Machine vision is used along the whole battery cell production process. During electrode manufacturing, the process steps are largely cell-type-independent, producing anode and cathode sheets or foils. In the cell assembly step, battery cells are assembled in pouch, cylindrical, or prismatic form. In the final cell finishing steps – formation and aging – the cells are charged, discharged, and tested for quality.
Electrode manufacturing is a crucial step in battery cell production, involving the creation of cathodes and anodes. The process begins with a substrate foil, onto which a slurry is applied. This is followed by a drying process before the coated foils are compacted by calendering. To finish off the electrode manufacturing, the calendered rolls are slit into smaller electrode rolls (daughter rolls) and cut by laser or shear cutting methods to notch or separate the electrode sheets from a continuous web.
With the help of machine vision, the process parameters of electrode manufacturing such as coating thickness, surface quality, adhesion, and cut edge geometry can be closely monitored.
Example applications for machine vision in electrode manufacturing
The cell assembly steps depend on the battery type. In the case of pouch and prismatic cells, the utilization of vacuum grippers ensures precise and damage-free stacking of up to 120 electrode sheets – comprising anodes, separators, cathodes, and more. In contrast, cylindrical cells undergo a winding process. Here, the so-called jelly roll is created by feeding the electrode webs and separator webs into the process, subsequently winding them around a center pin. Upon completing the packaging of the cells, the electrolyte is filled into the cell’s packaging.
With the help of machine vision, critical process parameters such as position accuracy, damage-free handling of the electrode sheets and the alignment of electrode and separator foils can be controlled.
Example applications for machine vision in cell assembly
Formation & aging
The formation and aging process is the third step in battery cell production, aimed at optimizing cell performance and longevity. Before the battery cells leave the factory, they undergo a variety of end-of-line tests to determine the cell quality, e.g., pulse tests, OCV tests, capacity test and leak tests. After successfully passing all tests, the cells are packaged and prepared for shipping.
With the help of machine vision, defective, deformed or damaged cells can be quickly classified and handled accordingly. Unreadable codes, impurities, or foreign bodies can also be robustly detected.
Example applications for machine vision during formation & aging
Robust machine vision software for electrode manufacturing
MVTec's comprehensive product lineup, featuring HALCON, MERLIC, and the Deep Learning Tool, presents a formidable arsenal for addressing the diverse application areas within electrode manufacturing.
Calibration: Use various robust calibration measures for all different types of hardware setups.
Measurement: High precision edge detection enables measurement of multiple objects or saving budget with lower resolution cameras.
Alignment & Position Recognition: Determine coated areas with high accuracy even on low resolution images. High precision fiducial recognition algorithms ensure exact electrode sheet positioning in milliseconds.
Surface Inspection: Differentiate coated and uncoated areas, as well as defects, even with minor brightness and gray value variations. Detect even the smallest scratches, dents, bumps on highly reflecting surfaces.
Defect Detection: Reliable detection of any type or size of defect – known or unknown.
Identification: Achieve ideal OCR results in challenging conditions like reflective surfaces or highly textured backgrounds. Read bar and data codes robustly despite motion blur or varying illumination.
Find out how customers use MVTec software to develop high-innovative solutions for battery production.