Method and apparatus for estimating state of battery

Patent metadata

Field	Value
Publication number	US-10101406-B2
Application number	US-201514794190-A
Country	US
Kind code	B2
Filing date	Jul 8, 2015
Priority date	Dec 4, 2014
Publication date	Oct 16, 2018
Grant date	Oct 16, 2018

Abstract

A method and apparatus for estimating a state of a battery are provided. A battery life estimation apparatus includes a time information accumulator configured to partition sensing data of a battery into sections, and to accumulate time information corresponding to the sections. The battery life estimation apparatus also includes a time information extractor configured to extract time information corresponding to a period from the accumulated time information. The battery life estimation apparatus further includes a life estimator configured to extract expected time information based on the accumulated time information, the time information corresponding to the period, and learning information, and configured to estimate an end of life (EOL) of the battery based on the expected time information.

First claim

What is claimed is:

1. A battery life estimation apparatus, comprising:
a time information accumulator configured to partition sensing data of a battery into sections, and to accumulate time information corresponding to the sections;
a time information extractor configured to extract time information corresponding to at least one period of plural periods from the accumulated time information; and
a life estimator configured to:
predict a change in the accumulated time information due to battery use;
generate expected time information, corresponding to the predicted change in the accumulated time information, based on the accumulated time information, the extracted time information corresponding to the at least one period, and learning information, the learning information being determined from training data; and
estimate an end of life (EOL) of the battery based on the generated expected time information.

2. The battery life estimation apparatus of claim 1 , wherein the time information accumulator is further configured to measure at least one of a voltage signal, a current signal, and a temperature signal of the battery in real time.

3. The battery life estimation apparatus of claim 1 , wherein the time information extractor is further configured to set the at least one period based on a usage history of the battery.

4. The battery life estimation apparatus of claim 1 , further comprising:
a charge/discharge determiner configured to determine, based on a current signal of the battery, whether the battery is being charged or discharged.

5. The battery life estimation apparatus of claim 1 , wherein the life estimator is further configured to add the time information corresponding to each of the plural periods to the accumulated time information in an order of a longest period to a shortest period among the plural periods, and to extract the expected time information for the generating.

6. The battery life estimation apparatus of claim 1 , wherein in response to pieces of time information corresponding to each period, the life estimator is further configured to extract the expected time information, based on time information corresponding to sensed data among the pieces of time information for the generating.

7. The battery life estimation apparatus of claim 5 , wherein the life estimator is further configured to set the accumulated time information as the expected time information, and to update the expected time information by adding the time information corresponding to each of the plural periods to the expected time information.

8. The battery life estimation apparatus of claim 7 , wherein the life estimator is further configured to estimate a life of the battery based on the updated expected time information and the learning information, and configured to update, for each of the plural periods, the expected time information by adding the time information corresponding to each of the plural periods to the expected time information until the estimated life of the battery is a predetermined end of the life of the battery.

9. The battery life estimation apparatus of claim 8 , wherein the life estimator is further configured to add time information corresponding to an n-th period to the expected time information, to which time information corresponding to each of a first period, as the longest period, through an (n−1)-th period is added until the estimated life of the battery is the predetermined end of the life of the battery.

10. The battery life estimation apparatus of claim 5 , wherein the life estimator is further configured to estimate the EOL by adding up the plural periods of time corresponding to the time information added to the accumulated time information.

11. The battery life estimation apparatus of claim 8 , wherein the life estimator is further configured to estimate a capacity of the battery based on the updated expected time information and the learning information, and configured to estimate the life of the battery based on the capacity of the battery.

12. The battery life estimation apparatus of claim 11 , wherein the life estimator is further configured to determine the estimated end of the life of the battery meets the predetermined end of the life of the battery, in response to the estimated capacity of the battery being less than 0.8 times an initial capacity of the battery.

13. The battery life estimation apparatus of claim 11 , further comprising:
a dimension transformer configured to transform an input vector corresponding to the expected time information to reduce a dimension of the input vector,
wherein the life estimator is further configured to estimate the capacity of the battery based on the transformed input vector and the learning information.

14. The battery life estimation apparatus of claim 13 , wherein the life estimator is further configured to estimate the capacity of the battery from the longest period to the shortest period by transmitting the transformed input vector and the learning information to a learner.

15. The battery life estimation apparatus of claim 14 , wherein the learner comprises one of a neural network, a hidden Markov model (HMM), a Bayesian network, a support vector machine (SVM), and a decision tree (DT).

16. The battery life estimation apparatus of claim 1 , wherein the life estimator is further configured to receive the learning information from an external apparatus using a communication interface.

17. A battery life estimation apparatus, comprising:
a training time information accumulator configured to partition training data of a battery into sections, and to accumulate training time information corresponding to the sections;
a learning information determiner configured to determine learning information, based on the accumulated training time information and predetermined reference information of the battery, to estimate an end of life (EOL) of the battery;
a time information accumulator configured to partition sensing data of the battery into sections, and to accumulate time information corresponding to the sections;
a time information extractor configured to extract time information corresponding to at least one period of plural periods from the accumulated time information; and
a life estimator configured to generate expected time information by predicting a change in the accumulated time information due to battery use, based on the accumulated time information, the extracted time information corresponding to the at least one period, and the learning information, and configured to estimate the EOL based on the generated expected time information.

18. The battery life estimation apparatus of claim 17 , further comprising:
a training data acquirer configured to acquire the training data of the battery and to measure at least one of a voltage signal, a current signal, and a temperature signal of the battery in real time.

19. The battery life estimation apparatus of claim 17 , wherein the learning information determiner is further configured to learn a parameter corresponding to the learning information by transmitting the accumulated time information and the reference information to a learner.

20. The battery life estimation apparatus of claim 17 , wherein the learning information determiner is further configured to transmit the learning information to an external apparatus using a communication interface.

21. A processor-implemented method of estimating a life of a battery, the processor-implemented method comprising:
accumu

Assignees

• Samsung Electronics Co Ltd

Inventors

• Park Sangdo

Classifications

• G01R31/3679 Primary

  Physics · mapped topic

• G01R31/3648

  comprising digital calculation means, e.g. for performing an algorithm · CPC title

• B60L58/16

  responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH] · CPC title

• H01M10/486

  for measuring temperature · CPC title

• G01R31/3842

  combining voltage and current measurements · CPC title