Vehicle battery analysis system

Patent metadata

Field	Value
Publication number	US-2022074993-A1
Application number	US-202017017016-A
Country	US
Kind code	A1
Filing date	Sep 10, 2020
Priority date	Sep 10, 2020
Publication date	Mar 10, 2022
Grant date	—

Abstract

In one embodiment, a vehicle battery diagnostics system forecasts a future state for a battery by monitoring, over a period of time, one or more of voltage, current or temperature signals from at least one battery of the vehicle, storing information from the voltage, current or temperature signals as time-series data, obtaining a forecasting model from a server, the forecasting model indicating at least one shapelet feature that corresponds to a forecast categorization, identifying, in the time-series data, a shapelet that matches the at least one shapelet feature to a degree exceeding a predetermined similarity threshold, and providing a notification indicating the forecast categorization.

First claim

What is claimed is:

1 . A vehicle battery diagnostics system for a vehicle, comprising:
a communication device to wirelessly transmit data to a server and receive data from the server; one or more processors; and a memory communicably coupled to the one or more processors and storing:
a monitoring module including instructions that when executed by the one or more processors cause the one or more processors to monitor, over a period of time, one or more of voltage, current or temperature signals from at least one battery of the vehicle and store information from the voltage, current or temperature signals as time-series data;
a forecast module including instructions that when executed by the one or more processors cause the one or more processors to obtain a forecasting model from the server, the forecasting model indicating at least one shapelet feature that corresponds to a forecast categorization; and
a classifier module including instructions that when executed by the one or more processors cause the one or more processors to identify, in the time-series data, a shapelet that matches the at least one shapelet feature to a degree exceeding a predetermined similarity threshold, and to provide a notification indicating the corresponding forecast categorization.

2 . The vehicle battery diagnostics system of claim 1 , wherein the forecast categorization indicates one or more of: a suitability of the battery for a second life application, an anomalous behavior of the battery, and a projection of an amount of performance capacity the battery would retain at a future time T.

3 . The vehicle battery diagnostics system of claim 1 , wherein the at least one shapelet is derived from a voltage or current signal, the forecasting model predicts a temperature of the battery from the at least one shapelet, and the classifier module further includes instructions to:
compare a present temperature of the battery detected when the shapelet that matches the at least one shapelet is identified; and determine that the battery exhibits anomalous behavior when the present temperature differs from the predicted temperature by a threshold amount.

4 . The vehicle battery diagnostics system of claim 1 , wherein the forecasting model is trained to classify a battery based on training data obtained from emulating driving conditions for a plurality of batteries identical or similar to the battery of the vehicle.

5 . The vehicle battery diagnostics system of claim 4 , wherein the forecasting model is trained by:
creating a drive-cycle dataset of one or more of voltage, current or temperature values by using cycler equipment to cycle the plurality of batteries to failure under a plurality of conditions; extracting, from the drive-cycle dataset, a plurality of shapelets that are associated with battery cells that exhibit one or more predetermined life-cycle capacity retention traits; enhancing information derived from the plurality of shapelets to create a plurality of features; and training the forecasting model based on the plurality of features to forecast, based on input time-series data, whether the battery will retain a threshold percentage of capacity at a future time T.

6 . The vehicle battery diagnostics system of claim 4 , wherein the plurality of conditions are determined by a drive-cycle profile generator that generates condition profiles in a closed-loop iterative process that receives extracted shapelets as input and adjusts the condition profiles to diversify the extracted shapelets.

7 . The vehicle battery diagnostics system of claim 4 , wherein the plurality of features are created by one or more of:
identifying a minimal distance of shapelets to a given segment of a timeseries; identifying correlative features of shapelets across signal types; and identifying shapelet co-occurrence in a time segment of the time-series data.

8 . A method of diagnosing a vehicle battery for a vehicle, comprising:
monitoring, over a period of time, one or more of voltage, current or temperature signals from at least one battery of the vehicle; storing information from the voltage, current or temperature signals as time-series data; obtaining a forecasting model from a server, the forecasting model indicating at least one shapelet feature that corresponds to a forecast categorization; identifying, in the time-series data, a shapelet that matches the at least one shapelet feature to a degree exceeding a predetermined similarity threshold; and providing a notification indicating the forecast categorization.

9 . The method of claim 8 , wherein the forecast categorization indicates one or more of: a suitability of the battery for a second life application, an anomalous behavior of the battery, and a projection of an amount of performance capacity the battery would retain at a future time T.

10 . The method of claim 8 , wherein the at least one shapelet is derived from a voltage or current signal and the forecasting model predicts a temperature of the battery from the at least one shapelet, the method further comprising:
comparing a present temperature of the battery detected when the shapelet that matches the at least one shapelet is identified; and determining that the battery exhibits anomalous behavior when the present temperature differs from the predicted temperature by a threshold amount.

11 . The method of claim 8 , wherein the forecasting model is trained to classify a battery based on training data obtained from emulating driving conditions for a plurality of batteries identical or similar to the battery of the vehicle.

12 . The method of claim 11 , wherein the forecasting model is trained by:
creating a drive-cycle dataset of one or more of voltage, current or temperature values by using cycler equipment to cycle the plurality of batteries to failure under a plurality of conditions; extracting, from the drive-cycle dataset, a plurality of shapelets that are associated with battery cells that exhibit one or more predetermined life-cycle capacity retention traits; enhancing information derived from the plurality of shapelets to create a plurality of features; and training the forecasting model based on the plurality of features to forecast, based on input time-series data, whether the battery will retain a threshold percentage of capacity at a future time T.

13 . The method of claim 11 , wherein the plurality of conditions are determined by a drive-cycle profile generator that generates condition profiles in a closed-loop iterative process that receives extracted shapelets as input and adjusts the condition profiles to diversify the extracted shapelets.

14 . The method of claim 11 , wherein the plurality of features are created by one or more of:
identifying a minimal distance of shapelets to a given segment of a timeseries; identifying correlative features across signal types; and identifying shapelet co-occurrence in a time segment of the time-series data.

15 . A non-transitory computer-readable medium for diagnosing a vehicle battery for a vehicle, including instructions that, when executed by one or more processors, cause the one or more processors to:
monitor, over a period of time, one or more of voltage, current or temperature signals from at least one battery of the vehicle; store information from the signals as time-series data; obtain a forecasting model from a server, the forecasting model indicating at least one shapelet feature that corresponds to a forecast categorization; identify, in the time-series data, a shapelet that matches the at least one shapelet featur

Assignees

• Toyota Res Inst Inc

Inventors

• Aykol Muratahan
• Balaji Gopal Chirranjeevi
• Herring Patrick K
• Anapolsky Abraham S

Classifications

• G06F30/15

  Vehicle, aircraft or watercraft design · CPC title

• Y02T90/16

  Information or communication technologies improving the operation of electric vehicles · CPC title

• Y02T10/72

  Electric energy management in electromobility · CPC title

• Y02T10/70

  Energy storage systems for electromobility, e.g. batteries · CPC title

• Y02E60/10

  Energy storage using batteries · CPC title