System, method, and article of manufacture for determining an estimated combined battery state-parameter vector

• US 20070188143A1
• Filed: 02/09/2006
• Published: 08/16/2007
• Est. Priority Date: 02/09/2006
• Status: Active Grant

• Current Assignee: LG Energy Solution Limited (LG Corporation)
• Original Assignee: LG Chem Limited (LG Corporation)
• Inventors: Plett, Gregory
• Granted Patent: US 7,400,115 B2
• Time in Patent Office: Days
• US Class Current: 320/132
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables

Abstract

A system, a method, and an article of manufacture for determining an estimated combined battery state-parameter vector are provided. The method determines the estimated combined battery state-parameter vector based on a plurality of predicted the combined battery state-parameter vectors, a plurality of predicted battery output vectors, and a battery output vector.

Claims

1. A method for determining an estimated combined battery state-parameter vector indicative of both a state and a parameter of a battery at a first predetermined time, the method comprising:

determining a first plurality of estimated augmented combined battery state-parameter vectors that are indicative of both a state and a parameter of the battery, a battery input noise, a sensor noise associated with a sensor measuring a battery output variable, an uncertainty of both the state and parameter of the battery, an uncertainty of the battery input noise, and an uncertainty of the sensor noise, at a second predetermined time prior to the first predetermined time;

determining a second plurality of predicted combined battery state-parameter vectors that are indicative of both the state and the parameter of the battery and an uncertainty of the both state and parameter of the battery at the first predetermined time based on the first plurality of estimated augmented combined battery state-parameter vectors;

determining a third plurality of predicted battery output vectors that are indicative of at least one output variable of the battery and an uncertainty of the output variable at the first predetermined time based on the first plurality of estimated augmented combined battery state-parameter vectors and the second plurality of predicted combined battery state-parameter vectors;

determining a first battery output vector having at least one measured value of the battery output variable; and

determining a first estimated combined battery state-parameter vector indicative of both the state and the parameter of the battery at the first predetermined time based on the second plurality of predicted combined battery state-parameter vectors, the third plurality of predicted battery output vectors, and the first battery output vector.

2. The method of claim 1, wherein determining the first plurality of estimated augmented combined battery state-parameter vectors comprises:

retrieving an estimated combined battery state-parameter vector indicative of both the state and the parameter of the battery at the second predetermined time from a memory;

determining a first estimated augmented combined battery state-parameter vector indicative of both the state and the parameter of the battery, the battery input noise, and the sensor noise at the second predetermined time, based on the estimated combined battery state-parameter vector, an expected combined battery state-parameter input noise vector, and an expected sensor noise vector;

retrieving an estimated combined battery state-parameter vector covariance matrix indicative of an uncertainty of both the state and parameter of the battery at the second predetermined time, from the memory;

determining a first estimated augmented combined battery state-parameter vector covariance matrix indicative based on the estimated combined battery state-parameter vector covariance matrix, a covariance matrix indicative of an uncertainty of the battery input noise and a covariance matrix indicative of an uncertainty of the sensor noise; and

determining the first plurality of estimated augmented combined battery state-parameter vectors based on the first estimated augmented combined battery state-parameter vector and the first estimated augmented combined battery state-parameter vector covariance matrix.

3. The method of claim 2, wherein determining the first plurality of estimated augmented combined battery state-parameter vectors comprises:

setting one member of the first plurality of estimated augmented combined battery state-parameter vectors equal to the first estimated augmented combined battery state-parameter vector;

setting a first additional set of L members of the first plurality of estimated augmented combined battery state-parameter vectors equal to the first estimated augmented combined battery state-parameter vector added to a constant value multiplied by respective columns extracted from a matrix square-root of the first estimated augmented combined battery state-parameter vector covariance matrix, where L is the length of the first estimated augmented combined battery state-parameter vector; and

setting a second additional set of L members of the first plurality of estimated augmented combined battery state-parameter vectors equal to the constant value multiplied by respective columns extracted from the matrix square-root of the first estimated augmented combined battery state-parameter vector covariance matrix subtracted from the first estimated augmented combined battery state-parameter vector.

