AI-Assisted Dismantling of Electric Vehicle Batteries – A Case Study on Technology Networking
Advancing electric mobility is essential for meeting worldwide climate objectives. Yet, this advancement will lead to a surge in battery systems, sparking concerns over their sustainable and eco-friendly recycling practices. The disassembly of these systems is a critical step for either recycling or repurposing them. To enhance both cost-effectiveness and operational efficiency, there is a pressing need to automate the disassembly process. Existing disassembly lines highlight the requirement for a system that can adapt to handle multiple battery model variants efficiently. As the number of batteries needing disassembly is expected to grow exponentially with the rise in electric vehicle sales, continuing with current manual methods may soon become impractical. Addressing this challenge, this paper introduces a new approach using artificial intelligence to disassemble a broad range of battery models efficiently down to the module level. The article further elaborates on the integration of the technology into the demonstrator system, including the object and camera-based recognition components, and presents initial training data utilized in the system’s development.
Authors
- Thomas Reuter (ORCID: https://orcid.org/0000-0002-4322-9409)
- Dominik Hertel (ORCID: https://orcid.org/0009-0004-4034-1261)
- Gerald Bräunig (ORCID: https://orcid.org/0009-0009-4328-5660)
- Kai Kaufmann (ORCID: https://orcid.org/0000-0002-1262-7578)
- Sara Menetrey
- Jonas Hummel
- Florian Richter
Institutions
- Chemnitz University of Technology (DE)
- Galmed Pharmaceuticals (Israel) (IL)
- ICM Chemnitz (DE)
Publication Details
- Journal
- Procedia Computer Science
- Published
- 2026-01-01
- DOI
- https://doi.org/10.1016/j.procs.2026.02.127
- Primary Topic
- Recycling and Waste Management Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00