| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
REWAS 2022: Automation and Digitalization for Advanced Manufacturing
|
| Presentation Title |
NOW ON-DEMAND ONLY - An Automated Recycling Process of End-of-life Lithium-ion Batteries Enhanced by Online Sensing and Machine Learning Techniques |
| Author(s) |
Liurui Li, Maede Maftouni, Zhenyu James Kong, Zheng Li |
| On-Site Speaker (Planned) |
Zheng Li |
| Abstract Scope |
The End-of-life (EOL) lithium-ion batteries (LIBs) are hazardous and flammable with various sizes and shapes, which create significant challenges to automate a few unit operations (e.g., disassembly at the cell level) of the recycling process. In this work, we demonstrate an automatic battery disassembly platform enhanced by online sensing and machine learning technologies. Computer vision is used to classify different types of batteries based on their brands and sizes. The real-time temperature data is captured from a thermal camera. A data-driven model is built to predict the cutting temperature pattern and the temperature spike can be mitigated by the close-loop control system. Furthermore, quality control is conducted using a neural network model to detect and mitigate the cutting defects. The integrated disassembly platform can realize the real-time diagnosis and closed-loop control of the cutting process to optimize the cutting quality and improve safety. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Recycling and Secondary Recovery, Sustainability |