Method and apparatus for estimating state of battery

Patent metadata

Field	Value
Publication number	US-2016018472-A1
Application number	US-201514802478-A
Country	US
Kind code	A1
Filing date	Jul 17, 2015
Priority date	Jul 18, 2014
Publication date	Jan 21, 2016
Grant date	—

Abstract

A battery state estimation apparatus includes a sensing data acquirer configured to acquire sensing data on a battery, and a battery state estimator configured to approximate the sensing data by mapping the sensing data to a predetermined feature space, and compare the approximated sensing data to predetermined reference information to estimate a state of the battery.

First claim

What is claimed is:

1 . A battery state estimation apparatus comprising:
a sensing data acquirer configured to acquire sensing data on a battery; and a battery state estimator configured to approximate the sensing data by mapping the sensing data to a predetermined feature space, and compare the approximated sensing data to predetermined reference information to estimate a state of the battery.

2 . The apparatus of claim 1 , wherein the battery state estimator comprises a sensing data segmenter configured to segment the sensing data based on a predetermined time interval, extract a segment having a predetermined size from each time interval, and generate a segment vector comprising the segment.

3 . The apparatus of claim 2 , wherein the battery state estimator further comprises a calibrator configured to correct a time error of the sensing data based on a predetermined calibration cycle as a reference.

4 . The apparatus of claim 2 , wherein the battery state estimator further comprises a feature space mapper configured to map the segment vector to the predetermined feature space based on a predetermined mapping parameter.

5 . The apparatus of claim 4 , wherein the predetermined mapping parameter comprises a predetermined reference matrix; and
the feature space mapper is further configured to project the segment vector onto the predetermined reference matrix to extract a feature vector having a dimension corresponding to the predetermined feature space.

6 . The apparatus of claim 5 , wherein the predetermined reference information comprises information on patterns for battery state types in the predetermined feature space; and
the battery state estimator further comprises a battery state determiner configured to determine the state of the battery by comparing the feature vector to the information on the patterns.

7 . The apparatus of claim 6 , wherein the battery state types comprise a normal state type, an abnormal state type, and a fault state type; and
each of the abnormal state type and the fault state type comprises at least one subtype.

8 . The apparatus of claim 6 , wherein the battery state determiner is further configured to calculate a similarity between the feature vector and each of the patterns in the predetermined feature space, and determine the state of the battery based on a comparison between the calculated similarities.

9 . A battery state estimation method comprising:
acquiring sensing data on a battery; approximating the sensing data by mapping the sensing data to a predetermined feature space; and comparing the approximated sensing data to predetermined reference information to estimate a state of the battery.

10 . The method of claim 9 , wherein the approximating comprises:
segmenting the sensing data based on a predetermined time interval; extracting a segment having a predetermined size from each time interval; and generating a segment vector comprising the segment.

11 . The method of claim 10 , wherein the approximating further comprises correcting a time error of the sensing data based on a predetermined calibration cycle as a reference.

12 . The method of claim 10 , wherein the approximating further comprises mapping the segment vector to the predetermined feature space based on a predetermined mapping parameter.

13 . The method of claim 12 , wherein the predetermined mapping parameter comprises a predetermined reference matrix; and
the mapping comprises projecting the segment vector onto the predetermined reference matrix to extract a feature vector having a dimension corresponding to the predetermined feature space.

14 . The method of claim 13 , wherein the predetermined reference information comprises information on patterns for battery state types in the predetermined feature space; and
approximating further comprises determining the state of the battery by comparing the feature vector to the information on the patterns.

15 . The method of claim 14 , wherein the battery state types comprise a normal state type, an abnormal state type, and a fault state type; and
each of the abnormal state type and the fault state type comprises at least one subtype.

16 . The method of claim 14 , wherein the determining comprises:
calculating a similarity between the feature vector and each of the patterns in the predetermined feature space; and determining the state of the battery based on a comparison between the calculated similarities.

17 . A non-transitory computer-readable storage medium storing instructions for causing computing hardware to perform the method of claim 9 .

Assignees

• Samsung Electronics Co Ltd

Inventors

• Sung Jaemo
• Park Jeonghyun

Classifications

• G01R31/382 Primary

  Arrangements for monitoring battery or accumulator variables, e.g. SoC · CPC title

• G01R31/367 Primary

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

• B60L58/10

  for monitoring or controlling batteries · CPC title

• G06F2218/12

  Classification; Matching · CPC title

• B60L58/15

  Preventing overcharging · CPC title