Correlative non-destructive techniques to investigate aging and orientation effects in automotive Li-ion pouch cells

Arthur Fordham, Zoran Milojevic, Emily Giles, Wenjia Du, Rhodri E. Owen, Stefan Michalik, Philip A. Chater, Prodip K. Das, Pierrot S. Attidekou, Simon M. Lambert, Phoebe K. Allan, Peter R. Slater, Paul A. Anderson, Rhodri Jervis, Paul R. Shearing, Dan J.L. Brett

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Summary The growing demand for electric vehicles (EVs) continues to raise concern for the disposal of lithium-ion batteries reaching their end of life (EoL). The cells inside EVs age differently depending on multiple factors. Yet, following extraction, there are significant challenges with characterizing degradation in cells that have been aged from real-world EV usage. We employed four non-destructive techniques—infrared thermography, ultrasonic mapping, X-ray tomography, and synchrotron X-ray diffraction—to analyze the aging of Nissan Leaf large-format pouch cells that were arranged in different orientations and locations within the pack. The combination of these methods provided complementary insights into cell degradation, with rotated/vertically aligned cells exhibiting distinct aging patterns compared with flat/horizontally aligned cells. These findings offer valuable information for pack design and demonstrate how cost-effective non-destructive techniques can provide practical assessment capabilities comparable to synchrotron studies. This approach enables decision support during EoL, enhancing battery production efficiency and minimizing material waste.
Original languageEnglish
Pages (from-to)2622-2652
Number of pages31
JournalJoule
Volume7
Issue number11
Early online date15 Nov 2023
DOIs
Publication statusE-pub ahead of print - 15 Nov 2023

Keywords / Materials (for Non-textual outputs)

  • automotive Li-ion pouch cell
  • electric vehicle
  • aging
  • infrared thermal imaging
  • ultrasound acoustic measurement
  • X-ray tomography
  • deep learning
  • synchrotron X-ray diffraction

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