Disclosed is an apparatus for sensing a temperature of an electric vehicle charger during charging of an electric vehicle. The apparatus is configured to sense the temperature of a charging pins using a temperature sensor provided in a charging plug, to transmit the same to the charger by serial communication using the existing signal line without adding a separate signal line, and to be supplied with power necessary for ICs and devices in the charging plug using the existing signal line during charging of the electric vehicle.

Disclosed is an apparatus for sensing a temperature of an electric vehicle charger during charging of an electric vehicle. The apparatus is configured to sense the temperature of a charging pins using a temperature sensor provided in a charging plug, to transmit the same to the charger by serial communication using the existing signal line without adding a separate signal line, and to be supplied with power necessary for ICs and devices in the charging plug using the existing signal line during charging of the electric vehicle.

A gateway system for a building comprises: a first relay to couple a first line of the gateway system to a first grid line of a power grid; a second relay to couple a second line of the gateway system to a second grid line of the power grid; a neutral line coupled to a neutral grid line of the power grid; a first electric-vehicle (EV) line coupled to the first line of the gateway system, the first EV line configured for being coupled to a first line of an EV charging connector; a second EV line coupled to the second line of the gateway system, the second EV line configured for being coupled to a second line of the EV charging connector, wherein the EV charging connector has no neutral line; and a balancing converter coupled to the first and second lines of the gateway system.

A gateway system for a building comprises: a first relay to couple a first line of the gateway system to a first grid line of a power grid; a second relay to couple a second line of the gateway system to a second grid line of the power grid; a neutral line coupled to a neutral grid line of the power grid; a first electric-vehicle (EV) line coupled to the first line of the gateway system, the first EV line configured for being coupled to a first line of an EV charging connector; a second EV line coupled to the second line of the gateway system, the second EV line configured for being coupled to a second line of the EV charging connector, wherein the EV charging connector has no neutral line; and a balancing converter coupled to the first and second lines of the gateway system.

A method and device for controlling a charging station, and a charging station system. The charging station includes multiple charging positions each comprising a charging pile and a parking space corresponding to the charging pile. The method includes: obtaining in real time first information comprising usage status information and daily usage information of the charging piles and occupancy status information of the parking spaces, the usage status information is information about whether the charging piles being currently in use, and the daily usage information is information about a quantity of charging piles used in a predetermined time period of each day; and determining parking information according to the first information, the parking information comprises a quantity of first available parking spaces used for fuel vehicles and a quantity of second available parking spaces used for electric vehicles.

A riding lawn mower, including: a frame; a seat; a walking assembly and a walking motor; a cutting assembly and a driving motor configured to drive the cutting assembly; a first energy storage device and a second energy storage device configured to supply power to at least one of the walking motor or the driving motor; a driving circuit to transfer power from at least one of the energy storage devices to at least one of the motors; and a charging circuit to charge at least one of the energy storage devices. The riding lawn mower further includes a first identification terminal engageable with the second energy storage device and a second identification terminal engageable with the first energy storage device; the riding lawn mower identifies a type of the energy storage devices through the identification terminals and selectively connects them to the driving circuit and the charging circuit.

A riding lawn mower, including: a frame; a seat; a walking assembly and a walking motor; a cutting assembly and a driving motor configured to drive the cutting assembly; a first energy storage device and a second energy storage device configured to supply power to at least one of the walking motor or the driving motor; a driving circuit to transfer power from at least one of the energy storage devices to at least one of the motors; and a charging circuit to charge at least one of the energy storage devices. The riding lawn mower further includes a first identification terminal engageable with the second energy storage device and a second identification terminal engageable with the first energy storage device; the riding lawn mower identifies a type of the energy storage devices through the identification terminals and selectively connects them to the driving circuit and the charging circuit.

Embodiments generate machine learning predictions to discover target device energy usage. One or more trained machine learning models configured to discover target device energy usage from source location energy usage can be stored. Multiple instances of source location energy usage over a period of time can be received for a given source location. Using the trained machine learning model, multiple discovery predictions for the received instances of source location energy usage can be generated, the discovery predictions comprising a prediction about a presence of target device energy usage within the instances of source location energy usage. And based on the multiple discovery predictions, an overall prediction about a presence of target device energy usage within the given source location's energy usage over the period of time can be generated.

Embodiments generate machine learning predictions to discover target device energy usage. One or more trained machine learning models configured to discover target device energy usage from source location energy usage can be stored. Multiple instances of source location energy usage over a period of time can be received for a given source location. Using the trained machine learning model, multiple discovery predictions for the received instances of source location energy usage can be generated, the discovery predictions comprising a prediction about a presence of target device energy usage within the instances of source location energy usage. And based on the multiple discovery predictions, an overall prediction about a presence of target device energy usage within the given source location's energy usage over the period of time can be generated.

An information processing apparatus includes a control section. The control section predicts an amount of electricity demanded for charging of vehicles that a plurality of users use, respectively, and determines a time slot in which the amount of electricity demanded exceeds a threshold value, and when the control section determines that one user of the plurality of users will refrain from use of the relevant vehicle on the following day, makes charging of the one user's vehicle wait in the time slot and provides an incentive to the one user.