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Case Study

EV-battery matchmaking to maximise circular revalorisation

  • Status: In Progress
  • Commodity(s): Lithium, Cobalt, Graphite, Graphene, Nickel, Manganese
  • Location: Global, Europe
  • Thematic Area: CE principles for raw materials and new geomodels

Objectives

  • Demonstrate technical and economic feasibility of improving post-use revalorisation of EV-batteries for cascading in secondary applications (e.g. replacement and spare parts, secondary storage, cascaded use to other transportation devices (e.g. forklifts) across multiple use cycles).
  • Use artificial intelligence to determine best revalorisation option as a function of state of health of LIB-battery and matching against demand for different use applications with different energy load profiles.
  • Establishing an economic viable, scalable and global market platform with embedded fulfilment operations to improve matchmaking between post-use battery owners and future users at reduced logistics cost and in full compliance with regulatory requirements.

Researchers

Professor Markus Zils

Dr Krishna Mohan

Carol Pettit

Professor Frances Wall

Partners

Circunomics GmbH

Cornish Lithium drilling rig

Description

The Electric Vehicle lithium-ion battery (EV-LIB) segment is characterised by a

  • high proliferation of different EV battery chemistries, builds and designs.
  • high degree of innovation in EVs with rapid replacement of EV-battery-types even within same EV-makes and models.
  • significant amount of used EV-batteries on the market already available due to EV-vehicle damage, excess production, expired quality credentials, etc.
  • an energy transition to renewables with significant increase in demand for battery-based energy storage.

While there is already a significant amount of EV-LIB-batteries becoming available at end-of-life (EoL) with significant residual energy storage capacity (frequently in excess of 80% state of health), finding suitable applications for those EoL batteries at component level (and not as feedstock for premature recycling operations) is a complex task with many hurdles to overcome, including for example:

  • difficulty to judge the exact state of health given unknown usage patterns in primary application.
  • insufficient diagnostic and documentation of residual capacity and state of health.
  • lack of clarity about which secondary application would be the best fit for the specific battery.
  • significant number of qualifications, certifications and licenses required to handle used batteries.
  • high degree of intransparency on the available demand and supply of batteries across very different and so far non-integrated industrial sectors with non-existing established collaboration agreements.

To overcome these barriers, the company Circunomics has focused on addressing the core prohibitors of best post-use options through building an innovative approach which includes: a) AI-powered diagnostics; b) an efficient match-making platform; and c) a set of procedures to ensure seamless logistics and documentation.

  1. Effective analytics to determine the best technical and most economical post use, secondar application requires an indepth understanding of the potential use case configurations and an intelligent analytics avoding to perform dedicated state-of-healt test for each individual cells.
  2. Efficient match-making platform for owners of EV-batteries and potential customers with a detailed technical and fit-for-purpose description of the available cells paired with a price-setting mechanism to ensure rapid clearing of stock.
  3. Compliant end-to-end fullfillment process comprising standard contracts and certifications, build up of pre-qualified potential logistics operators, collaboration with testing labs and handling operations.

With this model Circunomics could demonstrate options to revalorise the post-use residual energy storage capacities at component level and to avoid premature costly, low-yield recycling of precious EV-battery ensembles.

The learnings of this case study have been highlighted in the tech metals circular economy roadmap for LIB materials and examined using agent-based-modelling in Theme 4 of the Met4Tech project.