Battery Service Life Estimation Methods, Apparatus and Computer Program Products Using State Estimation Techniques Initialized Using a Regression Model

• US 20090228225A1
• Filed: 03/04/2008
• Published: 09/10/2009
• Est. Priority Date: 03/04/2008
• Status: Abandoned Application

• Current Assignee: Eaton Corp. (Wisconsin) (Eaton Corporation PLC.)
• Original Assignee: Eaton Corp. (Wisconsin) (Eaton Corporation PLC.)
• Inventors: Burgess, Webb Lewis
• Application Number:
  US12/041,801
• Publication Number:
  US 20090228225A1
• Time in Patent Office: Days
• US Class Current: 702/63
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables

Abstract

A measure of capacity of the battery responsive is generated responsive to a discharge of the battery. A prediction of service life is generated from the measure of capacity using a Kalman filter. Generation of a prediction of service life from the measure of capacity using a Kalman filter may be preceded by generating a set of measures of capacity of the battery corresponding to a series of discharges of the battery and generating a regression model, such as a straight-line model, that relates battery capacity to time based on the set of measures of battery capacity. Generating a prediction of service life from the measure of capacity using a Kalman filter may include initializing the Kalman filter responsive to the regression model meeting a predetermined criterion.

Claims

1. A method of estimating service life of a battery, the method comprising the following steps implemented in a computer:

generating a measure of capacity of the battery responsive to a discharge of the battery; and

generating a prediction of service life from the measure of capacity using a Kalman filter.

2. The method of claim 1:

wherein generating a prediction of service life from the measure of capacity using a Kalman filter is preceded by;

generating a set of measures of capacity of the battery corresponding to a series of discharges of the battery; and

generating a regression model that relates battery capacity to time based on the set of measures of battery capacity; and

wherein generating a prediction of service life from the measure of capacity using a Kalman filter comprises initializing the Kalman filter responsive to the regression model meeting a predetermined criterion.

3. The method of claim 2, wherein initializing the Kalman filter responsive to the regression model meeting a predetermined criterion comprises initializing the Kalman filter with an initial condition generated from the regression model.

4. The method of claim 2, wherein generating a regression model comprises generating a straight-line regression model.

5. The method of claim 2:

wherein generating a measure of capacity of the battery responsive to a discharge of the battery comprises generating a series of measures of capacity of the battery responsive to a series of discharges of the battery following initialization of the Kalman filter; and

wherein generating a prediction of service life from the measure of capacity using a Kalman filter comprises generating a series of predictions of service life from the series of measures of capacity using the Kalman filter.

6. The method of claim 1, wherein generating a measure of capacity of the battery responsive to a discharge of the battery comprises:

determining a time required to discharge the battery to a predetermined voltage; and

generating the measure of capacity based on the determined discharge time.

7. The method of claim 1, wherein generating a prediction of service life from the measure of battery capacity using a Kalman filter is preceded by detecting an acceleration of a decrease in battery capacity and wherein generating a prediction of service life from the measure of capacity using a Kalman filter comprises initializing the Kalman filter responsive to the detected acceleration of decrease in battery capacity.

8. A method of estimating service life of a battery, the method comprising the following steps implemented a computer:

generating a set of measures of battery capacity responsive to a series of discharges of the battery;

generating a regression model that relates battery capacity to time based on the set of measures of battery capacity;

initializing a state estimator of future battery capacity responsive to the regression model meeting a predetermined criterion; and

generating a prediction of service life using the state estimator.

9. The method of claim 8, wherein generating a regression model comprises generating a straight-line regression model from the set of measures of capacity.

10. The method of claim 8, wherein the state estimator comprises a deterministic model of battery capacity degradation, a probabilistic model of battery capacity degradation an/or an adaptive model of battery capacity degradation.

11. The method of claim 8, wherein the state estimator comprises a Kalman filter.

12. The method of claim 8, wherein initializing a state estimator of future battery capacity responsive to the regression model meeting a predetermined criterion comprises initializing the state estimator with an initial condition generated according to the regression model.

13. An apparatus comprising:

a battery service life estimator circuit configured to receive a measure of capacity of a battery and to generate a prediction of service life from the measure of capacity using a Kalman filter.

14. The apparatus of claim 13, wherein the battery service life estimator circuit is configured to generate a set of measures of capacity of the battery corresponding to a series of discharges of the battery, to generate a regression model that relates battery capacity to time based on the set of measures of battery capacity and to initialize the Kalman filter responsive to the regression model meeting a predetermined criterion.

