State variable and parameter estimator comprising several partial models for an electrical energy storage device

• US 7,518,339 B2
• Filed: 01/30/2004
• Issued: 04/14/2009
• Est. Priority Date: 01/30/2003
• Status: Active Grant

• Current Assignee: Robert Bosch GmbH
• Original Assignee: Robert Bosch GmbH
• Inventors: Schoch, Eberhard
• Primary Examiner(s): TSO, EDWARD H
• Application Number:
  US10/544,097
• Publication Number:
  US 20070035307A1
• Time in Patent Office: 1,901 Days
• Field of Search: 320/132, 320/149, 324/426, 324/430, 324/433, 340/635, 340/636.1, 340/636.11, 340/636.12
• 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: G01R 31/385 Arrangements for measuring battery or accumulator variables for monitoring G01R31/382

Abstract

A state variable and parameter estimator for determining state variables and parameters of a mathematical energy storage model, especially of a battery model, which calculates the state variables and the parameters from operating variables of an energy storage device. A particularly simple estimation of the state variables and the parameters may be carried out if the state variable and parameter estimator includes a plurality of mathematical submodels which are valid for different working ranges and/or frequency ranges of the energy storage device.

Claims

1. A method for determining state variables and parameters of a mathematical energy storage model by a state variable and parameter estimator, comprising:

causing the state variable and parameter estimator to calculate the state variables and the parameters of the mathematical energy storage model from operating variables of an energy storage device, wherein;

the state variable and parameter estimator includes a plurality of mathematical submodels that are validly applicable for at least one of different working ranges and different frequency ranges of the energy storage device, and

the state variables and parameters are used to perform one of energy management and user management of an electrical network.

2. The method as recited in claim 1, wherein:

the mathematical model is of a battery model.

3. The method as recited in claim 1, further comprising:

supplying one of a current and a voltage of the energy storage device to the submodels; and

restricting one of a current and a voltage restricted by a filter to the frequency range valid for at least one of the submodels.

4. The method as recited in claim 1, further comprising:

ascertaining an error between an operating variable of the energy storage device and an operating variable calculated by one of the submodels; and

feeding back the error into the respective submodel.

5. The method as recited in claim 4, further comprising:

feeding back the error into another one of the submodels.

6. The method as recited in claim 4, further comprising:

weighing the error in accordance with a factor.

7. The method as recited in claim 1, further comprising:

supplying one of a state variable calculated by one of the submodels and a calculated parameter to another of the submodels.

8. The method as recited in claim 7, further comprising:

feeding back one of the state variable and the calculated parameter in a weighted state.

9. The method as recited in claim 1, further comprising:

providing a stimulator in order to bring one of a current and a voltage that will be supplied to the submodels into one of a working range and a frequency range valid for the submodels.

21. The method as recited in claim 1, further comprising:

supplying one of a current and a voltage of the energy storage device to the submodels; and

restricting one of a current and a voltage restricted by a filter to the frequency range valid for at least one of the submodels;

wherein the mathematical model is of a battery model.

22. The method as recited in claim 21, further comprising:

ascertaining an error between an operating variable of the energy storage device and an operating variable calculated by one of the submodels;

feeding back the error into the respective submodel; and

feeding back the error into another one of the submodels.

23. The method as recited in claim 22, further comprising:

weighing the error in accordance with a factor; and

supplying one of a state variable calculated by one of the submodels and a calculated parameter to another of the submodels.

24. The method as recited in claim 23, further comprising:

feeding back one of the state variable and the calculated parameter in a weighted state.

25. The method as recited in claim 21, further comprising:

providing a stimulator in order to bring one of a current and a voltage that will be supplied to the submodels into one of a working range and a frequency range valid for the submodels.

10. A state variable and parameter estimator for determining state variables and parameters of a mathematical energy storage model, comprising:

an arrangement for calculating the state variables and the parameters from operating variables of an energy storage device; and

an arrangement for storing a plurality of submodels that are valid for at least one of different working ranges and different frequency ranges of the energy storage device.

11. The state variable and parameter estimator as recited in claim 10, wherein:

the mathematical model is of a battery model.

12. The state variable and parameter estimator as recited in claim 10, further comprising:

a filter connected in incoming circuit with at least one of the submodels in order to restrict the operating variables supplied to the submodels to the frequency range that is valid for one of the submodels.

13. The state variable and parameter estimator as recited in claim 10, further comprising:

an arrangement for ascertaining an error between an operating variable of the energy storage device and an operating variable calculated by one of the submodels; and

an arrangement for feeding back the error into the respective submodel.

14. The state variable and parameter estimator as recited in claim 13, further comprising:

an arrangement for feeding back the error into another submodel.

15. The state variable and parameter estimator as recited in claim 13, further comprising:

an arrangement for weighting the error that is fed back.

16. The state variable and parameter estimator as recited in claim 10, further comprising:

a stimulator for bringing one of a current curve and a voltage curve to be supplied to the submodels into one of a working range and a frequency range valid for the submodels.

17. The state variable and parameter estimator as recited in claim 10, further comprising:

a filter connected in incoming circuit with at least one of the submodels in order to restrict the operating variables supplied to the submodels to the frequency range that is valid for one of the submodels;

wherein the mathematical model is of a battery model.

18. The state variable and parameter estimator as recited in claim 17, further comprising:

an arrangement for ascertaining an error between an operating variable of the energy storage device and an operating variable calculated by one of the submodels; and

an arrangement for feeding back the error into the respective submodel.

19. The state variable and parameter estimator as recited in claim 18, further comprising:

an arrangement for feeding back the error into another submodel; and

an arrangement for weighting the error that is fed back.

20. The state variable and parameter estimator as recited in claim 17, further comprising:

a stimulator for bringing one of a current curve and a voltage curve to be supplied to the submodels into one of a working range and a frequency range valid for the submodels.