With the rapid growth of the new energy vehicle industry, the number of end-of-life power batteries, which serve as the technological core, is also increasing significantly. Unfortunately, this rise in retired power batteries has led to severe environmental pollution and resource wastage. The detection of shell bolts in power batteries has thus become a crucial step in the recycling and disassembly process. To address this issue, this research proposes a detection method for end-of-life power battery shell bolts. Based on market analysis, the target bolt for the retired power battery shell was identified. The bolt images were collected and preprocessed to create a custom dataset on the experimental platform. Four popular object detection algorithms were compared, and the YOLOv8 model is selected to improve with EMA module. The improved YOLOv8 model achieves 98.9% for mAP_0.5, which increases more than 2 percentage points. Based on the repeatability of bolt recognition, this detection method can be used for the identification of bolts in other battery shells, providing a theoretical foundation for promoting the robotic disassembly of battery shells.

中文翻译：

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动力电池报废外壳螺栓智能检测方法

随着新能源汽车产业的快速增长，作为技术核心的动力电池报废数量也在大幅增加。不幸的是，退役动力电池的增加导致了严重的环境污染和资源浪费。动力电池外壳螺栓的检测因此成为回收拆解过程中至关重要的一步。针对这一问题，本研究提出了一种报废动力电池外壳螺栓的检测方法。根据市场分析，确定了退役动力电池外壳的目标螺栓。收集并预处理螺栓图像，以在实验平台上创建自定义数据集。比较了四种流行的目标检测算法，并选择YOLOv8模型通过EMA模块进行改进。改进后的YOLOv8模型对于mAP_0.5达到了98.9%，提升了2个多百分点。基于螺栓识别的重复性，该检测方法可用于其他电池壳中螺栓的识别，为推广电池壳机器人拆解提供理论基础。