Galiounas, Elias;
(2024)
Data-driven lithium-ion battery monitoring using conventional and acoustic signals.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
The use of lithium-ion batteries is now ubiquitous in modern day life, with applications ranging from portable electronics to battery electric vehicles and to large stationary energy storage units serving the national grid. Their growing use in high-energy and high-power applications imposes a formidable array of requirements, including longevity and safety, necessitating advancements in both materials characterisation and real-time diagnostics. Although leaps of progress have been made in characterisation techniques over the past 15 years, those were largely based on the use of light or neutron sources which are expensive, large (typically entire facilities), and not easily accessible. This motivates the development of alternative techniques for operando characterisation, out of which acoustic testing has attracted increasing attention in the last decade. The speed and attenuation of sound travelling through a medium are known to be influenced by its mechanical properties, making acoustic testing a promising method for the characterisation of battery chemomechanics. Furthermore, battery chemomechanics are typically correlated to a battery’s internal states, making state estimation from acoustic signals compelling. This thesis builds on the relatively small corpus of prior research on acoustic testing of batteries, exploring its potential for lab-based characterisation and for model-based state estimation. Characterisation is focused on understanding the sensitivities of the technique to operational and environmental factors, and on identifying its strengths and limitations in monitoring battery degradation. After demonstrating the significant impact of these sensitivities, the thesis explores the use of machine learning as a powerful toolset for extracting battery insights from high-dimensional acoustic datasets, in spite of the sensitivities involved.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Data-driven lithium-ion battery monitoring using conventional and acoustic signals |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2024. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10202121 |
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