Abstract: An approach is provided for dynamically controlling power distribution amongst a plurality of charging station ports based on one or more objectives. A method includes obtaining input data, wherein the input data includes at least one of: user data associated with one or more current charging station users, or non-user data that is not associated with the current charging station users. The method includes processing the input data through one or more machine learning engines, wherein the one or more machine learning engines are trained to determine a particular power distribution, among the plurality of charging station ports, that achieves one or more objectives. The method includes configuring the plurality of charging station ports according to the particular power distribution. The particular power distribution specifies a maximum charging rate or a percentage of a power budget for each of the plurality of charging station ports.

Abstract: An approach is provided for estimating the optimal number and mixture of types of electric vehicle (EV) charging stations (EVCS) at one or more points of interest (POIs). A method includes generating, based on an EV adoption model, an EV adoption prediction. The method includes generating, based on a mobility simulation model, a driver-type prediction that predicts percentages of EV drivers qualifying for various EV driver types. The method includes generating, based on the EV adoption prediction, the driver-type prediction, and a visitation model, a visitation prediction that predicts how many EV drivers of each type of EV driver will visit the POI. The method includes determining and displaying, based on how many EV drivers of each type of EV driver is predicted to visit the POI, for each type of EV charging station of a plurality of types of EV charging stations, an optimal number of EVCS to install.

Abstract: An approach is provided for estimating electric vehicle (EV) charging station demand at specifics points of interest. A method includes determining a percentage of visitors to a point of interest (POI) that are electric vehicle (EV) drivers, a first percentage of the EV drivers qualifying as essential drivers, a second percentage of the EV drivers qualifying as opportunistic drivers. The method includes feeding input data to an inference engine, wherein the input data includes the above percentages, a number of visitors to the POI, and charge rates for the opportunistic and essential drivers. The inference engine generates output data regarding predicted EV charging demand for the POI, and generates a display based on the output data.

Abstract: An approach is provided for estimating electric vehicle (EV) charge needs among a population in a particular region. A method includes obtaining region-specific information associated with the particular region; wherein the region-specific information includes a number of EV drivers in the particular region. The method includes generating needs prediction data that indicates the EV charge needs among the population by applying the region-specific information to a mobility simulation of electrical vehicle drivers in the particular region; wherein the mobility simulation comprises a plurality of probability distribution functions. The method includes generating, based on the needs prediction data, on a display device of a computing device, a display that suggests a plurality of locations at which to place EV charging stations within the particular region to satisfy the estimated EV charge needs indicated in the needs prediction data.

Abstract: An electric vehicle charging network server establishes electric vehicle charging reservation groups. The electric vehicle charging network server is coupled with multiple electric vehicle charging stations that each include one or more charging ports and are owned by a charging station host. The server receives input from the charging station host to configure an electric vehicle charging reservation group. The server configures the electric vehicle charging reservation group according to the received input. The configured electric vehicle charging reservation group includes multiple charging ports and allows a limited number of electric vehicle charging group reservations that are each applicable to all of the charging ports that are part of the configured electric vehicle charging reservation group.