Liu, S and Li, K orcid.org/0000-0001-6657-0522 (2023) Thermal monitoring of lithium-ion batteries based on machine learning and fibre Bragg grating sensors. Transactions of the Institute of Measurement and Control. ISSN 0142-3312
Abstract
Lithium-ion batteries (LiBs) are well-known power sources due to their higher power and energy densities, longer cycle life and lower self-discharge rate features. Hence, these batteries have been widely used in various portable electronic devices, electric vehicles and energy storage systems. The primary challenge in applying a Lithium-ion battery (LiB) system is to guarantee its operation safety under both normal and abnormal operating conditions. To achieve this, temperature management of batteries should be placed as a priority for the purpose of achieving better lifetime performance and preventing thermal failures. In this paper, fibre Bragg Grating (FBG) sensor technology coupling with machine learning (ML) has been explored for battery temperature monitoring. The results based on linear and nonlinear models have confirmed that the novel methods can estimate temperature variations reliably and accurately.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © The Author(s) 2023. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) |
Keywords: | Lithium-ion battery thermal management, FBG sensor, fast recursive algorithm, linear/nonlinear model |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Nov 2022 13:37 |
Last Modified: | 04 Mar 2025 14:29 |
Status: | Published online |
Publisher: | SAGE |
Identification Number: | 10.1177/01423312221143776 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193245 |