A vehicle-grid integration management system determines use of a power grid by an electric vehicle in a dual multi-part rate structure including a grid account portion associated with a relationship between the electric vehicle and the power grid, a group account portion associated with a relationship between the vehicle group and the electric vehicle and/or the power grid, a consumption portion associated with a volume of electricity drawn from the power grid by the electric vehicle over a time period, a supply portion associated with a volume of electricity delivered to the power grid by the electric vehicle over the time period, a demand portion associated with an upper threshold of electricity drawn from the power grid by the electric vehicle over the time period, and a capacity portion associated with an upper threshold of electricity delivered to the power grid by the electric vehicle over the time period.

An autonomous base station for unmanned aerial vehicles (‘UAVs’) is disclosed, which includes a landing surface for a UAV, configured with at least one power transfer bus for supplying power to a power source of a UAV thereon. The base station further includes a networking module and data processing means operably connected to, and configured to control, the power transfer bus and the networking module. The data processing means is operably connected to the UAV through the networking module, and further configured to receive, store and process data from the UAV or another. The base station further includes a power supply operably connected to the or each power transfer bus, the or each networking module and the data processing means. A network of at least two such base stations is also disclosed, for sensing, modelling and monitoring an environment with UAVs.

A power supply system includes a power feeding system for transforming an AC medium-voltage power signal from a medium-voltage grid into a low-voltage power signal for feeding an electricity consumer site. The power supply system further includes a low-voltage multiphase converter for transforming a low-voltage signal into a low-voltage multiphase signal. The multiphase converter is arranged antiparallel to the power feeding system, and an LV/MV multiphase transformer for transforming the low-voltage multiphase signal into an output-signal that is conformant to the AC medium-voltage power signal.

A power supply system includes a power feeding system for transforming an AC medium-voltage power signal from a medium-voltage grid into a low-voltage power signal for feeding an electricity consumer site. The power supply system further includes a low-voltage multiphase converter for transforming a low-voltage signal into a low-voltage multiphase signal. The multiphase converter is arranged antiparallel to the power feeding system, and an LV/MV multiphase transformer for transforming the low-voltage multiphase signal into an output-signal that is conformant to the AC medium-voltage power signal.

Provided is a bootstrap method for registering a charging station (CS), which was in an offline state, to an electric vehicle charging station management system (CSMS) and operating same. The bootstrap method comprises the steps of: storing at least partial bootstrap information in a CS so as to configure bootstrap information; connecting the CS to a CSMS by setting a security channel between the CS and the CSMS for maintaining registration information about the CS; and registering the CS to the CSMS.

Provided is a bootstrap method for registering a charging station (CS), which was in an offline state, to an electric vehicle charging station management system (CSMS) and operating same. The bootstrap method comprises the steps of: storing at least partial bootstrap information in a CS so as to configure bootstrap information; connecting the CS to a CSMS by setting a security channel between the CS and the CSMS for maintaining registration information about the CS; and registering the CS to the CSMS.

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.

A remote computer server communicates with a fleet of electric vehicles, and gathers telemetry data from the fleet of electric vehicles. An intelligent EVSE unit and/or a DC fast charging unit communicates with the remote server, and charges an electric vehicle based at least in part on the telemetry data from the fleet of electric vehicles. The remote computer server can generate charging instructions based at least in part on the telemetry data gathered from the fleet of electric vehicles. The intelligent EVSE unit and/or the DC fast charging unit receive the charging instructions, and charge the electric vehicle based at least in part on the charging instructions, the telemetry data, and/or an existent electrical load associated with an electrical panel of a house or a building.

A remote computer server communicates with a fleet of electric vehicles, and gathers telemetry data from the fleet of electric vehicles. An intelligent EVSE unit and/or a DC fast charging unit communicates with the remote server, and charges an electric vehicle based at least in part on the telemetry data from the fleet of electric vehicles. The remote computer server can generate charging instructions based at least in part on the telemetry data gathered from the fleet of electric vehicles. The intelligent EVSE unit and/or the DC fast charging unit receive the charging instructions, and charge the electric vehicle based at least in part on the charging instructions, the telemetry data, and/or an existent electrical load associated with an electrical panel of a house or a building.