Battery failure prediction

1. A method comprising:

obtaining, by a processor, driver characterization data based on sensor data from one or more sensors onboard a vehicle, the sensor data captured during a time period that includes multiple discharging operations and multiple recharging operations of a battery pack of the vehicle;

providing, by the processor, the driver characterization data as input to a trained model to generate a model output, wherein the model output includes a classification that associates the driver characterization data with a driver type profile; and

estimating, by the processor, a battery failure timeline for the battery pack based on the classification.

2. The method of claim 1 , wherein the trained model corresponds to a neural network.

3. The method of claim 1 , wherein the sensor data comprises data determined by at least one sensor within the battery pack.

4. The method of claim 1 , wherein the sensor data comprises data determined by at least one sensor coupled to the battery pack.

5. The method of claim 1 , wherein the sensor data comprises data determined by an environmental sensor.

6. The method of claim 1 , wherein the time period includes at least one year.

7. The method of claim 1 , wherein the time period includes portions of at least two seasons.

8. The method of claim 1 , further comprising scheduling maintenance for the vehicle based on the battery failure timeline.

9. The method of claim 1 , further comprising:

storing data associating multiple vehicles with a maintenance location;

estimating battery failure timelines for battery packs of the multiple vehicles using the trained model; and

scheduling maintenance resources for the maintenance location based on the estimated battery failure timelines.

10. The method of claim 9 , wherein the maintenance resources include one or more of spare parts, test equipment, technical manuals, or technicians.

11. The method of claim 1 , wherein the battery failure timeline indicates expected life of a particular cell of the battery pack.

12. The method of claim 1 , wherein the driver type profile includes a brake pressure curve corresponding to a particular road condition.

13. The method of claim 1 , further comprising predicting warranty costs, maintenance costs, or both, based on the battery failure timeline.

14. The method of claim 1 , further comprising:

estimating battery failure timelines for battery packs of multiple vehicles using the trained model;

grouping drivers of the multiple vehicles into driver type groups based on the driver characterization data;

obtaining actual battery failure data for at least a subset of the multiple vehicles; and

identifying design or manufacturing defects that tend to produce early battery failures for particular driver types based on the driver type groups, the actual battery failure data, and the estimated battery failure timelines.

15. The method of claim 1 , wherein the driver characterization data indicates one or more of distances traveled, charge/discharge behavior, travel speed, acceleration/deceleration behavior, auxiliary equipment usage, or driving environment.

16. A system comprising:

one or more processors; and

one or more memory devices coupled to the one or more processors, the one or more memory devices storing instructions that are executable to cause the one or more processors to perform operations comprising:

obtaining driver characterization data based on sensor data from one or more sensors onboard a vehicle, the sensor data captured during a time period that includes multiple discharging operations and multiple recharging operations of a battery pack of the vehicle;

providing the driver characterization data as input to a trained model to generate a model output, wherein the model output includes a classification that associates the driver characterization data with a driver type profile; and

estimating a battery failure timeline for the battery pack based on the classification.

17. The system of claim 16 , wherein the trained model corresponds to a neural network.

18. The system of claim 17 , wherein the trained model comprises a classifier that associates a driver with the driver type profile based on the driver characterization data.

19. The system of claim 16 , further comprising at least one sensor coupled to or within the battery pack and configured to generate at least a portion of the sensor data.

20. The system of claim 16 , further comprising at least one environmental sensor configured to generate at least a portion of the sensor data.