Spectral Characterization of Battery Components from Li-Ion Battery Recycling Processes
Author:
Richter Julia1, Lorenz Sandra2ORCID, Kaas Alexandra3ORCID, Fuchs Margret2, Röder Christian1, Peuker Urs A.3ORCID, Heitmann Johannes1, Gloaguen Richard2ORCID
Affiliation:
1. Institute of Applied Physics, TU Bergakademie Freiberg, Leipziger Straße 23, D-09599 Freiberg, Germany 2. Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf, Chemnitzer Straße 40, D-09599 Freiberg, Germany 3. Institute of Mechanical Process Engineering and Mineral Processing, TU Bergakademie Freiberg, Agricola-Straße 1, D-09599 Freiberg, Germany
Abstract
Considering the increasing demand for Li-ion batteries, there is a need for sophisticated recycling strategies with both high recovery rates and low costs. Applying optical sensors for automating component detection is a very promising approach because of the non-contact, real-time process monitoring and the potential for complete digitization of mechanical sorting processes. In this work, mm-scale particles from shredded end-of-life Li-ion batteries are investigated by five different reflectance sensors, and a range from the visible to long-wave infrared is covered to determine the ideal detection window for major component identification as relevant input signals to sorting technologies. Based on the characterization, a spectral library including Al, Cu, separator foil, inlay foil, and plastic splinters was created, and the visible to near-infrared range (400–1000 nm) was identified as the most suitable spectral range to reliably discriminate between Al, Cu, and other battery components in the recycling material stream of interest. The evaluation of the different sensor types outlines that only imaging sensors meet the requirements of recycling stream monitoring and can deliver sufficient signal quality for subsequent mechanical sorting controls. Requirements for the setup parameters were discussed leading to the setup recommendation of a fast snapshot camera with a sufficiently high spectral resolution and signal-to-noise ratio.
Funder
Federal Ministry of Education and Research
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