Computing device and method for predicting battery temperature in an electric aircraft
AssigneeBeta Air, LLC
InventorSteven J. Foland
Life of this patent
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Abstract
In an aspect of the present disclosure is a computing device for predicting battery temperature in an electric aircraft, the computing device including a controller communicatively connected to the electric aircraft, the controller comprising: a battery model, the battery model configured to: receive a flight plan; instantiate a machine-learning model, wherein the machine-learning model is trained as a function of a training set correlating flight plan data to battery temperature labels.
Claims18
A computing device for predicting battery temperature in an electric aircraft, the computing device comprising:
- a controller communicatively connected to an electric aircraft, the controller comprising:
- a battery model, the battery model configured to:
- receive a flight plan;
- calculate an environmental metric using a wind datum;
- instantiate a machine-learning model, wherein the machine-learning model is trained as a function of a training set correlating flight plan data to battery temperature labels;
- generate a temperature output using the machine-learning model and the flight plan;
- and
- generate a revised temperature using the environmental metric, the machine-learning model and a revised flight plan;
- and
- wherein the controller is configured to:
- receive the temperature output from the battery model;
- compare the temperature output to a threshold temperature;
- and
- generate the revised flight plan as function of the temperature output exceeding a threshold temperature, wherein generating the revised flight plan comprises altering the trajectory and speed of the electric aircraft.
A method for predicting battery temperature in an electric aircraft, the method comprising:
- receiving, at a battery model in a controller communicatively connected to an electric aircraft, a flight plan;
- calculating, at the battery model, an environmental metric using a wind datum;
- instantiating, at the battery model, a machine-learning model, wherein the machine-learning model is trained as a function of a training set correlating flight plan data to battery temperature labels;
- generating, by the battery model, a temperature output using the environmental metric, the machine-learning model and the flight plan;
- receiving, by the controller, the temperature output from the battery model;
- comparing, by the controller, the temperature output to a threshold temperature;
- generating, by a controller, a revised flight plan as function of the temperature output exceeding a threshold temperature, wherein generating the revised flight plan comprises altering the trajectory and speed of the electric aircraft;
- generating, by the battery model, a revised temperature using the machine-learning model and the revised flight plan.
Dependencies are inferred from the claim text.