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Air-Coupled Ultrasound Spectroscopy for Electrolyte Fill Level Detection in Lithium-Ion Pouch Cell Batteries

  • University of Birmingham
  • Hy-Met Ltd
  • University of Bremen
  • University of Exeter

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

Abstract

Current testing of Li-ion pouch cell batteries in manufacturing settings is performed through batch testing and often by destructive means. This process is costly and is insensitive to some defects. Detecting defects earlier in the production process than the end-of-line is imperative for financial and material efficiency and safety objectives to be met. Internal defects, including an improper fill-level of electrolyte, commonly result in explosive thermal runaway and fire, which pose a significant danger to human life. Therefore, the development of methods to rapidly and robustly detect internal defects within cells assists in the initial refinement of production and allows for the identification of defects to prevent these batteries from entering the consumer market. Innovative analysis of signals from air-coupled ultrasound scans for batteries with different electrolyte fill-levels, uses a combination of signal metrics and wavelet transform analysis. Results of Support Vector Machine classification models are used to demonstrate the potential for predicting anomalous fill-levels from air-coupled ultrasound signals, conducive to rapid in-line inspection in gigafactories.
Original languageEnglish
Title of host publication2025 IEEE International Ultrasonics Symposium (IUS)
PublisherIEEE
Pages1-4
Number of pages4
ISBN (Electronic)979-8-3315-2332-9
DOIs
Publication statusPublished - 2025

Publication series

Name2025 IEEE International Ultrasonics Symposium (IUS)
PublisherIEEE
ISSN (Print)1948-5719
ISSN (Electronic)1948-5727

Funding

This work was funded via Royal Society Short Industry Fellowship grant SIF\R2\242003, and by the UK's Knowledge Exchange Hub for the Mathematical Sciences through the European Study Group in Industry Post-ESGI Impact Scheme Award

FundersFunder number
UK's Knowledge Exchange Hub for the Mathematical Sciences
The Royal SocietySIF\R2\242003

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