System and method for estimating a state vector associated with a battery

• US 20070046292A1
• Filed: 08/23/2005
• Published: 03/01/2007
• Est. Priority Date: 08/23/2005
• Status: Active Grant

• Current Assignee: LG Energy Solution Limited (LG Corporation)
• Original Assignee: LG Chem Limited (LG Corporation)
• Inventors: Plett, Gregory
• Granted Patent: US 7,589,532 B2
• Time in Patent Office: Days
• US Class Current: 324/429
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables
• CPC Class Codes: G01R 31/3835 involving only voltage measurements
• CPC Class Codes: G01R 31/3842 combining voltage and current measurements

Abstract

A system and a method for estimating a state vector associated with a battery are provided. The method includes determining a time interval that the battery has been electrically decoupled from a load circuit. The time interval starts at a first time. The method further includes obtaining a first state vector associated with the battery from a memory. The first state vector is determined prior to the first time. The method further includes calculating a second predicted state vector associated with the battery based on the first state vector and the time interval.

Claims

1. A method for estimating a state vector associated with a battery, the method comprising:

determining a time interval that the battery has been electrically decoupled from a load circuit, the time interval starting at a first time;

obtaining a first state vector associated with the battery from a memory, the first state vector being determined prior to the first time;

calculating a second predicted state vector associated with the battery based on the first state vector and the time interval;

measuring a battery voltage output by the battery to obtain a first battery voltage value after the first time interval when the battery is electrically re-coupled to the load circuit;

estimating a second battery voltage value associated with the battery based on the second predicted state vector;

calculating a voltage error value based on the first battery voltage value and the second battery voltage value; and

calculating a third estimated state vector associated with the battery based on the second predicted state vector and the voltage error value.

2. The method of claim 1, further comprising calculating a covariance value associated with the third estimated state vector.

3. The method of claim 1, wherein the step of calculating the second predicted state vector comprises calculating the second predicted state vector associated with the battery based on the first state vector and the time interval utilizing at least one of a Kalman filter, an extended Kalman filter, a sigma-point Kalman filter, a square-root sigma-point Kalman filter, and a particle filter.

4. The method of claim 1, wherein the step of calculating the third estimated state vector comprises calculating the third estimate state vector associated with the battery based on the second predicted state vector and the voltage error value interval utilizing at least one of a Kalman filter, an extended Kalman filter, a sigma-point Kalman filter, a square-root sigma-point Kalman filter, and a particle filter.

5. The method of claim 1, wherein the second predicted state vector is indicative of at least a predicted state-of-charge of the battery.

6. The method of claim 1, wherein the third predicted state vector is indicative of at least a predicted state-of-charge of the battery.

7. A system for estimating a state vector associated with a battery, the system comprising:

a voltage sensor configured to measure a voltage output from the battery; and

a computer operably coupled to the voltage sensor, the computer configured to determine a time interval that the battery has been electrically decoupled from a load circuit, the time interval starting at a first time, the computer further configured to obtain a first state vector associated with the battery from a memory, the first state vector being determined prior to the first time, the computer further configured to calculate a second predicted state vector associated with the battery based on the first state vector and the time interval, the computer further configured to induce the voltage sensor to measure the voltage output from the battery to obtain a first battery voltage value after the first time interval when the battery is electrically coupled to the load circuit, the computer further configured to estimate a second battery voltage value associated with the battery based on the second predicted state vector, the computer further configured to calculate a voltage error value based on the first battery voltage value and the second battery voltage value, the computer further configured to calculate a third estimated state vector associated with the battery based on the second predicted state vector and the voltage error value.

8. The system of claim 7, further comprising calculating a covariance value associated with the third estimated state vector.

9. The system of claim 7, wherein the computer is further configured to calculate the second predicted state vector associated with the battery based on the first state vector and the time interval, utilizing at least one of a Kalman filter, an extended Kalman filter, a sigma-point Kalman filter, a square-root Kalman filter, a square-root extended Kalman filter, a square-root sigma-point Kalman filter, and a particle filter.

10. The system of claim 7, wherein the computer is further configured to calculate the third estimated state vector associated with the battery based on the second predicted state vector, the first measured battery voltage, and the second estimated battery voltage, utilizing at least one of a Kalman filter, an extended Kalman filter, a sigma-point Kalman filter, a square-root Kalman filter, a square-root extended Kalman filter, a square-root sigma-point Kalman filter, and a particle filter.

11. The system of claim 7, wherein the second predicted state vector is indicative of at least a predicted state-of-charge of the battery.

12. The system of claim 7, wherein the third estimated state vector is indicative of at least a predicted state-of-charge of the battery.

13. An article of manufacture, comprising:

a computer storage medium having a computer program encoded therein for estimating a state vector associated with a battery, the computer storage medium comprising;

code for determining a time interval that the battery has been electrically decoupled from a load circuit, the time interval starting at a first time;

code for obtaining a first state vector associated with the battery from a memory, the first state vector being determined prior to the first time;

code for calculating a second predicted state vector associated with the battery based on the first state vector and the time interval;

code for measuring a battery voltage output from the battery to obtain a first battery voltage value after the first time interval when the battery is electrically coupled to the load circuit;

code for estimating a second battery voltage value associated with the battery based on the second predicted state vector;

code for calculating a voltage error value based on the first battery voltage value and the second battery voltage value; and

code for calculating a third estimated state vector associated with the battery based on the second predicted state vector and the voltage error value.

14. The article of manufacture of claim 13, wherein the computer storage medium further comprises code for calculating a covariance value associated with the third estimated state vector.

15. The article of manufacture of claim 13, wherein the code for calculating the second predicted state vector comprises code for calculating the second predicted state vector based on the first state vector and the time interval utilizing at least one of a Kalman filter, an extended Kalman filter, a sigma-point Kalman filter, a square-root Kalman filter, a square-root extended Kalman filter, a square-root sigma-point Kalman filter, and a particle filter.

16. The article of manufacture of claim 13, wherein the code for calculating the third estimated state vector comprises code for calculating the third estimated state vector based on the second predicted state vector, the first measured battery voltage and the second estimated battery voltage utilizing at least one of a Kalman filter, an extended Kalman filter, a sigma-point Kalman filter, a square-root Kalman filter, a square-root extended Kalman filter, a square-root sigma-point Kalman filter, and a particle filter.

17. The article of manufacture of claim 13, wherein the second predicted state vector is indicative of at least a predicted state-of-charge of the battery.

18. The article of manufacture of claim 13, wherein the third estimated state vector is indicative of at least a predicted state-of-charge of the battery.