BatProPass

Motivation

Industrial battery cell production is a key technology for the energy transition. The current state of the art is characterized by energy-intensive end-of-line testing, high scrap rates, and limited feedback on quality.

Process data remain unused in isolated solutions, while pressure is increasing due to requirements for carbon footprinting, the circular economy, and the EU Digital Product Passport (DPP). The potential lies in a Battery Production Passport (BPP) that links process parameters with models, digital twins, and environmental assessment in order to identify risks at an early stage, reduce testing, and lower costs and emissions.

Objectives and Approach

The aim of the Eureka research project BatProPass is to develop a BPP that integrates production data along the process chain, evaluates them using model-based approaches, and makes them usable for predictive quality control in the production process as well as for sustainability assessments.

To this end, standardized, cross-system data acquisition will be established and linked interoperably through a shared ontology.

Building on this, hybrid models will be developed that combine physics-based battery and aging models with machine learning approaches in order to predict quality risks at an early stage. In addition, a parametric life cycle assessment (LCA) will be integrated. The methods will be embedded in existing production lines and validated in a real manufacturing environment using key process parameters.

Innovations and Perspectives

Digital twins, production data acquisition, and life cycle assessments have so far largely existed as separate approaches. BatProPass integrates them for the first time in a production-oriented platform and links them with predictive quality control and DPP-compatible data structures.

The innovation lies in the real-time linking of production, quality, and sustainability data and their use for active process control. This will result in a validated BPP prototype that enables defect prediction, transparent carbon and energy indicators, and structured data provision in global value chains.