CN-116448185-B - Method and device for detecting abnormality of battery core in shell, electronic equipment and storage medium

CN116448185BCN 116448185 BCN116448185 BCN 116448185BCN-116448185-B

Abstract

The embodiment of the application discloses an abnormality detection method, device, electronic equipment and storage medium for a battery cell in-shell, which comprise the steps of obtaining detection data in the process that a battery cell is pushed into a battery shell by a shell-in machine, generating one or more curves to be detected according to the detection data, respectively detecting the one or more curves to be detected according to historical detection data to obtain a detection result corresponding to each curve to be detected, and determining the abnormality result of the battery cell in-shell according to the detection result corresponding to each curve to be detected. By implementing the embodiment of the application, the accuracy of abnormal cell judgment can be improved, the judgment process does not need to be checked manually, the intelligent degree of the cell detection process is improved, the main abnormal problem in the production process can be effectively determined, the problem can be solved in a targeted manner, and the accuracy and the efficiency of the abnormal detection in the lithium cell shell entering process can be improved.

Inventors

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Assignees

无锡先导智能装备股份有限公司

Dates

Publication Date: 20260508
Application Date: 20230516

Claims (8)

1. The method for detecting the abnormality of the battery cell in the shell is characterized by comprising the following steps: Acquiring detection data in the process that the battery cell is pushed into the battery shell; Generating one or more curves to be detected according to the detection data; Determining abnormal data corresponding to each curve to be detected according to the detection data; detecting the one or more curves to be detected respectively according to historical detection data to obtain a detection result corresponding to each curve to be detected, wherein the historical detection data are detection data when no abnormality occurs in the process that a historical battery cell is pushed into a battery shell; determining abnormal results of the battery cells in the shell according to detection results corresponding to each curve to be detected; The abnormal data includes a katon value and a burr value, the one or more curves to be detected are detected according to the historical detection data, and a detection result corresponding to each curve to be detected is obtained, and the method includes: determining an abnormal data threshold according to the historical detection data, wherein the abnormal data threshold comprises a stuck threshold and a burr threshold; Comparing the stuck value corresponding to each curve to be detected with the stuck threshold value to obtain a stuck comparison result corresponding to each curve to be detected; comparing the burr value corresponding to each curve to be detected with the burr threshold value to obtain a burr comparison result corresponding to each curve to be detected; and obtaining a detection result corresponding to each curve to be detected according to the katon comparison result corresponding to each curve to be detected and the burr comparison result corresponding to each curve to be detected.
2. The method according to claim 1, wherein the detection data comprises a displacement value and/or a thrust value of the cell being pushed into a battery housing; the generating one or more determined curves to be detected according to the detection data comprises the following steps: And generating a curve to be detected corresponding to the thrust value according to the thrust value, and/or generating a curve to be detected corresponding to the displacement value according to the displacement value.
3. The method of claim 1, wherein the detecting the one or more curves to be detected according to the historical detection data to obtain a detection result corresponding to each curve to be detected includes: Generating a standard curve corresponding to each curve to be detected according to the historical detection data, and determining a slope threshold corresponding to each standard curve according to each standard curve; Determining the slope of each curve to be detected, and comparing the slope of each curve to be detected with a corresponding slope threshold value to obtain a slope comparison result of each curve to be detected; determining the similarity between each curve to be detected and the corresponding standard curve, and comparing each similarity with a similarity threshold value to obtain a similarity comparison result of each curve to be detected; And obtaining a detection result corresponding to each curve to be detected according to the slope comparison result and the similarity comparison result of each curve to be detected.
4. The method according to claim 1, wherein the method further comprises: determining a first number of the target to-be-detected curves, wherein the first number of the to-be-detected curves is larger than the threshold value of the to-be-detected curves, according to the corresponding to the to-be-detected curves; determining a second number of the target to-be-detected curves, wherein the burr value of the second number is larger than the burr threshold value, according to the burr comparison result corresponding to the target to-be-detected curves; determining an abnormal score corresponding to the target curve to be detected according to the first quantity, the weight of the katon comparison result, the second quantity and the weight of the burr comparison result, wherein the weight of the katon comparison result is greater than the weight of the burr comparison result; And determining the abnormal grade of the battery core in the shell according to the abnormal score corresponding to each curve to be detected.
5. The method according to any one of claims 1 to 4, wherein after the generating one or more curves to be detected from the detection data, the method further comprises: dividing each curve to be detected into a plurality of sections of sub-curves according to the process flow of the battery core in the shell, wherein the sections of sub-curves are in one-to-one correspondence with a plurality of sub-processes of the process flow of the battery core in the shell; the step of respectively detecting the one or more curves to be detected according to the historical detection data to obtain a detection result corresponding to each curve to be detected, comprising the following steps: And respectively detecting each sub-curve of each curve to be detected according to the historical detection data to obtain a detection result corresponding to each sub-curve of each curve to be detected, and determining the detection result corresponding to each curve to be detected according to the detection result corresponding to each sub-curve.
6. An abnormality detection device for a battery cell in a case, the device comprising: The data acquisition module is used for acquiring detection data cells in the process that the cells are pushed into the battery case; the curve construction module is used for generating one or more curves to be detected according to the detection data; The data detection module is used for determining abnormal data corresponding to each curve to be detected according to the detection data; The curve detection module is used for respectively detecting the one or more curves to be detected according to historical detection data to obtain a detection result corresponding to each curve to be detected, wherein the historical detection data is detection data cells when no abnormality occurs in the process that a historical cell is pushed into a battery shell; The abnormal detection module is used for determining an abnormal result of the battery core in the shell according to the detection result corresponding to each curve to be detected; wherein the abnormal data comprises a stuck value and a burr value; The curve detection module is further used for: determining an abnormal data threshold according to the historical detection data, wherein the abnormal data threshold comprises a stuck threshold and a burr threshold; Comparing the stuck value corresponding to each curve to be detected with the stuck threshold value to obtain a stuck comparison result corresponding to each curve to be detected; comparing the burr value corresponding to each curve to be detected with the burr threshold value to obtain a burr comparison result corresponding to each curve to be detected; and obtaining a detection result corresponding to each curve to be detected according to the katon comparison result corresponding to each curve to be detected and the burr comparison result corresponding to each curve to be detected.
7. An electronic device comprising a memory and a processor, the memory having stored therein a computer program which, when executed by the processor, causes the processor to implement the method of any of claims 1 to 5.
8. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any one of claims 1 to 5.

