A conceptual framework for data-driven optimization in the semi-dry electrode production for lithium-ion batteries

The production of lithium-ion batteries is characterized by a high complexity and manifold cause-effect correlations between process parameters and product properties, especially in electrode production processes. Semi-dry electrode production for lithium-ion batteries allows a reduction in energy consumption during production due to a reduction of drying length and decreases the amounts of toxic solvents. In semi-dry electrode production, highly viscous granules are processed using a two-roller calender in contrast to a low-viscous slurry in conventional coating processes. For this two-roller calender applied in semi-dry electrode production, the relationships between process and product parameters are unknown and related processes, e.g., the conventional calendering process, have limited validity. This paper presents the approach to identifying cause-effect correlations in semi-dry electrode production using a calender with at least three main process parameter dimensions, such as roller temperature and speed as well as calender gap, leading to a large parameter space. In the first step, the most crucial process parameters in the semi-dry coating step were identified. A concept for setting up the digital infrastructure for data acquisition and controlling the calender during experiments to generate a suitable data base is proposed. In the second step, this data can be used to understand and model the semi-dry electrode manufacturing process and to identify optimized parameters for specific product properties based on machine learning algorithms. A multi-fidelity model in combination with gaussian process regression is a promising way to iteratively adapt the model to new materials or different granule formulations. Also, a straightforward extension based on the presented concept for other calender types is possible.