TY  - JOUR
UR  - http://dx.doi.org/10.1149/1945-7111/ad59c9
SN  - 0013-4651
A1  - Fordham, Arthur
A1  - Joo, Seung-Bin
A1  - Owen, Rhodri Ellis
A1  - Galiounas, Elias
A1  - Buckwell, Mark
A1  - Brett, Dan
A1  - Shearing, Paul
A1  - Jervis, Rhodri
A1  - Robinson, James B
N2  - Acoustic emission (AE) is a low-cost, non-invasive, and accessible diagnostic technique that uses a piezoelectric sensor to detect ultrasonic elastic waves generated by the rapid release of energy from a localised source. Despite the ubiquity of the cylindrical cell format, AE techniques applied to this cell type are rare in literature due to the complexity of acoustic wave propagation in the architecture alongside the challenges associated with sensor coupling. Here, we correlate the electrochemical performance of cells with their AE response, examining the differences during pristine and aged cell. AE data was obtained and used to train binary classifiers in a supervised setting, differentiating pristine from aged cells. The best accuracy was achieved by a deep neural network model. Unsupervised machine learning (ML) models, combining dimensionality reduction techniques with clustering, were also developed to group AE signals according to their form. The groups were then related to battery degradation phenomena such as electrode cracking, gas formation, and electrode expansion. There is the potential to integrate this novel ML-driven approach for widespread cylindrical cell testing in both academic and commercial settings to help improve the safety and performance of lithium-ion batteries.
AV  - public
TI  - Investigating the Performance and Safety of Li-Ion Cylindrical Cells Using Acoustic Emission and Machine Learning Analysis
PB  - The Electrochemical Society
Y1  - 2024/06/19/
N1  - As the Version of Record of this article is going to be/has been published on a gold open access basis under a CC 4.0 licence, this Accepted Manuscript is available for reuse under the applicable CC licence immediately.

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ID  - discovery10193681
JF  - Journal of The Electrochemical Society
ER  -