eprintid: 10193681
rev_number: 9
eprint_status: archive
userid: 699
dir: disk0/10/19/36/81
datestamp: 2024-06-21 09:29:35
lastmod: 2024-06-21 09:29:35
status_changed: 2024-06-21 09:29:35
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Fordham, Arthur
creators_name: Joo, Seung-Bin
creators_name: Owen, Rhodri Ellis
creators_name: Galiounas, Elias
creators_name: Buckwell, Mark
creators_name: Brett, Dan
creators_name: Shearing, Paul
creators_name: Jervis, Rhodri
creators_name: Robinson, James B
title: Investigating the Performance and Safety of Li-Ion Cylindrical Cells Using Acoustic Emission and Machine Learning Analysis
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F43
note: 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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abstract: 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.
date: 2024-06-19
date_type: published
publisher: The Electrochemical Society
official_url: http://dx.doi.org/10.1149/1945-7111/ad59c9
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2289240
doi: 10.1149/1945-7111/ad59c9
lyricists_name: Robinson, James
lyricists_name: Jervis, John
lyricists_name: Owen, Rhodri
lyricists_id: ROBIN60
lyricists_id: JRJER29
lyricists_id: ROWEN61
actors_name: Robinson, James
actors_id: ROBIN60
actors_role: owner
full_text_status: public
publication: Journal of The Electrochemical Society
issn: 0013-4651
citation:        Fordham, Arthur;    Joo, Seung-Bin;    Owen, Rhodri Ellis;    Galiounas, Elias;    Buckwell, Mark;    Brett, Dan;    Shearing, Paul;         ... Robinson, James B; + view all <#>        Fordham, Arthur;  Joo, Seung-Bin;  Owen, Rhodri Ellis;  Galiounas, Elias;  Buckwell, Mark;  Brett, Dan;  Shearing, Paul;  Jervis, Rhodri;  Robinson, James B;   - view fewer <#>    (2024)    Investigating the Performance and Safety of Li-Ion Cylindrical Cells Using Acoustic Emission and Machine Learning Analysis.                   Journal of The Electrochemical Society        10.1149/1945-7111/ad59c9 <https://doi.org/10.1149/1945-7111%2Fad59c9>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10193681/2/Robinson_Fordham%2Bet%2Bal_2024_J._Electrochem._Soc._10.1149_1945-7111_ad59c9.pdf