Systems and methods for determining vehicle battery health

Patent metadata

Field	Value
Publication number	US-10197631-B2
Application number	US-201514727238-A
Country	US
Kind code	B2
Filing date	Jun 1, 2015
Priority date	Jun 1, 2015
Publication date	Feb 5, 2019
Grant date	Feb 5, 2019

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.

First claim

What is claimed is:

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.

2. The method of claim 1 , wherein the other operating condition includes at least one of:
a humidity measurement,
a temperature measurement,
a range of humidity levels over a period of time,
a range of temperatures over a period of time,
voltage readings with the vehicle off versus on,
a coolant temperature minimum,
a coolant temperature maximum,
a coolant temperature average,
a barometric pressure surrounding the battery.

3. The method of claim 1 , wherein the one or more computing devices include a telematics device installed in the vehicle.

4. The method of claim 1 , wherein the internal resistances of the battery are derived from an ohmic testing device electrically coupled to the battery.

5. The method of claim 1 , wherein evaluating the prediction regarding the remaining battery life comprises:
accessing historical operating conditions of the battery; and
predicting the remaining battery life of the battery based on the internal resistances of the battery, the at least one other operating condition, the driving habits of the driver, and the historical operating conditions of the battery.

6. The method of claim 1 , further comprising:
receiving feedback regarding the accuracy of the prediction;
further refining the machine learning model based on the feedback.

7. The method of claim 1 , further comprising:
determining, based on another model, at least one other prediction regarding a future performance of the battery;
providing, to a server device, feedback regarding the accuracy of the at least one other prediction; and
receiving an updated version of the other model in response to providing the feedback to the server device.

8. One or more computing devices comprising:
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.

9. The one or more devices of claim 8 , wherein the other operating condition includes at least one of:
measurements of the internal resistance measurements that are made when the engine of the vehicle is off;
measurements of the internal resistance measurements that are made when the engine of the vehicle is idle, and
measurements of the internal resistance measurements that are made when the engine of the vehicle is moving.

10. The one or more devices of claim 8 , wherein the one or more computing devices include a telematics device installed in the vehicle.

11. The one or more devices of claim 8 , wherein the internal resistances of the battery are derived from an ohmic testing device that is built into the battery.

12. The one or more devices of claim 8 , wherein, to evaluate the prediction regarding the remaining battery life, the circuitry is to:
access historical operating conditions of the battery; and
predict the remaining battery life of the battery based on the internal resistances of the battery, the at least one other operating condition, and the historical operating conditions of the battery.

13. The one or more devices of claim 8 , wherein the circuitry is further to:
receive feedback regarding the accuracy of the prediction;
further refine the machine learning model based on the feedback.

14. The one or more devices of claim 8 , wherein the circuitry is further to:
determine, based on another model, at least one other prediction regarding a future performance of the battery;
provide, to a server device, feedback regarding the accuracy of the at least one other prediction; and
receive an updated version of the other model in response to providing the feedback to the server device.

15. One or more computing devices, comprising:
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 d

Assignees

• Verizon Patent & Licensing Inc

Inventors

• Barfield Jr James Ronald
• Welch Stephen Christopher
• Taylor Thomas Steven

Classifications

• G07C5/0816

  Indicating performance data, e.g. occurrence of a malfunction · CPC title

• G07C5/008

  communicating information to a remotely located station (transmission systems for measured values G08C) · CPC title

• G01R31/3648 Primary

  comprising digital calculation means, e.g. for performing an algorithm · CPC title

• G07C5/085

  using electronic data carriers · CPC title

• G01R31/367

  Software therefor, e.g. for battery testing using modelling or look-up tables · CPC title