Digital Twin in the Battery Production Context for the Realization of Industry 4.0 Applications

Abstract

Due to the worsening climate change drastic changes in the transportation sector are necessary. Crucial factors for sustainable energy supply are reliable and economical energy storage systems. Associated with that is the development of gigafactories with a capacity of up to 1000 GWh in 2030 in Europe (currently 25 GWh) for the production of battery cells especially for the automotive sector, which is one of the largest emitters of greenhouse gases in Europe. In addition to the required investments, high scrap rates due to unknown interdependencies within the process chain represent a central challenge within battery cell production. Another key challenge in series production is the product tracking along the value chain, which consists of continuous, batch and discrete processes. Because of it complexity the battery cell production industry is predestined for Industry 4.0 applications in order to meet the current challenges and to make battery cell production more efficient and sustainable. Digital twins and the use of AI algorithms enable the identification of previously unknown cause-effect relationships and thus a product improvement and increased efficiency. In this paper, the digital twin of a battery cell production will be developed. For this purpose, general requirements for the field of battery cell production are first determined and relevant parameters from the literature as well as from a production pilot line are defined. Based on the requirements and the selected parameters a corresponding structure for the digital twin in battery cell production is built and explained in this contribution. This provides the basis for measures to optimize production, such as predictive quality.