Hybrid neural network for determining at least one parameter of a charging plan for a vehicle
AssigneeStrong Force TP Portfolio 2022, LLC
InventorCharles Howard Cella
Life of this patent
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Abstract
A method of executing a charging plan for at least one vehicle in a transportation system includes: receiving, at a charging infrastructure control system, operational state and energy consumption information for a plurality of network-enabled vehicles; predicting a geolocation of one or more vehicles of the plurality of network-enabled vehicles in a geographic region; allocating the one or more vehicles of the plurality of network-enabled vehicles to charging infrastructure in the geographic region; and optimizing, at an artificial intelligence system, a parameter of the charging plan based on the prediction of the geolocation of the one or more vehicles in the geographic region.
Claims22
A method of executing a charging plan for at least one vehicle in a transportation system, the method comprising:
- receiving, at a charging infrastructure control system, operational state and energy consumption information for a plurality of network-enabled vehicles, wherein the operational state and the energy consumption information that is received at the charging infrastructure control system includes vehicle battery level and vehicle energy consumption rate;
- predicting a geolocation of one or more vehicles of the plurality of network-enabled vehicles in a geographic region;
- allocating the one or more vehicles of the plurality of network-enabled vehicles to a charging infrastructure in the geographic region;
- and
- optimizing in real-time, at an artificial intelligence system, a parameter of the charging plan based on the prediction of the geolocation of the one or more vehicles in the geographic region,
- wherein the optimizing the parameter of the charging plan comprises:
- predicting near-term charging needs for the one or more vehicles based on operational status;
- evaluating charging infrastructure availability and capacity information within a geofenced region;
- detecting accumulation of vehicles at a charging location within the geofenced region;
- and
- adjusting charging infrastructure control parameters locally to accommodate the detected accumulated vehicles queued for charging, wherein the adjusting is performed in real-time responsive to the detecting, and
- wherein the artificial intelligence system uses a machine learning algorithm to optimize the parameter of the charging plan.
A transportation system that implements a charging plan for a vehicle, the transportation system comprising:
- a charging infrastructure control system that receives operational state and energy consumption information for a plurality of network-enabled vehicles, wherein the operational state and the energy consumption information that is received at the charging infrastructure control system includes vehicle battery level and vehicle energy consumption rate;
- and
- an artificial intelligence system that is connected with the charging infrastructure control system and that, responsive to the operational state and the energy consumption information that is received, optimizes at least one charging plan parameter upon which the charging plan for at least a portion of the plurality of network-enabled vehicles is dependent,
- wherein the artificial intelligence system comprises:
- a first neural network that processes information including one or more vehicle routes and one or more stored energy states to predict one or more target energy renewal regions for at least the portion of the plurality of network-enabled vehicles;
- and
- a second neural network that processes vehicle energy renewal infrastructure usage and demand information within the one or more target energy renewal regions predicted by the first neural network, wherein the second neural network processes the vehicle energy renewal infrastructure usage and demand information to determine one or more charging infrastructure operational parameters, and
- wherein the artificial intelligence system uses a machine learning algorithm to optimize the at least one charging plan parameter in real-time based on one or more energy renewal regions and the determined one or more charging infrastructure operational parameters.
Dependencies are inferred from the claim text.