Improve labelling and barcoding or QR coding requirements for efficient sorting

  • Likely project output: xx April 20xx
  • Theme 1: Pre-treatment and second-life management
  • Main Centre Node: University of Woolongong

Project summary

During collection in recycling centres, most end-of-life LIBs are aggregated as a mixture of different sizes, formats, and application types. Due to poor labelling and barcoding by manufacturers, these aggregates can also contain a mixture of different LIB chemistries. Manual sorting and clustering is employed to separate the decommissioned batteries into pure waste streams segregated by LIB chemistry type, before further processing. This tedious technical route could be optimised based on intelligence-assisted predesign strategies to increase efficiency and improve the economic viability of battery recycling.

Benefits to industry

Our research will incorporate necessary parameters (i.e. standard composition, performance degradation rate, safety operation, hazardous species, and recommended recycling approaches) integrated with state-of-the-art digital technologies, such as standardized QR codes, IR sensors, laser scanners, or near field communication technology to the cell pack, aiming at establishing a “cradle -to-grave” online life-cycle data system that can be shared with the recycling centre for efficient sorting of decommissioned batteries.

This research will take advantage of outstanding co-located Computer Science expertise at UoA (Australian Institute for Machine Learning – AIML – also see Research Environment) to develop an application in digital platforms for battery recycling research.

Project Lead & Investigators

Co-Leads

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Chief Investigators

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Partner Investigator and Industry Placement

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