Overcharge is one of the main factors that lead to thermal runaway of lithium batteries. However, there is no research on the quantitative relationship between overcharged state and gas production, so as to effectively monitor the safe state of the battery and avoid thermal runaway. In this paper, X-ray computed tomography (CT) is proposed to explore the overcharge battery. The internal structure changes of bulge deformation and electrode separation is observed from tomographic images of two different cross-sectional directions. The relationship between gas production and overcharge state of charge (SOC) is quantitatively analyzed. As overcharge SOC increases, gas production increases exponentially. Gas distribution is analyzed by density distribution feature (DDF) vector. The gas production is mainly distributed in the middle of the overcharge batteries. It is envisaged that these techniques can be used to better understand the overcharge of battery nondestructively, visually and effectively, then will lead to avoid the occurrence of thermal runaway.

Overdischarge is one of the main factors of lithium-ion battery failure, due to the inconsistency of lithium-ion battery in pack. However, the failure mechanism remains unclear. This article introduces the X-ray computed tomography (CT) to explore the gas production and copper dissolution of lithium battery during overdischarge state. From tomographic images in two different cross-sectional directions, the internal structure changes of bulge deformation and copper deposition are observed to quantitatively analyze the relationship between copper deposition and overdischarge state of charge. The position distribution is analyzed by density distribution feature, which indicate that the gas production is mainly distributed in the middle of the battery, and the copper deposition is distributed around the outer side. The experimental result shows that X-ray CT is a nondestructive, quantitative, visual, and effective way to study the internal structure and material distribution of the overdischarge battery, so as to effectively monitor the state of the lithium-ion battery, to avoid dangerous problems such as internal short circuits and thermal runaway.

According to visual restaurant service robot application environment under the condition of uneven light and changeful, light and shade and occlusion, make the RGB image noise influence is very big which was collected by the binocular vision, difficult to use conventional image processing method for accurate identification of plate, the plate image is analyzed the characteristics and the traditional image processing technology, a adaptive edge of ellipse fitting method was proposed in this paper. Firstly, the image of dinner plate was preprocessed, and then the image edge information was extracted. Finally, according to the edge information, the dinner plate was identified by fitting ellipse with Hough transform. The experimental results show that under the circumstance of different light and shade in most restaurants, the ellipse feature of the edge of the plate can be accurately extracted by using this method, and the plate can be recognized, the validity of the method was verified.