Method and apparatus for a battery state of charge estimator

• US 6,534,954 B1
• Filed: 01/10/2002
• Issued: 03/18/2003
• Est. Priority Date: 01/10/2002
• Status: Expired due to Term

• Current Assignee: LG Energy Solution Limited (LG Corporation)
• Original Assignee: LG Chem Michigan Inc. (LG Corporation)
• Inventors: Plett, Gregory L.
• Primary Examiner(s): Tso, Edward H.
• Application Number:
  US10/044,278
• Time in Patent Office: 432 Days
• Field of Search: 320/127, 320/128, 320/132, 320/149, 320/DIG.21, 324/427, 324/430, 324/433, 340/635, 340/636
• US Class Current: 320/132
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables
• 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

The present invention relates to an implementation of a battery State of Charge (SOC) estimator for any battery type. It addresses the problems associated with the existing implementations such as high error uncertainty, limited application (i.e. only one battery type) and susceptibility to temperature changes. Embodiments of the present invention use Kalman filter or Extended Kalman filter for a battery model that has SOC as an internal system state. Having an SOC internal state allows the invention to provide an uncertainty associated with its SOC estimation. One embodiment does not take battery temperature as a parameter in its SOC estimation. Another embodiment uses battery temperature as a parameter to adjust its SOC estimation to keep the accuracy of the SOC estimation from being affected by changing temperature. One embodiment allows different modeling parameters during battery operation to accommodate highly dynamic batteries used in Hybrid Electric Vehicles and Electric Vehicles.

Claims

1. A method for estimating state-of-charge in a battery, comprising:

making an internal states prediction of said battery where said state-of-charge is one of said internal states;

making an uncertainty prediction of said internal states prediction;

correcting said internal states prediction and said uncertainty prediction; and

applying an algorithm that iterates said making an internal states prediction, said making an uncertainty prediction and said correcting to yield an ongoing estimation to said state-of-charge and an ongoing uncertainty to said state-of-charge estimation.

2. The method of claim 1 where said making an internal states prediction comprises:

determining a current measurement;

determining a voltage measurement; and

using said current measurement and said voltage measurement in a mathematical model to make said internal states prediction.

3. The method of claim 2 where said making an uncertainty prediction comprises using said current measurement and said voltage measurement in a mathematical model to make said uncertainty prediction.

4. The method of claim 3 where said correcting comprises:

computing a gain factor;

computing a corrected internal states prediction using said gain factor, said voltage measurement and said internal states prediction; and

computing a corrected uncertainty prediction using said gain factor and said uncertainty prediction.

5. The method of claim 4 where said applying comprises using said corrected internal states prediction and said corrected uncertainty prediction to obtain predictions for the next time step where said algorithm repeats again.

6. The method of claim 5, where said algorithm is a Kalman Filter.

7. The method of claim 5, where said algorithm is an Extended Kalman Filter.

8. The method of claim 7, where said making an internal states prediction further comprises using different mathematical models for predictions based on changing battery conditions.

9. The method of claim 7, where said making an uncertainty prediction further comprises using different mathematical models for predictions based on changing battery conditions.

10. The method of claim 2 where said making an internal states prediction further comprises:

determining a temperature; and

using said temperature measurement, said current measurement and said voltage measurement in a mathematical model to make said internal states prediction.

11. The method of claim 10 where said making an uncertainty prediction comprises using said temperature measurement, said current measurement and said voltage measurement in a mathematical model to make said uncertainity prediction.

12. The method of claim 11 where said correcting comprises:

computing a gain factor;

computing a corrected internal states prediction using said gain factor, said voltage measurement and said internal states prediction; and

computing a corrected uncertainty prediction using said gain factor and said uncertainty prediction.

13. The method of claim 12 where said applying comprises using said corrected internal states prediction and said corrected uncertainty prediction to obtain predictions for the next time step where said algorithm repeats again.

14. The method of claim 13, where said algorithm is a Kalman Filter.

15. The method of claim 13, where said algorithm is an Extended Kalman Filter.

16. The method of claim 15, where said making an internal states prediction further comprises using different mathematical models for predictions based on changing battery conditions.

17. The method of claim 15, where said making an uncertainty prediction further comprises using different mathematical models for predictions based on changing battery conditions.

18. An apparatus for estimating state-of-charge in a battery, comprising:

a component configured to make an internal states prediction of said battery where said state-of-charge is one of said internal states;

a component configured to make an uncertainty prediction of said internal states prediction;

a component configured to correct said internal states prediction and said uncertainty prediction; and

a component configured to apply an algorithm that iterates steps taken by said component configured to make an internal states prediction, said component configured to make an uncertainty prediction and said component configured to correct to yield an ongoing estimation to said state-of-charge and an ongoing uncertainty to said state-of-charge estimation.

19. The apparatus of claim 18 where said component configured to make an internal states prediction comprises:

a component configured to determine a current measurement;

a component configured to determine a voltage measurement; and

a component configured to use said current measurement and said voltage measurement in a mathematical model to make said internal states prediction.

20. The apparatus of claim 19 where said component configured to make an uncertainty prediction comprises a component configured to use said current measurement and said voltage measurement in a mathematical model to make said uncertainty prediction.

21. The apparatus of claim 20 where said component configured to correct comprises:

a component configured to compute a gain factor;

a component configured to compute a corrected internal states prediction using said gain factor, said voltage measurement and said internal states prediction; and

component configured to compute a corrected uncertainty prediction using said gain factor and said uncertainty prediction.

22. The apparatus of claim 21 where said component configured to apply comprises a component configured to use said corrected internal states prediction and said corrected uncertainty prediction to obtain predictions for the next time step where said algorithm repeats again.

23. The apparatus of claim 22, where said algorithm is a Kalman Filter.

24. The apparatus of claim 22, where said algorithm is an Extended Kalman Filter.

25. The apparatus of claim 24, where said component configured to make an internal states prediction further comprises a component configured to use different mathematical models for predictions based on changing battery conditions.

26. The apparatus of claim 24, where said component configured to make an uncertainty prediction further comprises a component configured to use different mathematical models for predictions based on changing battery conditions.

27. The apparatus of claim 19 where said component configured to make an internal states prediction further comprises:

a component configured to determine a temerpature measurement; and

a component configured to use said temperature measurement, said current measurement and said voltage measurement in a mathematical model to make said internal states prediction.

28. The apparatus of claim 27 where said component configured to make an uncertainty prediction comprises a component configured to use said temperature measurement, said current measurement and said voltage measurement in a mathematical model to make said uncertainty prediction.

29. The apparatus of claim 28 where said component configured to correct comprises:

a component configured to compute a gain factor;

a component configured to compute a corrected internal states prediction using said gain factor, said voltage measurement and said internal states prediction; and

a component configured to compute a corrected. uncertainty prediction using said gain factor and said uncertainty prediction.

30. The apparatus of claim 28 where said component configured to apply comprises a component configured to use said corrected internal states prediction and said corrected uncertainty prediction to obtain predictions for the next time step where said algorithm repeats again.

31. The apparatus of claim 30, where said algorithm is a Kalman Filter.

32. The apparatus of claim 30, where said algorithm is an Extended Kalman Filter.

33. The apparatus of claim 32, where said component configured to make an internal states prediction further comprises a component configured to use different mathematical models for predictions based on changing battery conditions.

34. The apparatus of claim 32, where said component configured to make an uncertainty prediction further comprises a component configured to use different mathematical models for predictions based on changing battery conditions.