Description

Method and device for detecting abnormality of battery core in shell, electronic equipment and storage medium Technical Field The application relates to the technical field of industrial detection, in particular to a method and a device for detecting abnormality of a battery cell in a shell, electronic equipment and a storage medium. Background With the continuous development of industrial products, the requirements for quality inspection of the industrial products are also higher and higher. In the existing battery assembly process, the battery cells need to be installed into the battery shell through a shell-entering machine. Abnormal conditions such as blocking or burrs and the like can occur in the process of putting the battery core into the shell, and the abnormal conditions can cause the quality reduction of the battery core and influence the performance and the safety of a finished battery product. At present, the electrical core with abnormal thrust value is checked by monitoring the thrust value in the process of inserting the electrical core into the shell in a manual mode so as to determine whether the electrical core has the abnormality, and the abnormality detection method has poor accuracy and lower intelligent degree. Disclosure of Invention The embodiment of the application discloses a method and a device for detecting abnormality of a battery cell in a shell, electronic equipment and a storage medium, which can improve the intelligent degree and the accuracy of the abnormality detection in the process of the battery cell in the shell. An embodiment of the present application provides a method for detecting an abnormality of a battery cell in a shell, including: acquiring detection data in the process that the battery cell is pushed into the battery shell; Generating one or more curves to be detected according to the detection data; Detecting the one or more curves to be detected respectively according to historical detection data to obtain a detection result corresponding to each curve to be detected, wherein the historical detection data are detection data when no abnormality occurs in the process that a historical battery cell is pushed into a battery shell; And determining an abnormal result of the battery cell entering the shell according to the detection result corresponding to each curve to be detected. As an optional implementation manner, in the first aspect of the embodiment of the present application, the detection data includes a displacement value and/or a thrust value of the cell pushed by the cell into the battery case; the generating one or more determined curves to be detected according to the detection data comprises the following steps: And generating a curve to be detected corresponding to the thrust value according to the thrust value, and/or generating a curve to be detected corresponding to the displacement value according to the displacement value. In an optional implementation manner, in a first aspect of the embodiment of the present application, the detecting, according to the historical detection data, the one or more curves to be detected respectively, to obtain a detection result corresponding to each curve to be detected includes: Generating a standard curve corresponding to each curve to be detected according to the historical detection data, and determining a slope threshold corresponding to each standard curve according to each standard curve; Determining the slope of each curve to be detected, and comparing the slope of each curve to be detected with a corresponding slope threshold value to obtain a slope comparison result of each curve to be detected; determining the similarity between each curve to be detected and the corresponding standard curve, and comparing each similarity with a similarity threshold value to obtain a similarity comparison result of each curve to be detected; And obtaining a detection result corresponding to each curve to be detected according to the slope comparison result and the similarity comparison result of each curve to be detected. As an optional implementation manner, in the first aspect of the embodiment of the present application, the method further includes: Determining abnormal data corresponding to each curve to be detected according to the detection data; the step of respectively detecting the one or more curves to be detected according to the historical detection data to obtain a detection result corresponding to each curve to be detected, comprising the following steps: determining an abnormal data threshold according to the historical detection data; and comparing the abnormal data corresponding to each curve to be detected with the abnormal data threshold value to obtain a detection result corresponding to each curve to be detected. As an optional implementation manner, in the first aspect of the embodiment of the present application, the abnormal data includes a stuck value and a glitch value, and the abnormal dat