METHOD AND APPARATUS FOR AUTOMATICALLY ESTIMATING REMAINING USEFUL LIFE (RUL) OF BATTERY IN REAL TIME

• US 20160259014A1
• Filed: 03/03/2016
• Published: 09/08/2016
• Est. Priority Date: 03/03/2015
• Status: Active Grant

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: ADAGOUDA PATIL, Meru, SEETHALAKSHMY HARIHARAN, Krishnan
• Granted Patent: US 10,338,153 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 31/392 Determining battery ageing or deterioration, e.g. state of health

Abstract

A method of estimating a remaining useful life (RUL) of a battery includes: identifying a class of data of the battery in real time; determining whether a second level RUL estimation is set for the class; estimating a gross RUL by performing a first level RUL estimation in response to the second level RUL estimation not being set for the class; and estimating a fine RUL of the battery in response to the second level RUL estimation being set for the class.

Claims

1. A method of estimating a remaining useful life (RUL) of a battery, the method comprising:

identifying a class of data of the battery in real time;

determining whether a second level RUL estimation is set for the class;

estimating a gross RUL by performing a first level RUL estimation in response to the second level RUL estimation not being set for the class; and

estimating a fine RUL of the battery in response to the second level RUL estimation being set for the class.

2. The method of claim 1, wherein the class is pre-defined based on a number of at least one of charge, discharge, and impedance cycles of the battery.

3. The method of claim 1, wherein the identifying of the class comprises:

collecting at least one primary parameter;

generating at least one secondary parameter from the at least one primary parameter;

generating an optimized set of parameters based on the primary and secondary parameters;

generating a real-time artificial intelligence (AI) model specific to the data of the battery based on the optimized set of parameters;

comparing the real-time AI model with a reference AI model; and

identifying the class in the reference AI model having data matching data in the real-time AI model.

4. The method of claim 1, wherein the estimating of the gross RUL comprises measuring a rough estimate of the RUL of the battery.

5. The method of claim 1, wherein the estimating of the fine RUL comprises:

collecting battery-specific data identified as belonging to the class;

generating a regression model based on the collected battery-specific data;

comparing the regression model with a reference regression model representing an optimized data set that represents the class;

identifying data in the reference regression model matching data in the regression model; and

estimating an RUL representing the identified data in the reference regression model as the fine RUL.

6. The method of claim 1, further comprising:

displaying the estimated fine RUL in response to a difference between the fine RUL and an end of life (EOL) of the battery being less than or equal to a threshold value.

7. A system for estimating a remaining useful life (RUL) of a battery, the system comprising:

an RUL estimator; and

a non-volatile memory comprising instructions,

wherein the instructions are configured to cause the RUL estimator to;

identify a class of data of the battery in real time;

determine whether a second level RUL estimation is set for the class;

estimate a gross RUL by performing a first level RUL estimation in response to the second level RUL estimation not being set for the class; and

estimate a fine RUL of the battery in response to the second level RUL estimation being set for the class.

8. The system of claim 7, wherein the RUL estimator is configured to provide at least one option to pre-define the class based on a number of at least one of charge, discharge, and impedance cycles of the battery.

9. The system of claim 7, further comprising a classifier configured to:

collect at least one primary parameter using an input/output (I/O) interface;

generate at least one secondary parameter from the at least one primary parameter;

generate an optimized set of parameters based on the primary and secondary parameters;

generate a real-time artificial intelligence (AI) model specific to the data of the battery based on the optimized set of parameters;

compare the real-time AI model with a reference AI model; and

identify the class in the reference AI model having data matching data in the real-time AI model,

wherein the RUL estimator is configured to identify the class of the data of the battery using the classifier.

10. The system of claim 7, further comprising:

a regression analyzer configured to measure a rough estimate of the RUL,

wherein the RUL estimator is configured to estimate the gross RUL using the regression analyzer.

11. The system of claim 7, wherein, to estimate the fine RUL of the battery, the RUL estimator is configured to:

collect battery-specific data identified as belonging to the class;

generate a regression model based on the collected battery-specific data;

compare the regression model with a reference regression model representing an optimized data set that represents the class;

identify data in the reference regression model matching data in the regression model; and

estimate the fine RUL based on the second level RUL estimation by estimating an RUL representing the identified data in the reference regression model as the fine RUL.

12. The system of claim 7, further comprising:

a display configured to display the fine RUL in response to a difference between the fine RUL and an end of life (EOL) of the battery being less than or equal to a threshold value.

13. A remaining useful life (RUL) estimator, comprising:

a processor;

a classifier implemented by the processor and configured to identify a class of data of a battery in real time;

a state of health (SOH) monitor implemented by the processor and configured to determine whether a second level RUL estimation is set for the class;

a regression analyzer implemented by the process and configured to

estimate a gross RUL by performing a first level RUL estimation in response to the second level RUL estimation not being set for the class, and

estimate a fine RUL of the battery in response to the second level RUL estimation being set for the class.

14. The RUL estimator of claim 13, wherein the estimating of the gross RUL comprises measuring a rough estimate of the RUL of the battery.

15. The RUL estimator of claim 13, wherein the class is pre-defined based on a number of at least one of charge, discharge, and impedance cycles of the battery.

16. The RUL estimator of claim 13, wherein the RUL estimator is configured to trigger an alert in response to the estimated fine RUL being greater than an upper threshold value or less than a lower threshold value.