BATWOMAN: BatWoMan Carbon Neutral European Battery Cell Production with Sustainable, Innovative Processes and 3D Electrode Design to Manufactur
- Start: 01/09/2022
- End: 31/08/2025
- Budget: 4.8 Million €
Partners:
- Austrian Institute of Technology GmbH (AIT- Coordinator)
- CIDETEC Energy Storage (CIDETEC)
- Karlsruher Institut für Technologie (KIT)
- Universität Duisburg – Essen (UDE)
- Sovema S.p.a (SOV)
- Mathews International GmbH (Saueressig)
- RISE Research Institutes of Sweden AB (RISE AB)
Website:
The project:
BatWoMan develops new sustainable and cost-efficient Li-ion battery cell production concepts, paving the way towards carbon-neutral cell production within the European Union.
Three main technological efforts will be supported digitally via creating an AI-driven, innovative platform for smart re-tooling, constantly monitoring the sustainability and efficiency of the proposed individual production steps, and developing a battery data space providing relevant cell background data.
This way, BatWoMan will lead to an estimated cell production cost reduction of 63.5% and cell production energy consumption reduction by 52.6% and therefore enable a European leadership position in sustainable battery production.
CIDETEC's role in the project:
CIDETEC Energy Storage will lead the production of Li-ion battery electrodes using sustainable manufacturing techniques with reduced energy consumption, low carbon footprint and no Volatile Organic Compounds (VOCs). CIDETEC has the know-how about the development on slurry formulation, coating process optimization and cell assembly during the battery manufacturing process. CIDETEC will develop solvent-reduced electrode processing and will optimize the upscaled process. Additionally, CIDETEC will support in eco-friendly cell assembly and testing activities; more concretely, CIDETEC will identify several cell formation protocols (from literature and the project itself) to reduce formation time and match the final performance of the cell.
Concerning modelling activities, CIDETEC will support battery manufacturing optimization through data-driven modelling approaches. With that purpose, gathering the best experimental data is needed. This will be done by selecting machine parameters and output properties of each manufacturing step to then create a custom Design of Experiment (DoE) to maximize input/output variability while minimizing the number of experiments needed. These experiments will be made to gather data to build the machine learning model.