4. The method of claim 1, wherein determining the first plurality of estimated augmented combined battery state-parameter vectors comprises:

retrieving an estimated combined battery state-parameter vector indicative of both the state and the parameter of the battery at the second predetermined time from a memory;

determining a first estimated augmented combined battery state-parameter vector indicative of both the state and the parameter of the battery, the battery input noise, and the sensor noise at the second predetermined time based on the estimated combined battery state-parameter vector, an expected combined battery state-parameter input noise vector, and an expected sensor noise vector;

retrieving an estimated combined battery state-parameter vector square-root covariance matrix indicative of an uncertainty of both the state and the parameter of the battery at the second predetermined time from the memory;

determining a first estimated augmented combined battery state-parameter vector square-root covariance matrix indicative of an uncertainty of both the state and the parameter of the battery, the battery input noise, and the sensor noise, based on the estimated combined battery state-parameter vector square-root covariance matrix, a covariance matrix indicative of an uncertainty of the battery input noise and a covariance matrix indicative of an uncertainty of the sensor noise; and

determining the first plurality of estimated augmented combined battery state-parameter vectors based on the first estimated augmented combined battery state-parameter vector and the first estimated augmented combined battery state-parameter vector square-root covariance matrix.

5. The method of claim 4, wherein determining the first plurality of estimated augmented combined battery state-parameter vectors comprises:

setting one member of the first plurality of estimated augmented combined battery state-parameter vectors equal to the first estimated augmented combined battery state-parameter vector;

setting a first additional set of L members of the first plurality of estimated augmented combined battery state-parameter vectors equal to the first estimated augmented combined battery state-parameter vector added to a constant value multiplied by respective columns extracted from the estimated augmented combined battery state-parameter vector square-root covariance matrix, where L is the length of the estimated augmented combined battery state-parameter vector; and

setting a second additional set of L members of the first plurality of estimated augmented combined battery state-parameter vectors equal to the constant value multiplied by respective columns extracted from the first estimated augmented combined battery state-parameter vector square-root covariance matrix subtracted from the first estimated augmented combined battery state-parameter vector.

6. The method of claim 1, wherein determining the second plurality of predicted combined battery state-parameter vectors comprises:

extracting values indicative of both the state and the parameter of the battery and of an uncertainty of both the state and the parameter of the battery at the second predetermined time from the first plurality of estimated augmented combined battery state-parameter vectors, to obtain a fourth plurality of estimated combined battery state-parameter vectors;

extracting values indicative of the battery input noise and an uncertainty of the battery input noise at the second predetermined time from the first plurality of estimated augmented combined battery state-parameter vectors, to obtain a fifth plurality of estimated combined battery state-parameter input noise vectors;

generating the first battery input vector having at least one measured value of a battery input variable at the second predetermined time; and

determining the second plurality of predicted combined battery state-parameter vectors based on the fourth plurality of predicted combined battery state-parameter vectors, the fifth plurality of predicted combined battery state-parameter input noise vectors, and the first battery input vector.

7. The method of claim 1, wherein determining the third plurality of predicted battery output vectors comprises:

extracting values indicative of the sensor noise and of an uncertainty of the sensor noise at the second predetermined time from the first plurality of estimated augmented combined battery state-parameter vectors, to obtain a fourth plurality of estimated sensor noise vectors;

generating the first battery input vector having at least one measured value of a battery input variable at the first predetermined time; and

determining the third plurality of predicted battery output vectors based on the second plurality of predicted combined battery state-parameter vectors, the first battery input vector, and the fourth plurality of estimated sensor noise vectors.

8. The method of claim 1, wherein determining the first estimated combined battery state-parameter vector comprises:

determining a first predicted combined battery state-parameter vector indicative of both the state and the parameter of the battery at the first predetermined time based on the second plurality of predicted combined battery state-parameter vectors;

determining a first predicted battery output vector indicative of at least one output variable of the battery at the first predetermined time based on the third plurality of predicted output vectors;

determining a gain matrix based on the first predicted combined battery state-parameter vector, the first predicted battery output vector, the second plurality of predicted combined battery state-parameter vectors, and the third plurality of predicted battery output vectors; and

calculating the first estimated combined battery state-parameter vector based on the first predicted combined battery state-parameter vector, the first predicted battery output vector, the gain matrix, and the first battery output vector.

