MODEL-BASED ESTIMATION OF BATTERY HYSTERESIS

• US 20090210179A1
• Filed: 02/19/2008
• Published: 08/20/2009
• Est. Priority Date: 02/19/2008
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations Incorporated (General Motors Company)
• Inventors: Frisch, Damon R., Koch, Brian J., Zhang, Xiaodong, Chin, Yuen-Kwok, Tang, Xidong
• Granted Patent: US 7,957,921 B2
• Time in Patent Office: Days
• US Class Current: 702/63
• CPC Class Codes: H01M 10/48 Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte constructional details of current conducting connections for detecting conditions inside cells or batteries, e.g. details of voltage sensing terminals, H01M50/569
• CPC Class Codes: Y02E 60/10 Energy storage using batteries

Abstract

A method of battery state of charge estimation considering battery hysteresis includes using a Preisach-model-based algorithm to calculate a battery state of charge.

Claims

1. A method of battery state of charge estimation considering battery hysteresis comprising:

using an algorithm based on the Preisach-model to calculate a battery state of charge.

2. The method of claim 1, wherein said using said algorithm comprises:

defining a hysteron state vector considering minimum and maximum values of the state of charge for said battery;

training a hysteron specific weight vector to match representative behavior of the battery; and

applying data inputs related to the battery to said algorithm.

3. The method of claim 2, wherein said hysteron state vector comprises hysteron states.

4. The method of claim 3, wherein said hysteron states comprise groupings of individual elementary hysterons.

5. The method of claim 2, wherein said hysteron state vector comprises elementary hysterons.

6. The method of claim 2, wherein said hysteron specific weight vector is initially trained with hysteron specific weights.

7. The method of claim 6, wherein said training comprises processing test data gathered by cycling a test battery, said cycling operative to capture a boundary hysteresis loop and internal hysteresis loops.

8. The method of claim 2, wherein data inputs include an open circuit voltage.

9. The method of claim 8, wherein said open circuit voltage is estimated from parameters available while said battery is in a state of charge or discharge.

10. The method of claim 2, wherein said data inputs include a battery voltage, a battery current, and a temperature.

11. The method of claim 1, wherein said using said algorithm comprises:

defining a basic hysteresis curve characterized in said Preisach-model;

weighting said basic hysteresis curve with hysteron specific weights based upon training data;

shifting said basic hysteresis curve in order to match an output range of said algorithm to a range of actual battery states of charge; and

applying input data related to said battery to said algorithm.

12. The method of claim 11, wherein said training data comprises test data gathered by cycling a test battery, said cycling operative to capture a boundary hysteresis loop and internal hysteresis loops.

13. The method of claim 11, wherein said range of actual battery states of charge comprise a maximum value corresponding to a state of charge for a fully charged battery and a minimum value corresponding to a state of charge for a fully discharged battery.

14. The method of claim 11, wherein data inputs include an open circuit voltage.

15. The method of claim 14, wherein said open circuit voltage is estimated from parameters available while said battery is in a state of charge or discharge.

16. The method of claim 11, wherein said data inputs include a battery voltage, a battery current, and a temperature.