Project Goals
This project develops flexible, (semi-)automated, and safety-focused methods for minimally destructive battery disassembly, complemented by structured pre-separation to boost material purity and second-life suitability. An AI-supported decision model weighs ecological, economic, and safety criteria to choose optimal routes among disassembly, pre-processing, and recycling, while accommodating heterogeneous system designs and emerging chemistries (e.g., SIB, ASSB). Expected outcomes include a compact methodological framework, validated mechanical separation procedures, and transferable decision rules that improve yield, purity, and second-life potential across current and future battery systems.
Place of Employment
Institute of Production Engineering, TU Graz, Kopernikusgasse 24, 8010 Graz
Supervisory Team
Franz Haas (TU Graz; primary), Georg Pesch (TU Wien), Christoph Spijker (MU Leoben)
Secondments
Experiments on dismantling strategies and evaluation of safety risks (1 month at BSCG/Graz), short visits at TU Wien and MU Leoben
Admission Requirements
Master degree in mechanical engineering, or similar areas
Essential Qualifications
- Strong expertise in mechanical separation technologies; hands-on experience with experimental prototyping, test design (DoE), and lab/bench-scale setup operation
- Knowledge of battery systems and materials (pack/module/cell structure, safety, handling of HV and hazardous materials).
Skills in data analysis and process evaluation
- Knowledge in AI-based decision modeling (e.g., Python, ML) is a plus
- Confident working with CAD
- Basic familiarity with automation/robotics or mechatronics integration
Offered Employment
Full time (40h/week) for 48 months
