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. Everyone is permitted to use all or part of the original content in this article, provided that they adhere to all the terms of the applicable licence referred to in the article – either https://creativecommons.org/licenses/by/4.0/ or https://creativecommons.org/licenses/by-nc-nd/4.0/ Although reasonable endeavours have been taken to obtain all necessary permissions from third parties to include their copyrighted content within this article, their full citation and copyright line may not be present in this Accepted Manuscript version. Before using any content from this article, please refer to the Version of Record on IOPscience once published for full citation and copyright details, as permissions may be required. All third party content is fully copyright protected and is not published on a gold open access basis under a CC licence, unless that is specifically stated in the figure caption in the Version of Record. 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