Fordham, Arthur;
Joo, Seung-Bin;
Owen, Rhodri Ellis;
Galiounas, Elias;
Buckwell, Mark;
Brett, Dan;
Shearing, Paul;
... Robinson, James B; + view all
(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.
Preview |
Text
Robinson_Fordham+et+al_2024_J._Electrochem._Soc._10.1149_1945-7111_ad59c9.pdf Download (2MB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | Investigating the Performance and Safety of Li-Ion Cylindrical Cells Using Acoustic Emission and Machine Learning Analysis |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1149/1945-7111/ad59c9 |
| Publisher version: | http://dx.doi.org/10.1149/1945-7111/ad59c9 |
| Language: | English |
| Additional information: | 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. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10193681 |
Archive Staff Only
 |
View Item |
