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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI-Assisted Dismantling of Electric Vehicle Batteries – A Case Study on Technology Networking

Thomas Reuter, Dominik Hertel, Gerald Bräunig, Kai Kaufmann et al.
Procedia Computer Science
Recycling and Waste Management Techniques
article

AI-Assisted Dismantling of Electric Vehicle Batteries – A Case Study on Technology Networking

Thomas Reuter, Dominik Hertel, Gerald Bräunig, Kai Kaufmann, Sara Menetrey, Jonas Hummel, Florian Richter
article en

Abstract

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.

Procedia Computer ScienceVol. 277
Chemnitz University of Technology (DE), Galmed Pharmaceuticals (Israel) (IL), ICM Chemnitz (DE)
Climate action
Openalex Percentile: Top 10%
Recycling and Waste Management Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.