15. The apparatus of claim 14, wherein the battery service life estimator circuit is configured to initialize the Kalman filter with an initial condition generated from the regression model.

16. The apparatus of claim 14, wherein the battery service life estimator circuit is configured to generate a straight-line regression model based on the set of measures of battery capacity and to initialize the Kalman filter responsive to the straight-line regression model meeting a predetermined criterion.

17. The apparatus of claim 14, wherein the battery service life estimator circuit is further configured to receive a series of measures of capacity of the battery corresponding to respective ones of a series of discharges of the battery following initialization of the Kalman filter and to generate a series of predictions of service life from the series of measures of capacity using the Kalman filter.

18. The apparatus of claim 13, further comprising a battery capacity measurement circuit configured to generate the measure of battery capacity responsive to the discharge of the battery.

19. The apparatus of claim 18, wherein the battery capacity measurement circuit is configured to determine a time required to discharge the battery to a predetermined voltage and to generate the measure of capacity based on the determined discharge time.

20. The apparatus of claim 13, wherein the battery service life estimator circuit is configured to detect an acceleration of a decrease in battery capacity and to initialize the Kalman filter responsive to the detected acceleration of the decrease in battery capacity.

21. The apparatus of claim 13, further comprising:

an uninterruptible power supply (UPS) circuit configured to be coupled to the battery and to charge and discharge the battery; and

a battery capacity measurement circuit configured to generate the measure of capacity of the battery responsive to a discharge of the battery by the UPS circuit.

22. The apparatus of claim 13, wherein the battery capacity measurement circuit and the battery service life estimator circuit are implemented in a processor that controls the UPS.

23. An apparatus comprising:

a battery service life estimator circuit configured to receive a set of measures of battery capacity for a battery, to generate a regression model that relates battery capacity to time based on the set of measures of battery capacity, to initialize a state estimator of future battery capacity responsive to the regression model meeting a predetermined criterion and to generate a prediction of service life using the state estimator.

24. The apparatus of claim 23, wherein the battery service life estimator circuit is configured to generate a straight-line regression model from the set of measures of capacity and to initialize the state estimator responsive to the straight-line regression model meeting a predetermined criterion.

25. The apparatus of claim 23, wherein the state estimator comprises a deterministic model of battery capacity degradation, a probabilistic model of battery capacity degradation an/or an adaptive model of battery capacity degradation.

26. The apparatus of claim 23, wherein the state estimator comprises a Kalman filter.

27. The apparatus of claim 23, wherein the battery service life estimator circuit is configured to initialize the state estimator with an initial condition generated according to the regression model.

28. The apparatus of claim 23, further comprising:

an uninterruptible power supply (UPS) configured to be coupled to the battery and to charge and discharge the battery; and

a battery capacity measurement circuit configured to generate the measure of capacity of the battery responsive to a discharge of the battery by the UPS.

29. The apparatus of claim 23, wherein the battery capacity measurement circuit and the battery service life estimator circuit are implemented in a processor that controls the UPS.

30. A computer-readable medium having computer program code embodied therein, the computer program code comprising:

program code configured to receive a measure of capacity of the battery; and

program code configured to generate a prediction of service life from the measure of capacity using a Kalman filter.

31. The computer-readable medium of claim 30, wherein the program code configured to generate a prediction of service life from the measure of capacity using a Kalman filter comprises:

program code configured to generate a regression model that relates battery capacity to time based on the set of measures of battery capacity; and

program code configured to initialize the Kalman filter responsive to the regression model meeting a predetermined criterion.

32. The computer-readable medium of claim 31, wherein the program code configured to initialize the Kalman filter responsive to the regression model meeting a predetermined criterion comprises program code configured to initialize the Kalman filter with an initial condition generated from the regression model.

33. A computer-readable medium having computer program code embodied therein, the computer program code comprising:

program code configured to receive a set of measures of battery capacity corresponding to respective ones of a series of discharges of a battery;

program code configured to generate a regression model that relates battery capacity to time based on the set of measures of battery capacity;

program code configured to initialize a state estimator of future battery capacity responsive to the regression model meeting a predetermined criterion; and

program code configured to generate a prediction of service life using the state estimator.

34. The computer-readable medium of claim 33, wherein the state estimator comprises a Kalman filter.

35. The computer-readable medium of claim 33, wherein the program code configured to initialize a state estimator of future battery capacity responsive to the regression model meeting a predetermined criterion comprises program code configured to initialize the state estimator with an initial condition generated according to the regression model.