No abstract

The increasing relevance of automotive lithium‐ion battery cells spotlights the importance of economic production in a high quantity. In this context, production technology for large battery formats is of great relevance. Therefore, it is necessary to identify effects on important cell properties, and based on this, develop an understanding of the interaction between process parameters and product properties. Large‐format cells are not comprehensively examined, particularly, in a large sample size, analyzing cell properties in terms of distributed values. Hence, there is so far no statistical data concerning large‐format batteries and their distributed discharge capacity and self‐discharge. For this reason, and in contrast to other studies, the scope of this work is to investigate a large sample size of 79 industrial‐scale 9 Ah battery cells to ensure statistical relevance and generate distributed data of cell properties. For this purpose, a large number of cells are produced and extensively electrochemically investigated. Subsequently, the essential parameters are correlated with the electrode parameter of carbon black particle size. Hence, the foundation for this process–product–property relationship is laid.

In lithium-ion battery manufacturing, wetting of active materials is a time-critical process. Consequently, the impact of possible process chain extensions such as lamination needs to be explored to potentially improve the efficiency of the electrode and separator stacking process in battery cell manufacturing. This paper addresses the research gap of the unexplored effects of lamination on the wetting rate of electrode-separator assemblies in pouch cells. Based on the triangulation of three measurement techniques (gravimetric, optical, electrochemical), a correlation between lamination and wettability of electrode-separator assemblies is experimentally demonstrated, thus providing an important research contribution.

Nowadays, battery production involves the use of cost‐intensive materials and highly complex production processes. The knowledge of interactions between product properties and process parameters is often limited to best‐practice‐experience rather than based on actual quantitative correlations. This work quantifies the influence of electrode deposition accuracy (process parameter) on the electrochemical performance (product property) of large battery cells (discharge capacity >8 Ah). In total, 40 lithium‐ion battery cells with five different deposition error configurations are manufactured and electrochemically tested. The electrochemical results of these tests show a linear decrease in the discharge capacity with a linear increase in the deposition error. The reduced discharge capacity is attributed to the extended nonoverlap of cathode and anode areas as well as the larger travel distances of intercalating ions.

Due to the energy transition and the growth of electromobility, the demand for lithium-ion batteries has increased in recent years. Great demands are being placed on the quality of battery cells and their electrochemical properties. Therefore, the understanding of interactions between products and processes and the implementation of quality management measures are essential factors that requires inline capable process monitoring. In battery cell lamination processes, a typical problem source of quality issues can be seen in missing or misaligned components (anodes, cathodes and separators). An automatic detection of missing or misaligned components, however, has not been established thus far. In this study, acoustic measurements to detect components in battery cell lamination were applied. Although the use of acoustic measurement methods for process monitoring has already proven its usefulness in various fields of application, it has not yet been applied to battery cell production. While laminating battery electrodes and separators, acoustic emissions were recorded. Signal analysis and machine learning techniques were used to acoustically distinguish the individual components that have been processed. This way, the detection of components with a balanced accuracy of up to 83% was possible, proving the feasibility of the concept as an inline capable monitoring system.