Method and system for operating a battery in a selected application

• US 8,190,384 B2
• Filed: 10/27/2011
• Issued: 05/29/2012
• Est. Priority Date: 10/27/2011
• Status: Active Grant

• Current Assignee: Sakti3, Inc
• Original Assignee: Sakti3, Inc
• Inventors: Zhang, Xiangchun, Chen, Yen-Hung, Wang, Chia-Wei, Sastry, Ann Marie
• Primary Examiner(s): KUNDU, SUJOY K
• Application Number:
  US13/283,524
• Publication Number:
  US 20120041698A1
• Time in Patent Office: 215 Days
• Field of Search: 702/57, 702/58, 702/60, 702 63- 65, 702/130, 702/136, 702/179, 702181-183, 702/185, 702/187, 702/190, 320/101, 320/127, 320/132, 320/134, 320/136, 320149-150
• US Class Current: 702/63
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables
• CPC Class Codes: G01R 31/3842 combining voltage and current measurements

Abstract

A method of the present invention using a prediction process including a battery equivalent circuit model used to predict a voltage and a state of charge of a battery. The equivalent circuit battery model includes different equivalent circuit models consisting of at least an ideal DC power source, internal resistance, and an arbitrary number of representative parallel resistors and capacitors. These parameters are obtained a priori by fitting the equivalent circuit model to battery testing data. The present invention further uses a correction process includes determining a corrected predicted state of charge of the battery; and storing the corrected state of charge of the battery in a storage medium. In the present invention, an expectation of the predicted voltage of the battery and an expectation of the predicted state of charge of the battery are obtained by an unscented transform with sigma points selected by a Gaussian process optimization.

Claims

1. A method for operating an application, the application is selected from one of a mobile communication device, a vehicle, display device, or computing device, the method including a process for monitoring and predicting a state of a battery device while the battery device is configured with the application, comprising:

detecting, by a processor, a current and a voltage of the battery device in the application using one or more sensing device coupled to an electronic circuit structure;

capturing, by a processor, a current value associated with the current and a voltage value associated with the voltage;

processing, by a processor, the current value and the voltage value;

determining, by a processor, a state of charge of the battery device using at least a prediction-correction process including the current value and the voltage value, an arbitrary number of representative resistors and capacitors associated with a battery equivalent circuit model, and an unscented transform;

storing, by a processor, the state of charge in one or more memories;

outputting, by a processor, the stating of charge; and

using the state of charge for the application;

whereupon the unscented filter transform comprises a Gaussian process for selection of more than two Sigma points.

2. The method of claim 1 wherein battery equivalent circuit models consist of at least an ideal DC power source, internal resistance, and an arbitrary number of representative parallel resistors and capacitors.

3. The method of claim 2 wherein said battery equivalent circuit model uses parameters for resistors and capacitors, the parameters being obtained a priori by fitting the equivalent circuit model to battery testing data, the parameters able to be obtained concurrently or jointly with state of charge of the battery cell.

4. The method of claim 1 wherein the prediction-correction process comprises:

predicting a value of battery state of charge;

predicting a value of battery voltage;

determining a difference between the measured voltage and a predicted voltage to obtain a voltage difference value;

multiplying the difference value by a gain factor to obtain a correction value;

adding the correction value to the predicted cell state of charge to obtain a corrected predicted state of charge of the battery cell; and

storing the corrected state of charge of the battery cell in a storage medium.

5. The method of claim 4 wherein said gain factor is determined by dividing the covariance of the stochastic variables of voltage and state of charge by the covariance of stochastic variable of voltage and voltage.

6. The method of claim 4 wherein said predicted voltage is the expectation of the predicted stochastic variable voltage, the expectation being obtained by an unscented transform.

7. The method of claim 1 wherein the electronic circuit structure comprises voltage sensing devices, current sensing devices, resistors, capacitors, electronic amplifier, analog-to-digital converters.

8. The method of claim 1 wherein the unscented transform comprises a priori or a Gaussian process optimization for selection of more than two Sigma points to approximate the expectation and covariance of stochastic variables such as state of charge and voltage of a battery.

9. The method of claim 1 wherein the outputting comprises a state of charge, a voltage, a current, and a corresponding time.

10. The method of claim 1 wherein the arbitrary number includes any positive integer and zero.

11. The method of claim 1 wherein said unscented transform based prediction-correction filter calculates the innovation covariance for parameter estimation using a fixed forgetting factor or an exponentially annealed forgetting factor when the battery equivalent circuit model parameters are concurrently estimated with battery state of charge.

12. A method for operating an application, the application is selected from one of a mobile communication device, a vehicle, display device, or computing device, the method including a process for monitoring and predicting a state of a battery device while the battery device is configured with the application, comprising:

detecting, by a processor, a current and a voltage of the battery device in the application using one or more sensing device coupled to an electronic circuit structure;

capturing, by a processor, a current value associated with the current and a voltage value associated with the voltage;

processing, by a processor, the current value and the voltage value using an arbitrary number of representative resistors and capacitors associated with a battery equivalent circuit model and an unscented transform, the unscented filter transform comprising a Gaussian process for selection of more than two Sigma points;

predicting a value of battery state of charge;

predicting a value of battery voltage;

determining, by a processor, a difference between the measured voltage and a predicted voltage to obtain a voltage difference value;

multiplying the difference value by a gain factor to obtain a correction value;

adding the correction value to the predicted cell state of charge to obtain a corrected predicted state of charge of the battery cell;

storing, by a processor, the corrected state of charge of the battery cell in a storage medium;

outputting, by a processor, the stating of charge; and

using the state of charge for the application.

13. The method of claim 12 wherein said gain factor is determined by dividing the covariance of the stochastic variables of voltage and state of charge by the covariance of stochastic variable of voltage and voltage.

14. The method of claim 12 wherein said predicted voltage is the expectation of the predicted stochastic variable voltage, the expectation being obtained by an unscented transform.

15. The method of claim 12 wherein the electronic circuit structure comprises voltage sensing devices, current sensing devices, resistors, capacitors, electronic amplifier, analog-to-digital converters.

16. The method of claim 12 wherein the unscented transform comprises a priori or a Gaussian process optimization for selection of more than two Sigma points to approximate the expectation and covariance of stochastic variables such as state of charge and voltage of a battery.

17. The method of claim 12 wherein the outputting comprises a state of charge, a voltage, a current, and a corresponding time.

18. The method of claim 12 wherein said unscented transform based prediction-correction filter calculates the innovation covariance for parameter estimation using a fixed forgetting factor or an exponentially annealed forgetting factor when the battery equivalent circuit model parameters are concurrently estimated with battery state of charge.

19. A system for operating an application, the application is selected from one of a mobile communication device, a vehicle, display device, or computing device, the method including a process for monitoring and predicting a state of a battery device while the battery device is configured with the application, comprising:

at least one sensing device configured for detecting a current value and a voltage value of the battery device in the application;

an electronic circuit configured for capturing the current value associated with the current and the voltage value associated with the voltage; and

a processing unit coupled to the electronic circuit, the processing unit configured to execute one or more instructions, the one or more instructions including a process for

determining a state of charge of the battery device using at least a prediction-correction process including the current value and the voltage value, an arbitrary number of representative resistors and capacitors associated with a battery model, an unscented transform comprising a Gaussian process for selection of more than two Sigma points, and one or more memory device configured to store a value associated with the state of charge.

20. The system of claim 19 further comprising an output device to display the value associated with the state of charge.