Abstract
This paper attempts to address key challenges to automate unit operations (e.g., disassembling and sorting battery components at the cell level) of the lithium-ion battery direct recycling process. In our previous publications, we introduced the design and prototype of an automated disassembly system that can separate cell cases, metal tab, cathode, anode, and separators of a LIB pouch cell with minimum human intervention. In this paper, we take one step further to integrate industrial vision cameras and sensors into the prototyped system which allows real-time process defect detection and corrective action. Specifically, this paper focuses on online sensor integration in the electrode sorting step for separating cathode and anode electrode sheets. The electrode sorting and separation method developed here simplifies the subsequent materials extraction and purification operations of the LIB direct recycling.
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Acknowledgements
This work was funded by the Department of Mechanical Engineering at Virginia Tech and Alfred P. Sloan Foundation under award G-2020-12651.
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Li, L., Maftouni, M., Kong, Z.J., Li, Z. (2022). An Automated Recycling Process of End-of-Life Lithium-Ion Batteries Enhanced by Online Sensing and Machine Learning Techniques. In: Lazou, A., Daehn, K., Fleuriault, C., Gökelma, M., Olivetti, E., Meskers, C. (eds) REWAS 2022: Developing Tomorrow’s Technical Cycles (Volume I). The Minerals, Metals & Materials Series. Springer, Cham. https://doi.org/10.1007/978-3-030-92563-5_49
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DOI: https://doi.org/10.1007/978-3-030-92563-5_49
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