The increasing global demand for high‐quality and low‐cost battery electrodes poses major challenges for battery cell production. As mechanical defects on the electrode sheets have an impact on the cell performance and their lifetime, inline quality control during electrode production is of high importance. Correlation of detected defects with process parameters provides the basis for optimization of the production process and thus enables long‐term reduction of reject rates, shortening of the production ramp‐up phase, and maximization of equipment availability. To enable automatic detection of visually detectable defects on electrode sheets passing through the process steps at a speed of 9 m s−1, a You‐Only‐Look‐Once architecture (YOLO architecture) for the identification of visual detectable defects on coated electrode sheets is demonstrated within this work. The ability of the quality assurance (QA) system developed herein to detect mechanical defects in real time is validated by an exemplary integration of the architecture into the electrode manufacturing process chain at the Battery Lab Factory Braunschweig.

The increasing global demand for high-quality and low-cost battery electrodes poses major challenges for battery cell production. As mechanical defects on the electrode sheets have an impact on the cell performance and their lifetime, inline quality control during electrode production is of high importance. Correlation of detected defects with process parameters provides the basis for optimization of the production process and thus enables long-term reduction of reject rates, shortening of the production ramp-up phase, and maximization of equipment availability. To enable automatic detection of visually detectable defects on electrode sheets passing through the process steps at a speed of 9 m/s, a You-Only-Look-Once architecture (YOLO architecture) for the identification of visual detectable defects on coated electrode sheets is demonstrated within this work. The ability of the quality assurance (QA) system developed here to detect mechanical defects in real time is validated by an exemplary integration of the architecture into the electrode manufacturing process chain at the Battery Lab Factory Braunschweig.

This paper presents an approach for the design and derivation for establishing a digital product twin for battery cells. A digital product twin is a virtual replica of a physical battery cell and can be used to predict and optimize quality properties and performance in real-time. The study focuses on pouch cell manufacturing and aims to map the large amount and variety of process information down to purchased parts and interim products. The approach for this study was to collect and analyze data from the physical production process and use this information to structure a digital battery product twin based on its product architecture. The main findings of this study indicate that a digital product twin can be effectively structured and implemented in a digital interface based on its product architecture in combination with data from the physical production process. The results of this study show the potential of digital product twins, in which statements about material, design, and behavior can be made using real information from production. Further research will focus on the practical application and implementation of digital product twins in a battery cell pilot production.