ELECTRIC VEHICLE CHARGING CONTROL DEVICE
    1.
    发明公开

    公开(公告)号:US20240359585A1

    公开(公告)日:2024-10-31

    申请号:US18309772

    申请日:2023-04-28

    CPC classification number: B60L53/65 B60L53/62 B60L53/665

    Abstract: An electric vehicle charging control device includes an electronic dynamic charging schedule generator, a non-transitory computer readable medium and an electronic communicator. The non-transitory computer readable medium stores vehicle profile data and building profile data. The electronic communicator is configured to receive profile updates to the vehicle profile data from an electronic user interface. The electronic controller is programmed to generate a driver charging profile based on the vehicle profile data and the building profile data. The electronic controller is programmed to update the driver charging profile based on the electric vehicle user's behavior. The electronic controller is programmed to generate a charging schedule for the electric vehicle based on the updated driver charging profile. The electronic controller is programmed to controlling the charging port in accordance with the charging schedule upon the electric vehicle user confirming acceptance of the charging schedule on the electronic user interface.

    FORECASTING ENERGY CONSUMPTION IN A MIXED-VEHICLE FLEET

    公开(公告)号:US20250030766A1

    公开(公告)日:2025-01-23

    申请号:US18708438

    申请日:2022-11-18

    Abstract: A system for forecasting energy consumption by vehicles in a mixed-vehicle fleet. The system includes neural network(s) that generate a predictive function for vehicles in each of a number of classes (e.g., electric vehicles, hybrid vehicles, internal combustion vehicles, vehicle models, model years, etc.). To capture both the generalizable patterns that govern energy consumption across all vehicle classes and the features and relationships that are specific to each class, the neural network(s) include a multi-task learning model that includes shared layers for all of the classes of vehicles and a set of vehicle-specific layers for each class. In some of those embodiments, for example to predict to energy consumption of an additional class with limited data, the neural networks further include an inductive transfer learning model that includes the shared layers transferred from the multi-task learning model and vehicle-specific layers for the additional class.

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