储能设备电池极片缺陷检测网络研究

Research on Defect Detection Network for Battery Electrodes in Energy Storage Devices

储能设备在平衡能源供应与提高能源利用效率方面发挥着关键作用。锂电池因其高能量密度、长循环寿命成为储能设备的主流选择之一。然而,锂电池的制造过程面临诸多质量控制挑战,特别是电池极片的缺陷问题。本文针对电池极片生产中的划痕、聚团、裂纹和气泡四种主要缺陷,设计了一种包含边缘特征增强模块和多尺度特征提取模块的改进型检测模型;通过多种卷积核级联进行特征融合,提高了对多尺度缺陷目标的提取能力。实验结果显示,该模型表现出了较高的精确度,将全类缺陷识别准确率从88.9%提高到了95.9%,可准确识别及定位电池极片生产时出现的缺陷。该研究为锂电池极片缺陷检测提供了一种高效的解决方案,推动了锂电池储能设备的质量提升和安全保障。

Energy storage devices play a crucial role in balancing energy supply and enhancing energy utilization efficiency.Lithium batteries have become one of the mainstream choices for energy storage devices due to their high energy density and long cycle life.However,the manufacturing process of lithium batteries faces numerous quality control challenges,particularly with defects in battery electrodes.In this paper,an improved detection model,which includes edge feature enhancement module and multi-scale feature extraction module,is designed to solve the four major defects in the production of battery electrode pieces,namely scratches,clumps,cracks and bubbles.Multiple convolution kernel cascades are used for feature fusion to improve the extraction capability of multi-scale defect targets.Experimental results show that the model demonstrates high accuracy,increasing the overall defect recognition rate from 88.9%to 95.9%,and accurately identifies and locates defects occurring during battery electrode production.This research provides an efficient solution for defect detection in lithium battery electrodes,promoting quality improvement and safety assurance in lithium battery energy storage devices.