A novel disassembly process of end-of-life lithium-ion batteries enhanced by online sensing and machine learning techniques

• Published in: Journal of intelligent manufacturing 2023-06, Vol.34 (5), p.2463-2475
• Main Authors: Lu, Yingqi, Maftouni, Maede, Yang, Tairan, Zheng, Panni, Young, David, Kong, Zhenyu James, Li, Zheng
• Format: Article
• Language: English
• Subjects: Aluminum; Automation; Business and Management; Business competition; Closed loops; Computer vision; Control; Control systems; Cutting; Dismantling; Electrolytes; End of life; Feedback control; Flammability; Flammable materials; Hazardous materials; Lithium; Lithium-ion batteries; Machine learning; Machines; Manufacturing; Mass production; Mechatronics; Neural networks; Processes; Production; Quality control; Real time; Rechargeable batteries; Robotics; Strategic materials; Sustainability; Waste disposal
• ISSN: 0956-5515; 1572-8145
• DOI: 10.1007/s10845-022-01936-x

An effective lithium-ion battery (LIB) recycling infrastructure is of great importance to alleviate the concerns over the disposal of waste LIBs and the sustainability of critical elements for producing LIB components. The End-of-life (EOL) LIBs are in 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. Meanwhile, hazardous and flammable materials are contained in LIBs, posing great threats to the human exposure. Therefore, it is difficult to dismantle the LIBs safely and efficiently to recover critical materials. Automation has become a competitive solution in manufacturing world, which allows for mass production at outstanding speeds and with great repeatability or quality. It is imperative to develop automatic disassembly solution to effectively disassemble the LIBs while safeguarding human workers against the hazards environment. In this work, we demonstrate an automatic battery disassembly platform enhanced by online sensing and machine learning technologies. The 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 the safety.