Battery state of charge control using machine learning
AssigneeRivian IP Holdings, LLC
InventorsPaul McLachlan, Weiyang Sun
Life of this patent
Estimating the term…
Abstract
Controlling a battery state of charge using machine learning is provided. A system of an electric vehicle identifies data including a state of charge of a battery of the electric vehicle, a state of health of the battery of the electric vehicle, and a drive mode of the electric vehicle. The system establishes, based on input of the data into a local model configured on the electric vehicle and trained with machine learning, a schedule to control charging of the battery of the electric vehicle. The system executes, responsive to a power source electrically coupled to the battery of the electric vehicle, the schedule to control an amount of current supplied from the power source to the battery of the electric vehicle.
Claims18
A system, comprising:
- a data processing system of an electric vehicle, the data processing system comprising one or more processors, coupled to memory, to:
- identify data comprising a state of charge of a battery of the electric vehicle, a state of health of the battery of the electric vehicle, and a drive mode of the electric vehicle;
- receive, from a server, a weight generated for a regional model that includes charging characteristics of electric vehicles across a region;
- generate, based on input of the data and the weight into a model trained with machine learning, a charge schedule to charge the battery of the electric vehicle, wherein the charge schedule controls charging of the battery so that an amount of current from a power source varies over time;
- facilitate, responsive to the power source electrically coupled to the battery of the electric vehicle, charging of the battery of the electric vehicle in accordance with the charge schedule;
- and
- prevent conditioning of the battery in accordance with the charge schedule.
A method, comprising:
- identifying, by a data processing system comprising one or more processors and memory, data comprising a state of charge of a battery of an electric vehicle, a state of health of the battery of the electric vehicle, and a drive mode of the electric vehicle;
- receiving, by the data processing system, from a server, a weight generated for a regional model that includes charging characteristics of electric vehicles across a region;
- generating, by the data processing system, based on input of the data and the weight into a model trained with machine learning, a charge schedule to charge the battery of the electric vehicle, wherein the charge schedule controls charging of the battery so that an amount of current from a power source varies over time;
- facilitating, by the data processing system, responsive to the power source electrically coupled to the battery of the electric vehicle, charging of the battery of the electric vehicle in accordance with the charge schedule;
- and
- preventing, by the data processing system, conditioning of the battery in accordance with the charge scheduled.
An electric vehicle, comprising:
- a battery;
- and
- one or more processors, in communication with the battery, to:
- identify data comprising a state of charge of the battery, a state of health of the battery, and a drive mode of the electric vehicle;
- receive, from a server, a weight generated for a regional model that includes charging characteristics of electric vehicles across a region;
- generate, based on input of the data and the weight into a model trained with machine learning, a charge schedule to charge the battery of the electric vehicle, wherein the charge schedule controls charging of the battery so that an amount of current from a power source varies over time;
- facilitate, responsive to the power source electrically coupled to the battery of the electric vehicle, charging of the battery of the electric vehicle in accordance with the charge schedule;
- and
- prevent conditioning of the battery in accordance with the charge schedule.
Dependencies are inferred from the claim text.