9. The method of claim 8, wherein determining the first predicted combined battery state-parameter vector comprises calculating a weighted average of the second plurality of predicted combined battery state-parameter vectors.

10. The method of claim 8, wherein determining the first predicted battery output vector comprises calculating a weighted average of the third plurality of predicted battery output vectors.

11. The method of claim 8, wherein determining the gain matrix comprises:

determining a first predicted battery output vector covariance matrix based on the first predicted battery output vector and the third plurality of predicted battery output vectors;

determining a first predicted cross-covariance matrix between a battery output vector and a combined battery state-parameter vector based on the first predicted battery output vector, the third plurality of predicted battery output vectors, the first predicted combined battery state-parameter vector, and the second plurality of predicted combined battery state-parameter vectors; and

calculating the gain matrix based on the first predicted cross-covariance matrix and the first predicted battery output vector covariance matrix.

12. The method of claim 11, wherein determining the first predicted battery output vector covariance matrix comprises:

determining a first predicted battery output vector square-root covariance matrix based on the first predicted battery output vector and the third plurality of predicted battery output vectors; and

calculating the first predicted battery output vector covariance matrix based on the first predicted battery output vector square-root covariance matrix and the first predicted battery output vector square-root covariance matrix.

13. The method of claim 8, wherein calculating the first estimated combined battery state-parameter vector comprises:

determining an innovation vector based on the first battery output vector and the first predicted battery output vector;

determining an update vector based on the gain matrix and the innovation vector; and

calculating the first estimated combined battery state-parameter vector based on the predicted combined battery state-parameter vector and the update vector.

14. The method of claim 8, further comprising determining an estimated combined battery state-parameter vector covariance matrix at the first predetermined time based on the first predicted combined battery state-parameter vector, the second plurality of predicted combined battery state-parameter vectors, the gain matrix, and the first predicted battery output vector covariance matrix.

15. The method of claim 8, further comprising determining an estimated combined battery state-parameter vector square-root covariance matrix at the first predetermined time based on the first predicted combined battery state-parameter vector, the second plurality of predicted combined battery state-parameter vectors, the gain matrix, and a predicted battery output vector square-root covariance matrix.

16. A system for determining an estimated combined battery state-parameter vector indicative of both a state and a parameter of a battery at a first predetermined time, the system comprising:

a sensor configured to generate a first signal indicative of an output variable of the battery; and

a computer operably coupled to the sensor, the computer configured to determine a first plurality of estimated augmented combined battery state-parameter vectors that are indicative of both a state and a parameter of the battery, a battery input noise, a sensor noise associated with the sensor, an uncertainty of both the state and parameter of the battery, an uncertainty of the battery input noise, and an uncertainty of the sensor noise, at a second predetermined time prior to the first predetermined time, the computer further configured to determine a second plurality of predicted combined battery state-parameter vectors that are indicative of both the state and the parameter of the battery and an uncertainty of both the state and parameter of the battery at the first predetermined time based on the first plurality of estimated augmented combined battery state-parameter vectors, the computer further configured to determine a third plurality of predicted battery output vectors that are indicative of an at least one output variable of the battery and an uncertainty of the output variable at the first predetermined time based on the first plurality of estimated augmented combined battery state-parameter vectors and the second plurality of predicted combined battery state-parameter vectors, the computer further configured to determine a first battery output vector based on the first signal, the computer further configured to determine a first estimated combined battery state-parameter vector indicative of both the state and the parameter of the battery at the first predetermined time based on the second plurality of predicted combined battery state-parameter vectors, the third plurality of predicted battery output vectors, and the first battery output vector.

