Iterative approach for scaling machine parameters to desired scale

The production of modern energy storage systems, requires a high degree of agility and flexibility in the planning of new or the expansion of existing production facilities due to scientific or regulatory developments. In this context, process chain simulation is an established tool to support planners and decision-makers in their respective considerations by comparing different variants of possible production scenarios. However, a simulation requires sufficient machine performance data in terms of completeness and accuracy for the assessed scale. This is currently a major challenge, as datasets are often incomplete or only available for systems on a laboratory scale. For this reason, data is often estimated based on the data for laboratory-scale systems using different scaling approaches without adequately considering the accuracy. In order to create a better data basis for the scalable simulation of battery production lines the aim of this work is to fill data gaps and contribute to better data quality of scaled datasets. Therefore, a developed iterative scaling approach is presented which considers parameter dependencies and utilizes the most promising scaling method in the context of data availability. It is exemplarily applied onto a reference unit of the battery production and validated against reference datasets of respective scales. One of the resulting findings is that the developed approach is especially superior in scaling characteristic machine element dimensions and energy demand. Additionally, the importance of parameter dependencies on the scaling reference in order to reduce complexity or to set boundary conditions could be shown.