Apparatus and method for diagnosing battery, and computer program

Patent metadata

Field	Value
Publication number	US-2025209586-A1
Application number	US-202418651385-A
Country	US
Kind code	A1
Filing date	Apr 30, 2024
Priority date	Dec 21, 2023
Publication date	Jun 26, 2025
Grant date	—

Abstract

An apparatus for diagnosing a battery, the apparatus including a processor; and a memory storing instructions that, when executed on the processor, cause the processor to perform: generating a plurality of cross-sectional images of the battery from a three-dimensional (3D) image of the battery; plotting a plurality of points according to pixel values of a cross-sectional image of the plurality of cross-sectional images on the cross-sectional image to generate plot data in which at least some of the points are rounded; generating a two-dimensional (2D) flat image, in which the 3D image of the battery is spread out, by linearizing the generated plot data; and diagnosing the battery based on the generated 2D flat image.

First claim

What is claimed is:

1 . An apparatus for diagnosing a battery, the apparatus comprising:
a processor; and a memory configured to store instructions that, when executed by the processor, cause the processor to: generate a two-dimensional (2D) flat image in which a three-dimensional (3D) image of the battery is spread out; and diagnose the battery by analyzing a pattern of pixel values on the 2D flat image and distinguishing between intrinsic noise reflected in the 2D flat image and an abnormality of the battery.

2 . The apparatus of claim 1 , wherein the processor is configured to generate a cross-sectional image of the battery from the 3D image of the battery, to plot a plurality of points according to pixel values of the generated cross-sectional image on the cross-sectional image to generate plot data in which at least some of the points are rounded, and to generate the 2D flat image by linearizing the generated plot data.

3 . The apparatus of claim 1 , wherein the processor is configured to divide the 2D flat image into a plurality of zones, and to distinguish between the intrinsic noise and the abnormality of the battery by identifying regularity of the pattern of the pixel values in the plurality of zones.

4 . The apparatus of claim 3 , wherein parts of the 2D flat image for first to N th zones, which are the plurality of zones, are respectively defined as first to N th flat images, and areas where peaks of pixel values of the first to N th flat images appear are respectively defined as first to N th peak areas (N is a natural number of 2 or more), and
wherein the processor is configured to distinguish between the intrinsic noise and the abnormality of the battery by comparing and analyzing the first to N th peak areas.

5 . The apparatus of claim 4 , wherein the processor is configured to identify the intrinsic noise by analyzing positions of the first to N th peak areas with first to fourth flat images bring aligned relative to a first axis direction.

6 . The apparatus of claim 5 , wherein the processor is configured to identify an area corresponding to an inlier from among the first to N th peak areas as the intrinsic noise based on an overlapping position thereof.

7 . The apparatus of claim 5 , wherein the processor is configured to identify an area corresponding to an outlier from among the first to N th peak areas as an abnormal area of the battery based on an overlapping position thereof.

8 . The apparatus of claim 4 , wherein the processor is configured to use an Mth peak area as a reference, and to identify a Kth peak area, which has a different position from the M th peak area, as an abnormal area of the battery (M and K being natural numbers less than or equal to N, M>K).

9 . The apparatus of claim 1 , wherein the 3D image of the battery comprises a computed tomography (CT) image, and the pixel values are greyscale intensities of the CT image.

10 . The apparatus of claim 1 , wherein the processor is configured to diagnose deformation of an internal structure of the battery based on the abnormality of the battery.

11 . A method of diagnosing a battery, the method comprising:
generating, by a processor, a two-dimensional (2D) flat image in which a three-dimensional (3D) image of the battery is spread out; and diagnosing, by the processor, the battery by analyzing a pattern of pixel values on the 2D flat image and distinguishing between intrinsic noise reflected in the 2D flat image and an abnormality of the battery.

12 . The method of claim 11 , wherein, in the generating of the 2D flat image, the processor is configured to generate a cross-sectional image of the battery from the 3D image of the battery, to plot a plurality of points according to pixel values of the generated cross-sectional image on the cross-sectional image to generate plot data in which at least some of the points are rounded, and to generate the 2D flat image by linearizing the generated plot data.

13 . The method of claim 11 , wherein, in the diagnosing of the battery, the processor is configured to divide the 2D flat image into a plurality of zones, and to distinguish between the intrinsic noise and the abnormality of the battery by identifying regularity of the pattern of the pixel values in the plurality of zones.

14 . The method of claim 13 , wherein parts of the 2D flat image for first to N th zones, which are the plurality of zones, are respectively defined as first to N th flat images and areas where peaks of pixel values of the first to N th flat images appear are respectively defined as first to N th peak areas (N being a natural number of 2 or more), and
wherein in the diagnosing of the battery, the processor is configured to distinguish between the intrinsic noise and the abnormality of the battery by comparing and analyzing the first to N th peak areas.

15 . The method of claim 14 , wherein, in the diagnosing of the battery, the processor is configured to identify the intrinsic noise by analyzing positions of the first to N th peak areas with first to fourth flat images being aligned relative to a first axis direction.

16 . The method of claim 15 , wherein, in the diagnosing of the battery, the processor is configured to identify an area corresponding to an inlier from among the first to N th peak areas as the intrinsic noise based on an overlapping position thereof.

17 . The method of claim 15 , wherein, in the diagnosing of the battery, the processor is configured to identify an area corresponding to an outlier from among the first to N th peak areas as an abnormal area of the battery based on an overlapping position thereof.

18 . The method of claim 14 , wherein, in the diagnosing of the battery, the processor is configured to use an M th peak area as a reference, and to identify a Kth peak area, which has a different position from the M th peak area, as an abnormal area of the battery (M and K being natural numbers less than or equal to N, M>K).

19 . The method of claim 11 , wherein, in the diagnosing of the battery, the processor is configured to diagnose deformation of an internal structure of the battery based on the abnormality of the battery.

20 . A computer program, which is coupled to hardware and stored in a computer-readable storage medium, for performing operations of:
generating a two-dimensional (2D) flat image in which a three-dimensional (3D) image of a battery is spread out; and diagnosing the battery by analyzing a pattern of pixel values on the 2D flat image and distinguishing between intrinsic noise reflected in the 2D flat image and abnormality of the battery.

Assignees

• Samsung Sdi Co Ltd
• Ulsan National Institute Of Science And Tech

Inventors

• Woo Seong Ho
• Lee Hyun Wook
• Kim Dae Sik
• Lee Sung Kook
• Kim Ji Young
• Lee Bo Ram
• Park Chang Hyun
• Jung Uk Hyun

Classifications

• G06T2207/10081

  Computed x-ray tomography [CT] · CPC title

• G01N2223/607

  strain · CPC title

• G01N2223/419

  computed tomograph · CPC title

• G01N2223/401

  image processing · CPC title

• H01M10/4285

  Testing apparatus · CPC title