BATTERY HEALTH ASSESSMENT ESTIMATOR

• US 20100198536A1
• Filed: 01/30/2009
• Published: 08/05/2010
• Est. Priority Date: 01/30/2009
• Status: Active Grant

• Current Assignee: BAE Systems Controls Incorporated (BAE Systems Plc)
• Original Assignee: BAE Systems Controls Incorporated (BAE Systems Plc)
• Inventors: Hess, Robert A.
• Granted Patent: US 8,116,998 B2
• Time in Patent Office: Days
• US Class Current: 702/63
• CPC Class Codes: G01R 31/3842 combining voltage and current measurements
• CPC Class Codes: G01R 31/392 Determining battery ageing or deterioration, e.g. state of health

Abstract

A battery state of health estimator and similar method, system and computer product is disclosed providing for a estimate of a state of health (SOH) of one or more batteries, comprising, estimating a sampling of internal resistances of the one or more batteries, generating a time history of the internal resistance over a predetermined amount of time, generating a cumulative internal resistance histogram from the time history, calculated a final estimate of internal resistance of said one or more batteries which represents the calculated SOH of said one or more batteries and comparing the calculated SOH to a predetermined critical resistance threshold. If the calculated SOH is less than the predetermined critical resistance threshold, the battery is in no worse than a “Blue Monday” condition, and if the calculated SOH is greater than the critical resistance threshold, then the one or more batteries has failed.

Claims

1. A method of estimating a state of health (S OH) of one or more batteries, comprising the steps of:

estimating a sampling of internal resistances of said one or more batteries;

generating a time history of said internal resistances over a predetermined amount of time;

generating a cumulative internal resistance histogram from said time history;

calculating a final estimate of the internal resistance of said one or more batteries which represents the calculated SOH of said one or more batteries; and

comparing said calculated SOH to a predetermined critical resistance threshold to determining if said one or more batteries are in a failed condition.

2. The method of claim 1, wherein if said calculated SOH is less than the predetermined critical resistance threshold, one or more batteries are in no worse than a Blue Monday condition, and if said calculated SOH is more than the critical resistance threshold, then said one or more batteries has failed.

3. The method of claim 1, wherein the sampling of internal resistances are estimated by employing a state estimator.

4. The method of claim 3, wherein the state estimator is a Kalman filter.

5. The method of claim 1, wherein the step of generating a cumulative histogram further includes the step of setting a smallest partition in said internal resistance histogram to zero count.

6. The method of claim 1 further comprising the steps of:

generating an open circuit voltage (OCV) histogram of a nominal open circuit voltage from each of said one or more batteries;

selecting a peak OCV value from said generated OCV histogram; and

comparing said selected peak OCV value to a predetermined nominal range,

wherein if said selected peak OCV value is outside said predetermined nominal range, said one or more batteries has a shorted cell.

7. The method of claim 1 further comprising the steps of:

estimating an open circuit voltage (OCV) of said one or more batteries as a function of a predetermined state of charge;

comparing said estimated OCV to a predetermined nominal value,

wherein if said estimated OCV is below said predetermined nominal value said one or more batteries are degraded.

8. A system of estimating a state of health (SOH) of one or more batteries, comprising:

means for estimating a sampling of internal resistances of said one or more batteries;

means for generating a time history of said sampling of internal resistances over a predetermined amount of time;

means for generating a cumulative internal resistance histogram from said time history;

means for calculating a final estimate of internal resistance of said one or more batteries which represents the calculated SOH of said one or more batteries; and

means for comparing said calculated SOH to a predetermined critical resistance threshold to determining if said one or more batteries are in a failed condition.

9. The system of claim 8, wherein if said calculated SOH is less than the predetermined critical threshold said battery is in no worse than a “

Blue Monday”

condition, and if said calculated SOH is more than the critical resistance threshold, then said one or more batteries has failed.

10. The system of claim 8, wherein the means for estimating the sampling of internal resistances employs a state estimator.

11. The system of claim 10, wherein the state estimator is a Kalman filter.

12. The system of claim 8, wherein the means for generating a cumulative histogram further includes a means for setting the smallest partition in said internal resistance histogram to zero count.

13. The system of claim 8 further comprising:

means for generating an open circuit voltage (OCV) histogram of a nominal open circuit voltage from each of said one or more batteries;

means for selecting a peak OCV value from said generated OCV histogram; and

means for comparing said selected peak OCV value to a predetermined nominal value,

wherein if said selected peak OCV value is below said predetermined nominal value, said one or more batteries has a shorted cell.

14. The system of claim 8 further comprising:

means for estimating an open circuit voltage (OCV) of said one or more batteries as a function of a predetermined state of charge;

means for comparing said estimated OCV to a predetermined nominal value,

wherein if said estimated OCV is below said predetermined nominal value, said one or more batteries are degraded.

15. A method of determining if one or more batteries are shorted, comprising the steps of :

generating an open circuit voltage (OCV) histogram of a nominal open circuit voltage from each of said one or more batteries;

selecting a peak OCV value from said generated OCV histogram; and

comparing said selected peak OCV value to a predetermined nominal range, wherein if said selected peak OCV value is outside said predetermined nominal range, said one or more batteries has a shorted cell.

16. The method of claim 15 further comprising the steps of:

estimating an open circuit voltage (OCV) of said one or more batteries as a function of a predetermined state of charge;

comparing said estimated OCV to a predetermined nominal value,

wherein if said estimated OCV is below said predetermined nominal value, said one or more batteries are degraded.

17. The method of claim 15 further comprising the steps of:

estimating a sampling of internal resistances of said one or more batteries;

generating a time history of said sampling of internal resistances over a predetermined amount of time;

generating a cumulative internal resistance histogram from said time history;

calculating a final estimate of internal resistance of said one or more batteries which represents the calculated SOH of said one or more batteries; and

comparing said calculated SOH to a predetermined critical resistance threshold,

wherein if said calculated SOH is less than the predetermined critical resistance threshold, said battery is in no worse than a Blue Monday condition, and if said calculated SOH is greater than the critical resistance threshold, then said one or more batteries has failed.

18. The method of claim 17, wherein the sampling of the internal resistances are estimated by employing a state estimator.

19. The method of claim 17, wherein the step of generating a cumulative histogram further includes the step of setting a smallest partition in said cumulative internal resistance histogram to zero count.

20. A computer program product comprising computer usable medium having computer readable program code embodied therein for enabling a computer to estimate a state of health (SOH) of one or more batteries, and for causing the computer to activate the steps of:

estimating a sampling of internal resistances of said one or more batteries;

generating a time history of said internal resistances over a predetermined amount of time;

generating a cumulative internal resistance histogram from said time history;

calculating a final estimate of internal resistance of said one or more batteries which represents the calculated SOH of said one or more batteries; and

comparing said calculated SOH to a predetermined critical resistance threshold,

wherein if said calculated SOH is less than the predetermined critical threshold, said battery is in no worse than a Blue Monday condition, and if said calculated SOH is greater than the critical resistance threshold, then said one or more batteries has failed.