An abnormal battery cell identification method, device, apparatus and medium
By analyzing the differential capacity characterization parameters and clustering techniques in the cell formation process, the problem of difficulty in identifying cell anomalies after the formation process is solved, achieving non-destructive and full-coverage identification of abnormal cells, applicable to cells of different specifications.
CN121484254BActive Publication Date: 2026-06-05CONTEMPORARY AMPEREX RUNZHI SOFTWARE TECH LTD +1
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CONTEMPORARY AMPEREX RUNZHI SOFTWARE TECH LTD
- Filing Date
- 2026-01-12
- Publication Date
- 2026-06-05
AI Technical Summary
Technical Problem
In existing technologies, cell anomalies after the formation process are difficult to identify through visual inspection, leading to missed detections and additional costs.
Method used
By acquiring differential capacity characterization parameters during the cell formation process, analyzing process similarity and performing clustering, abnormal cells can be identified.
Benefits of technology
It achieves non-destructive and full-coverage identification of abnormal battery cells, avoiding missed detections and additional costs, and is applicable to battery cells of different specifications.
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Figure CN121484254B_ABST
Abstract
The application discloses an abnormal battery cell identification method, device, equipment and medium. The method comprises the following steps: acquiring formation process data of a plurality of target battery cells, wherein the formation process data comprises a differential capacity characteristic parameter at a plurality of preset potentials in a formation process; analyzing process similarity of each target battery cell, wherein the process similarity of the target battery cell represents the similarity between the formation process data of the target battery cell and the formation process data of other target battery cells; clustering the plurality of target battery cells based on the process similarity of each target battery cell to obtain a clustering result; and determining an abnormal battery cell in the plurality of target battery cells based on the clustering result. The above scheme can realize abnormal battery cell identification.
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Citation Information
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