Development of a Digital Twin for Improved Ramp-Up Processes in the Context of Li-Ion-Battery-Cell-Stack-Formation

Abstract

The ramp-up of machines for stack formation processes in the context of battery cell production is difficult due to a lack of knowledge about cause-effect relationships. This concerns the initial setup of the machine as well as the change of process input variables. For example, there are strong material dependencies in the area of cell stack formation of battery cells. Individual adjustments of the machine parameters to the different materials are therefore necessary. Digital twins represent the production process and the machine operations in a virtual environment. Cause-effect relationships can thus be quantified and evaluated. Optimization approaches for ramp-up-processes can be tested with low risk in virtual space before they are implemented in reality. This paper describes the development process of a digital twin representing a machine for flexible cell stack formation of pouch cells. As basis for the digital twin, a kinematic process model of the machine is developed from the underlying CAD files. Sensors and actuators are virtually integrated in the design environment of the machine. Connecting the model to a virtual controller, allows virtual testing and evaluating of the developed PLC code within the digital twin. Furthermore, the development of a simulation model for the prediction of the electrode web tension, as a quality-critical parameter, is presented. This purpose requires relevant aspects of the machine, for example the unwinder drive behaviour, to be recognized and integrated. In order to enable near-real-time runability, this simulation model is converted into a reduced-order-model. This substitution can be validated by tracing and comparing the web tension during commissioning scenarios on the real machine. Therefore it is possible to virtually represent control-side kinematic processes while also making statements regarding the web tension of the electrode material. The resulting functional digital twin of the flexible stack formation machine will be used to optimize the process parameters as well as the current machine design.