Thermal monitoring of lithium-ion batteries based on machine learning and fibre Bragg grating sensors

• Published in: Transactions of the Institute of Measurement and Control Vol. 45; no. 8; pp. 1570 - 1578
• Main Authors: Liu, Shiyun, Li, Kang
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.05.2023
• Subjects: linear/nonlinear model; FBG sensor; Lithium-ion battery thermal management; fast recursive algorithm
• ISSN: 0142-3312; 1477-0369
• DOI: 10.1177/01423312221143776

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.