17. The system of claim 16, wherein the computer is further configured to retrieve an estimated combined battery state-parameter vector indicative of both the state and the parameter of the battery at the second predetermined time from a memory, the computer further configured to determine a first estimated augmented combined battery state-parameter vector indicative of both the state and the parameter of the battery, the battery input noise and the sensor noise at the second predetermined time, based on the estimated combined battery state-parameter vector, an expected combined battery state-parameter input noise vector, and an expected sensor noise vector, the computer further configured to retrieve an estimated combined battery state-parameter vector covariance matrix indicative of an uncertainty of both the state and parameter of the battery at the second predetermined time from the memory, the computer further configured to determine a first estimated augmented combined battery state-parameter vector covariance matrix indicative of an uncertainty of both the state and the parameter of the battery, the battery input noise, and the sensor noise, based on the estimated combined battery state-parameter vector covariance matrix, a covariance matrix indicative of an uncertainty of the battery input noise and a covariance matrix indicative of an uncertainty of the sensor noise, the computer further configured to determine the first plurality of estimated augmented combined battery state-parameter vectors based on the first estimated augmented combined battery state-parameter vector and the first estimated augmented combined battery state-parameter vector covariance matrix.

18. The system of claim 16, wherein the computer is further configured to determine a first predicted combined battery state-parameter vector indicative of both the state and the parameter of the battery at the first predetermined time based on the second plurality of predicted combined battery state-parameter vectors, the computer further configured to determine a first predicted battery output vector indicative of at least one output variable of the battery at the first predetermined time based on the third plurality of predicted output vectors, the computer further configured to determine a gain matrix based on the first predicted combined battery state-parameter vector, the first predicted battery output vector, the second plurality of predicted combined battery state-parameter vectors, and the third plurality of predicted battery output vectors, the computer further configured to calculate the first estimated combined battery state-parameter vector based on the first predicted combined battery state-parameter vector, the first predicted battery output vector, the gain matrix, and the first battery output vector.

19. An article of manufacture, comprising:

a computer storage medium having a computer program encoded therein for determining an estimated combined battery state-parameter vector indicative of a state and a parameter of a battery at a first predetermined time, the computer storage medium comprising;

code for determining a first plurality of estimated augmented combined battery state-parameter vectors that are indicative of both a state and a parameter of the battery, a battery input noise, a sensor noise associated with a sensor measuring a battery output variable, an uncertainty of both the state and parameter of the battery, an uncertainty of the battery input noise, and an uncertainty of the sensor noise at a second predetermined time prior to the first predetermined time;

code for determining a second plurality of predicted combined battery state-parameter vectors that are indicative of the state and the parameter of the battery and an uncertainty of both the state and parameter of the battery at the first predetermined time based on the first plurality of estimated augmented combined battery state-parameter vectors;

code for determining a third plurality of predicted battery output vectors that are indicative of at least one output variable of the battery and of an uncertainty of the output variable at the first predetermined time based on the first plurality of estimated augmented combined battery state-parameter vectors and the second plurality of predicted combined battery state-parameter vectors;

code for determining a first battery output vector having at least one measured value of the battery output variable; and

code for determining a first estimated combined battery state-parameter vector indicative of both the state and the parameter of the battery at the first predetermined time based on the second plurality of predicted combined battery state-parameter vectors, the third plurality of predicted battery output vectors, and the first battery output vector.

20. The article of manufacture of claim 19, wherein the code for determining the first estimated combined battery state-parameter vector comprises:

code for determining a first predicted combined battery state-parameter vector indicative of both the state and the parameter of the battery at the first predetermined time based on the second plurality of predicted combined battery state-parameter vectors;

code for determining a first predicted battery output vector indicative of at least one output variable of the battery at the first predetermined time based on the third plurality of predicted output vectors;

code for determining a gain matrix based on the first predicted combined battery state-parameter vector, the first predicted battery output vector, the second plurality of predicted combined battery state-parameter vectors, and the third plurality of predicted battery output vectors; and

code for calculating the first estimated combined battery state-parameter vector based on the first predicted combined battery state-parameter vector, the first predicted battery output vector, the gain matrix, and the first battery output vector.