STATE OF CHARGE ESTIMATOR AND METHODS OF MAKING AND USING THE SAME

• US 20150301116A1
• Filed: 04/21/2014
• Published: 10/22/2015
• Est. Priority Date: 04/21/2014
• Status: Active Application

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations LLC (General Motors Company)
• Inventors: Baker, Daniel R., Verbrugge, Mark W., Frost, Patrick, Koch, Brian J., Wampler, Charles W. II, Laskowsky, Patricia M.
• Granted Patent: US 10,288,693 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 31/389 Measuring internal impedance, internal conductance or related variables

Abstract

A number of variations include a method, which may include using at least a segment of voltage-based Battery State Estimation data, and using real-time linear regression, which may be a method of estimating future behavior of a system based on current and previous data points, to provide a robust and fast-adapting impedance response approximator. Linear regression may be performed by forming an RC circuit which is “equivalent” to electrochemical impedance spectroscopy data and processing the runtime values of that RC circuit using any number of known real-time linear regression algorithms including, but not limited, to a weighted recursive least squares (WRLS), Kalman filter or other means.

Claims

1. A method comprising:

estimating the state of charge of a battery using at least a segment of voltage-based battery state estimation data, and forming an RC circuit which operates in a manner approximating the electrochemical impedance spectroscopy data, and processing the runtime values of that RC circuit using a real-time linear regression algorithm.

2. A method as set forth in claim 1 wherein the RC circuit includes a resistor in series to N resistor-capacitor pairs.

3. A method as set forth in claim 2 wherein N is greater than 2.

4. A method as set forth in claim 1 wherein the algorithm uses a weighted recursive least squares (WRLS) filter.

5. A method as set forth in claim 1 wherein the algorithm uses a Kalman filter.

6. A method comprising;

estimating the state of charge of an energy storage device using a state of charge estimator including a RC circuit which is modeled based on electrochemical impedance spectroscopy data, and processing the runtime values of that RC circuit using a real-time linear regression.

7. A method as set forth in claim 6 wherein the RC circuit includes a resistor in series to N resistor-capacitor pairs.

8. A method as set forth in claim 7 wherein N is greater than 2.

9. A method as set forth in claim 6 wherein the algorithm using a weighted recursive least squares (WRLS) filter.

10. A method as set forth in claim 6 wherein the algorithm using a Kalman filter.

11. A method as set forth in claim 6 further comprising a controller constructed and arranged to receive input from the state of charge estimator, the controller comparing the input from the estimator with predetermined values and taking action when the input from the estimator is within a predetermined range of the predetermined values.

12. A method as set forth in claim 11 wherein the taking action comprises preventing a fuel burning engine of a vehicle including the energy storage device from turning off if the input from the estimate is within a predetermined range of the predetermined values.

13. A method as set forth in claim 11 wherein the taking action comprises sending a signal representative of the state of charge of the energy storage device.

14. A method as set forth in claim 11 wherein the energy storage device is a battery.

15. A method as set forth in claim 11 wherein the energy storage device is a supercapacitor.