Research on Battery Defect Detection System Based on LabVIEW

With the widespread use of new energy vehicles and smart devices, battery safety and reliability have become increasingly important. However, current defect detection in battery manufacturing largely relies on manual inspection, which is time-consuming and prone to inaccuracies. To address these challenges, this study designs and implements a battery defect detection system based on the LabVIEW platform. The system constructs modules for image acquisition, processing, and analysis to efficiently detect both external and internal battery defects. This paper first introduces various battery defect types and their detection principles, followed by a detailed explanation of the system’s architecture, including hardware configuration and software implementation. Utilizing image processing techniques, this study develops algorithms for feature extraction, morphological processing, and defect recognition, and further optimizes the algorithm’s performance. Experimental results show that the system effectively identifies common defects such as surface cracks, tab misalignment, and poor welding, achieving a detection accuracy of 98.5% with a detection time of 0.5 seconds. The findings demonstrate that the LabVIEW-based battery defect detection system significantly enhances the automation level of the battery manufacturing process, presenting broad application prospects and promotional value.