Systems and methods for determining vehicle battery health
First Claim
1. A method performed by one or more computing devices, comprising:
- receiving, by the one or more computing devices, information corresponding to internal resistances of a battery of a vehicle;
receiving, by the one or more computing devices, information corresponding to at least one other operating condition, in addition to the internal resistances, corresponding to the vehicle;
receiving, by the one or more computing devices, information corresponding to an identity of a driver of the vehicle, the information originating from a mobile user device associated with the driver;
determining, by the one or more computing devices and based on the identity, driving habits that are based on historic driving data associated with the driver;
receiving, by the one or more computing devices, a machine learning model that was trained, over time, based on internal resistances of a plurality of vehicles, other operating conditions of the plurality of vehicles, and driving habits of a plurality of drivers associated with the plurality of vehicles;
evaluating, by the one or more computing devices, the machine learning model based on the internal resistances of the battery of the vehicle, the at least one other operating condition, and the driving habits of the driver to produce a prediction regarding the remaining battery life;
presenting, by the one or more computing devices and via a display device associated with the vehicle, the prediction regarding the remaining battery life; and
refining, by the one or more computing devices, the machine learning model based on the internal resistances of the battery of the vehicle, the operating condition corresponding to the vehicle, and the driving habits of the driver of the vehicle, wherein the refined machine learning model is subsequently used in a prediction of remaining battery life of a battery of another vehicle.
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Abstract
Techniques described herein may be used to provide a driver of a vehicle with an accurate assessment of the remaining life of the vehicle battery. An on-board device may collect information from one or more sensors or devices within the vehicle. The information may be processed to generate a data set that accurately describes the current status and operating conditions of the battery. The data set may be used to evaluate the health of the battery and make predictions regarding the future performance of the battery, which may be communicated to the driver of the vehicle. Machine-learning techniques may be implemented to improve upon methodologies to evaluate the health of the battery and make predictions regarding battery performance.
28 Citations
20 Claims
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1. A method performed by one or more computing devices, comprising:
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receiving, by the one or more computing devices, information corresponding to internal resistances of a battery of a vehicle; receiving, by the one or more computing devices, information corresponding to at least one other operating condition, in addition to the internal resistances, corresponding to the vehicle; receiving, by the one or more computing devices, information corresponding to an identity of a driver of the vehicle, the information originating from a mobile user device associated with the driver; determining, by the one or more computing devices and based on the identity, driving habits that are based on historic driving data associated with the driver; receiving, by the one or more computing devices, a machine learning model that was trained, over time, based on internal resistances of a plurality of vehicles, other operating conditions of the plurality of vehicles, and driving habits of a plurality of drivers associated with the plurality of vehicles; evaluating, by the one or more computing devices, the machine learning model based on the internal resistances of the battery of the vehicle, the at least one other operating condition, and the driving habits of the driver to produce a prediction regarding the remaining battery life; presenting, by the one or more computing devices and via a display device associated with the vehicle, the prediction regarding the remaining battery life; and refining, by the one or more computing devices, the machine learning model based on the internal resistances of the battery of the vehicle, the operating condition corresponding to the vehicle, and the driving habits of the driver of the vehicle, wherein the refined machine learning model is subsequently used in a prediction of remaining battery life of a battery of another vehicle. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. One or more computing devices comprising:
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a non-transitory memory device storing a plurality of processor-executable instructions; and a processor configured to execute the processor-executable instructions, wherein executing the processor-executable instructions causes the processor to; receive information corresponding to internal resistances of a battery of a vehicle; receive information corresponding to at least one other operating condition, in addition to the internal resistances, corresponding to the vehicle; receive information corresponding to an identity of a driver of the vehicle, the information originating from a mobile user device associated with the driver; determine driving habits that are based on historic driving data associated with the driver; receive a machine learning model that was trained, over time, on internal resistances of a plurality of vehicles, other operating conditions of the plurality of vehicles, and driving habits of a plurality of drivers associated with the plurality of vehicles; evaluate the model based on the internal resistances of the battery of the vehicle and the at least one other operating condition, and the driving habits of the driver to produce a prediction regarding the remaining battery life; communicate the prediction, regarding the remaining battery life, to a component of the vehicle; and refine the machine learning model based on the internal resistances of the battery of the vehicle, the operating condition corresponding to the vehicle, and the driving habits of the driver of the vehicle, wherein the refined machine learning model is subsequently used in a prediction of remaining battery life of a battery of another vehicle. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. One or more computing devices, comprising:
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a non-transitory memory device storing a plurality of processor-executable instructions; and a processor configured to execute the processor-executable instructions, wherein executing the processor-executable instructions causes the processor to; receive measurements of internal resistances of batteries installed at a plurality of vehicles; receive measurements of other operating conditions, in addition to the internal resistances, relating to the plurality of vehicles; receive information corresponding to identities of a plurality of drivers for the plurality of vehicles, vehicle the information originating from mobile user devices of the drivers; determining driving habits based on historic driving data associated with the drivers; train models over time, using machine learning techniques and based on the measurements of the internal resistances of batteries installed at the plurality of vehicles, the measurements of the other operating conditions, and the driving habits of the plurality of drivers, to predict battery failure in the plurality of vehicles, wherein training the models over time includes modifying the models over time based on; the measurements of internal resistances of batteries installed at a plurality of vehicle, the other operating conditions relating to the plurality of vehicles, and the driving habits of the plurality of drivers; and transmit at least one trained model, of the trained models, to the plurality of vehicles, for prediction of battery failure at the plurality of vehicles. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification