A device for providing charging information for a charging process is configured to determine a total set of N data tuples for N different times of a charging time period for the charging process. A data tuple includes values of one or more characteristic variables relating to electrical energy that can be provided in the charging process. Furthermore, the device is configured to reduce the total set of N data tuples to a reduced set of M data tuples, with M<N, and to provide the reduced set of M data tuples for the determination of a charging plan for the charging process.

A device for providing charging information for a charging process is configured to determine a total set of N data tuples for N different times of a charging time period for the charging process. A data tuple includes values of one or more characteristic variables relating to electrical energy that can be provided in the charging process. Furthermore, the device is configured to reduce the total set of N data tuples to a reduced set of M data tuples, with M<N, and to provide the reduced set of M data tuples for the determination of a charging plan for the charging process.

Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.

Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.

Techniques are described herein for fleet electrification management. A method includes determining a composition of electric vehicles (EVs) to replace at least a portion of non-electric vehicles in a vehicle fleet while satisfying travel requirements of the vehicle fleet. The method includes estimating an energy demand of the composition of EVs. The method includes determining an electric vehicle supply equipment (EVSE) charging infrastructure to meet the estimated energy demand. The method includes providing one or more recommendations including at least one of: a fleet electrification recommendation for transitioning the vehicle fleet into the composition of EVs, or a charging infrastructure recommendation for implementing the EVSE charging infrastructure.

Techniques are described herein for fleet electrification management. A method includes determining a composition of electric vehicles (EVs) to replace at least a portion of non-electric vehicles in a vehicle fleet while satisfying travel requirements of the vehicle fleet. The method includes estimating an energy demand of the composition of EVs. The method includes determining an electric vehicle supply equipment (EVSE) charging infrastructure to meet the estimated energy demand. The method includes providing one or more recommendations including at least one of: a fleet electrification recommendation for transitioning the vehicle fleet into the composition of EVs, or a charging infrastructure recommendation for implementing the EVSE charging infrastructure.

Systems and methods are provided for aggregating and assigning power capacity for charging electric vehicles and providing power to other loads. The systems include a DC bus system used to harmonize and combine power drawn from grid connections having different electrical characteristics such as different voltages or phase levels and from other devices such as energy storage systems and generators at the site. Using the systems and methods can help enable utility customer sites to providing electric vehicle charging, especially for multiple electric vehicles, where the sites would otherwise not have sufficient power to do so without significant and expensive service upgrades and modifications.

Systems and methods are provided for aggregating and assigning power capacity for charging electric vehicles and providing power to other loads. The systems include a DC bus system used to harmonize and combine power drawn from grid connections having different electrical characteristics such as different voltages or phase levels and from other devices such as energy storage systems and generators at the site. Using the systems and methods can help enable utility customer sites to providing electric vehicle charging, especially for multiple electric vehicles, where the sites would otherwise not have sufficient power to do so without significant and expensive service upgrades and modifications.

Systems and methods for dynamic charger reservations are provided. In one embodiment, a method includes identifying an initial reservation for an electric vehicle to receive a charge from a charging entity at a first place at a first time. The method also includes calculating an estimated time of arrival and an arrival state of charge of at a reservation time. The method further includes detecting a grid event that changes the first cost for the charging entity. The method yet further includes generating a revised reservation for the electric vehicle. The method includes generating an initial compensation offer based on the estimated time of arrival and the arrival state of charge. The method also includes providing the revised reservation and the initial compensation offer to a user. The method further includes updating the initial reservation to the revised reservation in response to receiving a confirmation from the user.

Systems and methods for dynamic charger reservations are provided. In one embodiment, a method includes identifying an initial reservation for an electric vehicle to receive a charge from a charging entity at a first place at a first time. The method also includes calculating an estimated time of arrival and an arrival state of charge of at a reservation time. The method further includes detecting a grid event that changes the first cost for the charging entity. The method yet further includes generating a revised reservation for the electric vehicle. The method includes generating an initial compensation offer based on the estimated time of arrival and the arrival state of charge. The method also includes providing the revised reservation and the initial compensation offer to a user. The method further includes updating the initial reservation to the revised reservation in response to receiving a confirmation from the user.