METHOD FOR ANOMALY PREDICTION OF BATTERY PARASITIC LOAD

• US 20090055330A1
• Filed: 08/23/2007
• Published: 02/26/2009
• Est. Priority Date: 08/23/2007
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations Incorporated (Motors Liquidation Co. GUC Trust)
• Inventors: Barajas, Leandro G., Zhang, Yilu, Medasani, Swarup, Srinivasa, Narayan, Jiang, Qin, Kapsokavathis, Nick S.
• Granted Patent: US 7,761,389 B2
• Time in Patent Office: Days
• US Class Current: 706/2
• CPC Class Codes: G01R 31/3647 for determining the ability of a battery to perform a critical function, e.g. cranking
• CPC Class Codes: G01R 31/382 Arrangements for monitoring battery or accumulator variables, e.g. SoC

Abstract

Anomaly prediction of battery parasitic load includes processing input data related to a state of charge for a battery and a durational factor utilizing a machine learning algorithm and generating a predicted start-up state of charge. Warnings are issued if the predicted start-up state of charge drops below a threshold level within an operational time.

Claims

1. A method for anomaly prediction of parasitic load on a battery comprising:

processing input data related to a state of charge for the battery and a durational factor, wherein said processing comprises a machine learning algorithm and is operative to generate a predicted start-up state of charge; and

indicating a warning if said predicted start-up state of charge is below a threshold level within an operational time.

2. The method of claim 1, wherein said machine learning algorithm comprises fuzzy logic.

3. The method of claim 2, wherein said fuzzy logic updates on the basis of said input data, said predicted start-up state of charge, and a prediction error.

4. The method of claim 3, wherein said fuzzy logic correlates a temporal indication to said input data, such that said processing indicates temporal trends in the input data.

5. The method of claim 2, wherein said machine learning algorithm further comprises a neural network.

6. The method of claim 1, wherein said machine learning algorithm comprises a neural network.

7. The method of claim 6, wherein said neural network updates on the basis of said input data, said predicted start-up state of charge, and a prediction error.

8. The method of claim 1, wherein said input data relating to said state of charge comprises a running state of charge.

9. The method of claim 1, wherein said input data relating to said state of charge comprises a battery state of health.

10. The method of claim 1, wherein said input data relating to said durational factor comprises a key-off time.

11. The method of claim 1, wherein said threshold level is a predetermined critical level, said critical level being a calculated lowest charge that will support a start-up event.

12. The method of claim 1, wherein said threshold level is a predetermined critical level, said critical level being a predetermined measure higher than a calculated lowest charge that will support a start-up event.

13. The method of claim 1, wherein said threshold level is a threshold function based upon said durational factor.

14. The method of claim 1, further comprising:

measuring an actual state of charge of said battery;

comparing said actual state of charge to said predicted start-up state of charge; and

initiating a warning if said comparison indicates that said actual state of charge has dropped more than a threshold drop below said predicted start-up state of charge.

15. The method of claim 1, wherein said warning comprises an alert by means of a communications network.

16. The method of claim 1, wherein said warning comprises reduced functionality of devices utilizing said battery.

17. The method of claim 1, wherein said processing further comprises heuristic techniques for the purpose of overcoming inconsistencies in said input data.

18. The method of claim 1, wherein said processing further comprises trend analysis whereby a battery state of health for said battery is developed.

19. A method for anomaly prediction of parasitic load on a battery in a motor vehicle comprising:

receiving input data related to a running state of charge for the battery and a key-off time;

processing said input data to create a predicted start-up state of charge for said battery, wherein said processing comprises a machine learning algorithm; and

initiating a warning if said predicted start-up state of charge is below a threshold level before a threshold time.

20. The method of claim 19, wherein said machine learning algorithm comprises fuzzy logic operable to analyze and identify said input data into a plurality of fuzzy groups indicating a degree to which said input data conforms to historical input data sets, said historical input data sets being correlated to resulting prediction errors.

21. The method of claim 20, wherein said machine learning algorithm comprises a layered feed-forward neural network operative to adapt to multiple iterations of said input data and related prediction errors.