Data-based process and method development to increase efficiency and quality in Li-ion cell production (E-Qual)
01.03.2020 – 28.02.2023
- Fraunhofer Institute for Surface Engineering and Thin Films (IST)
- Karlsruhe Institute of Technology (KIT), Institute of Production Science (wbk)
- Technical University of Munich (TUM), Institute for Machine Tools and Industrial Management (iwb)
- Center for Solar Energy and Hydrogen Research Baden-Württemberg, Production Research (ZSW)
The E-Qual project aims to increase efficiency and quality in the production of lithium-ion batteries. Using data-based approaches, the four partnering institutes develop and implement solutions to minimize costs and environmental impacts as well as measures to increase quality, energy density and throughput in the field of battery cell construction. The validation of the project results is based on the production of large-format cells in the PHEV1 format on an industrial scale at the Research Production Line (FPL) in Ulm. The focus is in particular on the development of flexible processes and machines, the modelling to increase material and energy efficiency, and innovative quality assurance methods. Already established cell assembly processes are consistently developed and optimized in this project with regard to their flexibility, quality and efficiency. The creation of virtual process models allows the derivation of optimal process and machine parameters and the implementation and validation of newly developed machine components. A special emphasis lies on the implementation of a function-integrated process for the production of format-flexible cells, that is optimized for minimum set-up time. In order to realize hidden efficiency potentials in cell production, methods for increasing resource efficiency are applied to the production lines of the participating research partners. This also includes the establishment of automated data acquisition and processing as well as the development of dynamic material and energy flow models. Since the increase of product quality and the reduction of rejects are essential drivers for efficiency increases, integrated quality management concepts for a varied production are being considered. The application of Big Data methods for quality forecasting and early rejection detection as well as the investigation of the influence of energy efficiency/flexibility measures on product quality will make innovative contributions to improved quality management.