State of charge estimator and methods of making and using the same

Patent metadata

Field	Value
Publication number	US-10288693-B2
Application number	US-201414257572-A
Country	US
Kind code	B2
Filing date	Apr 21, 2014
Priority date	Apr 21, 2014
Publication date	May 14, 2019
Grant date	May 14, 2019

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.

First claim

What is claimed is:

1. A method comprising:
determining electrochemical impedance spectroscopy data of a battery using an analyzer;
providing a vehicle including a vehicle battery;
providing a controller constructed and arranged to receive input from a battery state estimator having a linear RC circuit;
estimating a state of charge of the vehicle battery using the battery state estimator, wherein the linear RC circuit includes a number of components and operates in a manner approximating the electrochemical impedance spectroscopy data of the battery from the analyzer, wherein a number of values for the number of components included in the linear RC circuit are derived from a fractional polynomial, in jω, representative of the electrochemical impedance spectroscopy data;
processing the runtime values of that linear RC circuit using a real-time linear regression algorithm provided in the battery state estimator to produce an estimated future behavior of the vehicle battery; and,
using the controller to send a signal to a vehicle component based upon the estimated future behavior of the vehicle battery.

2. A method as set forth in claim 1 wherein the linear 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 a state of charge of an energy storage device using a state of charge estimator including a linear RC circuit;
measuring electrochemical impedance spectroscopy data of the energy storage device using an analyzer;
determining a number of values for the number of components included in the linear RC circuit according to a derivation process wherein a fractional polynomial in jω, representative of the impedance spectroscopy data is determined, a time-domain expression that is linear with regard to the number of values for the number of circuit components is obtained by transforming the fractional polynomial, and the values for the number of circuit components are derived from the time-domain expression;
building the linear RC circuit based at least upon the derivation process;
processing the runtime values of the linear circuit using a real-time linear regression algorithm in the state of charge estimator to estimate future behavior of the energy storage device;
providing a controller constructed and arranged to receive input from the state of charge estimator;
comparing the input from the state of charge estimator with predetermined values; and,
using the controller to send a signal to a vehicle component when the input from the state of charge estimator is within a predetermined range of the predetermined values.

7. A method as set forth in claim 6 wherein the linear 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 uses a weighted recursive least squares (WRLS) filter.

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

11. A method as set forth in claim 6 wherein sending a signal to the vehicle component comprises sending a signal to a fuel burning engine of a vehicle including the energy storage device to prevent the engine from turning off.

12. A method as set forth in claim 6 wherein the sending a signal the vehicle component comprises sending a signal representative of the state of charge of the energy storage device to the vehicle component.

13. A method as set forth in claim 6 wherein the energy storage device is a battery.

14. A method as set forth in claim 6 wherein the energy storage device is a supercapacitor.

Assignees

• Gm Global Tech Operations Llc

Inventors

• Baker Daniel R
• Verbrugge Mark W
• Frost Patrick
• Koch Brian J
• Wampler Ii Charles W
• Laskowsky Patricia M

Classifications

• G01R31/3835

  involving only voltage measurements · CPC title

• G01R31/367

  Software therefor, e.g. for battery testing using modelling or look-up tables · CPC title

• G01R31/389 Primary

  Measuring internal impedance, internal conductance or related variables · CPC title

• G01R31/3662 Primary

  Physics · mapped topic

• G01R31/3651

  Physics · mapped topic