately simple so that it is easy to understand, easily seen to be fair and leads to parking policies that are easy to remember and act upon. We study the convergence properties of the iteration scheme and prove that it converges to a reasonable distribution for a very large class of models. The algorithm is in use to change parking rates in over 6000 spaces in downtown Los Angeles since June 2012 as part of the LA Express Park project. Initial results are encouraging with a reduction of congestion and underuse, while in more locations rates were decreased than increased. A computer implemented method, apparatus, and computer program product for automatically managing incentives associated with an electric vehicle charging transaction is provided. Incentives are received from a set of sources to form received incentives, by an incentive service. Applicable incentives are selected from the received incentives based on an identification of an electric vehicle, a charging station, and a set of principals associated with the electric vehicle charging transaction, by the incentive service. A set of selected incentives is identified from the received incentives for utilization in the electric vehicle charging transaction. The set of selected incentives is sent to an energy transaction planner, wherein the energy transaction planner incorporates the set of selected incentives into an energy transaction plan that is used to control the electric vehicle charging transaction. In this paper, the problem of grid-to-vehicle energy exchange between a smart grid and plug-in electric vehicle groups (PEVGs) is studied using a noncooperative Stackelberg game. In this game, on the one hand, the smart grid, which acts as a leader, needs to decide on its price so as to optimize its revenue while ensuring the PEVGs' participation. On the other hand, the PEVGs, which act as followers, need to decide on their charging strategies so as to optimize a tradeoff between the benefit from battery charging and the associated cost. Using variational inequalities, it is shown that the proposed game possesses a socially optimal Stackelberg equilibrium in which the grid optimizes its price while the PEVGs choose their equilibrium strategies. A distributed algorithm that enables the PEVGs and the smart grid to reach this equilibrium is proposed and assessed by extensive simulations. Further, the model is extended to a time-varying case that can incorporate and handle slowly varying environments. Some example embodiments include a method for recharging a number of battery electric vehicles. The method include receiving (by a control module configured to control an electrical grid system that include a number of recharging stations that are configured to recharge the number of battery electric vehicles and from the number of battery electric vehicles) usage data that comprises a current charge level, a current location, and a planned itinerary that includes a destination. The method includes determining anticipated electrical loads in the number of sectors of the electrical grid system based on the usage data of the number of battery electric vehicles. The method also includes redistributing the electrical supply on the electrical grid system to at least one recharging station of the number of recharging stations based on the anticipated electrical loads, prior to actual usage defined by the usage data by the number of battery electrical vehicles.