A testing device and method for lithium-ion battery soft-pack aluminum foil

By acquiring data on light source intensity and incident angle, calculating pixel grayscale difference and micro-displacement, and generating defect optical path difference coefficient, the shortcomings of existing aluminum foil defect detection technologies are solved, achieving high-precision defect identification and depth judgment, and ensuring high-quality evaluation of lithium-ion battery aluminum foil.

CN121298756BActive Publication Date: 2026-05-26GUANGXI ACAD OF SCI
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
GUANGXI ACAD OF SCI
Filing Date
2025-11-07
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing technologies lack sensitivity in detecting defects in lithium-ion battery aluminum foil, especially tiny or deep defects. Traditional methods struggle to identify shallow microcracks or small pinholes, leading to instability in aluminum foil quality assessment and battery performance.

Method used

A method for detecting soft-pack aluminum foil for lithium-ion batteries is adopted. By acquiring data on light source intensity and incident angle, calculating pixel grayscale difference and micro-displacement, a defect optical path difference coefficient is generated. Combined with multi-dimensional data weighted calculation, the depth and location of defects are accurately identified.

Benefits of technology

This improves the accuracy and reliability of defect detection, avoids missing minor defects, and ensures the accuracy of aluminum foil quality assessment and high-quality standards for battery materials.

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Abstract

This invention relates to the field of defect detection technology, specifically to a detection device and method for lithium-ion battery soft-pack aluminum foil, comprising the following steps: acquiring detection parameters, collecting raw frame recording data to establish a matrix, calculating and filtering pixel difference matrices, calculating displacement optical path difference normalization generation coefficients, determining depth allocation identifier statistical distribution, and normalizing and generating confidence coefficients. In this invention, by controlling the combination of light source intensity and incident angle, pixel grayscale difference data is acquired, refining the microstructure analysis of the aluminum foil surface, identifying microcracks and pinhole defects, especially shallow defects, comprehensively analyzing grayscale differences and displacement differences, calculating micro-displacement and optical path differences, extracting defect depth information and refining classification, improving the ability to identify deep defects, and combining multi-dimensional data weighting to generate confidence coefficients, thereby improving detection accuracy and reliability, optimizing quality assessment, avoiding the omission of minor defects, and ensuring battery material quality standards.
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