Method and apparatus for identifying abnormal battery cell, electronic device, and storage medium

Patent metadata

Field	Value
Publication number	US-2024255581-A1
Application number	US-202418595117-A
Country	US
Kind code	A1
Filing date	Mar 4, 2024
Priority date	Apr 19, 2022
Publication date	Aug 1, 2024
Grant date	—

Abstract

A method for identifying abnormal battery cell includes determining, based on target feature data of a battery cell, whether the battery cell is an abnormal battery cell. The target feature data includes feature data used for differentiation between an abnormal battery cell and a normal battery cell generated during a formation process.

First claim

What is claimed is:

1 . A method for identifying abnormal battery cell, comprising:
determining, based on target feature data of a battery cell, whether the battery cell is an abnormal battery cell; wherein the target feature data comprises feature data used for differentiation between an abnormal battery cell and a normal battery cell generated during a formation process.

2 . The method for identifying abnormal battery cell according to claim 1 , wherein the target feature data comprises feature data influenced by electrolyte in the battery cell generated during the formation process.

3 . The method for identifying abnormal battery cell according to claim 2 , wherein the target feature data comprises:
a first target parameter value corresponding to the first arrival of a parameter variation at a first process variation and a second target parameter value corresponding to the first arrival of the parameter variation at a second process variation, during a process of collecting parameters in the first stage of the formation process; wherein the first process variation and the second process variation are different.

4 . The method for identifying abnormal battery cell according to claim 1 , wherein the target feature data comprises feature data influenced by water content in the battery cell generated during the formation process.

5 . The method for identifying abnormal battery cell according to claim 4 , wherein the target feature data comprises:
a first target parameter value corresponding to the first arrival of a parameter variation at a first process variation and a second target parameter value corresponding to the first arrival of the parameter variation at a second process variation, during a process of collecting parameters in the first stage of the formation process; wherein the first process variation and the second process variation are different.

6 . The method for identifying abnormal battery cell according to claim 1 , wherein determining, based on the target feature data of the battery cell, whether the battery cell is an abnormal battery cell comprises:
inputting the target feature data into a preset identification model to obtain an identification result of whether the battery cell is an abnormal battery cell.

7 . The method for identifying abnormal battery cell according to claim 6 , wherein the identification model is a two-dimensional Gaussian model.

8 . The method for identifying abnormal battery cell according to claim 7 , wherein inputting the target feature data into the preset identification model to obtain the identification result of whether the battery cell is an abnormal battery cell comprises:
inputting the target feature data into the two-dimensional Gaussian model to obtain a probability density of the battery cell calculated by the two-dimensional Gaussian model; and under a condition that the probability density of the battery cell is less than a preset probability density threshold, determining that the battery cell is an abnormal battery cell.

9 . The method for identifying abnormal battery cell according to claim 7 , wherein inputting the target feature data into the preset identification model to obtain the identification result of whether the battery cell is an abnormal battery cell comprises:
inputting the target feature data into the two-dimensional Gaussian model to obtain a probability density of the battery cell calculated by the two-dimensional Gaussian model; and under a condition that the probability density of the battery cell is less than a preset probability density threshold and that the target feature data is within a preset feature range, determining that the battery cell is an abnormal battery cell.

10 . The method for identifying abnormal battery cell according to claim 1 , wherein determining, based on the target feature data of the battery cell, whether the battery cell is an abnormal battery cell comprises:
determining, during the formation process of the battery cell based on the target feature data of the battery cell, whether the battery cell is an abnormal battery cell.

11 . An electronic device, comprising a processor and a memory; wherein
the processor is configured to execute one or more instructions stored in the memory to implement the method for identifying abnormal battery cell according to claim 1 .

12 . A non-transitory computer-readable storage medium storing one or more instructions, the instructions being executable by a processor to implement the method for identifying abnormal battery cell according to claim 1 .

13 . An apparatus for identifying battery cell with electrolyte abnormalities, comprising:
an identification module, configured to determine, based on target feature data of a battery cell, whether the battery cell is an abnormal battery cell; wherein the target feature data comprises feature data used for differentiation between an abnormal battery cell and a normal battery cell generated during a formation process.

Assignees

• Contemporary Amperex Technology Co Ltd

Inventors

• Fan Xiaoyun
• Sun Jianan
• Lin Deming
• Wu Jingliang

Classifications

• G01R31/392

  Determining battery ageing or deterioration, e.g. state of health · CPC title

• G01R31/385

  Arrangements for measuring battery or accumulator variables (for monitoring G01R31/382) · CPC title

• H01M10/4285 Primary

  Testing apparatus · CPC title

• H01M10/04

  Construction or manufacture in general (H01M10/058, H01M10/12, H01M10/28, H01M10/38 take precedence) · CPC title

• G01R31/367

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