Demand for lithium-ion battery cells (LIB) for electromobility has risen sharply in recent years. In order to continue to serve this growing market, large-scale production capacities require further expansion and the overall effectiveness of processes must be increased. Effectiveness can be significantly optimized through innovative manufacturing technology and by identifying scrap early in the production chain. To enable these two approaches, it is imperative to quantify safety- and function-critical product features in critical manufacturing steps through appropriate measurement techniques. The overview in this paper on quality control in LIB production illustrates the necessity for improved inspection techniques with X-rays to realize a fast, online measurement of inner features in large-scale cell assembly with short cycle times and to visualize inner product-process interactions for the optimization in electrolyte filling. Therefore, two new inspection techniques are presented that contribute to overcoming the aforementioned challenges through the targeted use of X-rays. First, based on the results of previous experiments in which the X-ray beam directions were deliberately varied, a online coordinate measurement of anode-cathode (AC) overhang was developed using a line detector. Second, a new concept and the results of a continuous 2D visualization of the electrolyte filling process are presented, which can be used in the future to optimize this time-critical process step. By using a X-ray-permeable and portable vacuum chamber it is possible to quantify the influence of process parameters on the distribution of the electrolyte in the LIB.

Research on battery materials and systems is broader than ever and a large amount of data is being generated. The data collected is often challenging to correlate or compare because they are not compiled in a structured way since no generally uniform data structure exists yet in the battery field. Besides, manufacturing steps and analysis parameters vary enormously from laboratory scale to industrial scale, from research institutions to consortia. Within the "DigiBatMat project" we have created a digital platform for battery material data and processing data that combines collected material data and knowledge within a defined structure based on taxonomies and ontologies. The structure of the digital platform was developed to cover the Battery LabFactory Braunschweig (BLB) pilot-scale production line for lithium-ion cells while being compatible with other battery production processes, for example at the laboratory bench scale. The platform’s underlying data structure is divided into an “electrode production” and “cell production” taxonomy, providing input modules for the most typical battery material and cell characterization methods. This approach offers a commercially available platform with a defined structure, which still allows flexibility for individual electrode and cell productions through option based input. Due to this structure, we aim to go beyond simple data storage by implementing functionalities that allow users to work with and analyze the data within the digital platform. Beside the functionalities we have implemented so far, which are shown here, we also aim to develop further functionalities that enable the identification of new correlations that are otherwise difficult or even impossible to obtain. A consistent data set is necessary to build up and validate such a digital platform. Hence, we specifically generated “reference” data and tested their management and analysis with the platform. We focus on three key use cases of collecting process data and characterizing electrodes and cells properties along the production process. In one of these key use cases we compare state-of-the-art cathodes with Ni-rich active materials, namely NMC811 and NCA. Data are collected from cells with such cathodes with varying loadings and electrode densities. Characterization of the electrodes includes, among other things, microscope data analysis of the microstructure and electrochemical characterization such as electrochemical impedance spectroscopy and rate capability. By showing how raw data can be imported into the database, processed and analyzed, we aim to demonstrate the possibilities and advantages of our digital platform.