ViPro

Motivation

The continuously growing demand for electrical energy storage devices for mobile and stationary applications is accompanied by high demands on the manufacturing processes. Lithium-ion battery cell production is characterized by a high level of complexity along the process chain. Furthermore, there are strong interactions between the individual sub-processes. As a result, fluctuations within the sub-processes have a direct impact on subsequent processes and thus on the cell properties. The cross-process optimization addressed in ViPro leads to a higher resource efficiency, reduces rejects and improves the product quality.

Project Description

The linking of sub-processes and the development of a process control and monitoring system is initially carried out in virtual space. This enables optimization approaches to be tested in a realistic and low-risk manner. For this purpose, four process steps are modeled exemplary. These process steps are connected through the cross-process control. With the help of cross-process control, information can be shared across all processes, deviations in intermediate product properties can be assessed, and the parameters of downstream processes can be determined. Machine learning can be used to identify unknown interactions of process parameters. With an operations control system linked to the production control, the worker is able to monitor and verify the solutions suggested by the system before the new parameters can be submitted. Visual means are also implemented for decision support. The suggestions made base on the system’s ability to predict the results for any parameter adaptions. Thus an overall optimization of the lithium-ion production process can be achieved.

Contact:

Benjamin Schumann
b.schumann(at)tu-braunschweig.de

Aleksandra Naumann
al.naumann(at)tu-braunschweig.de