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

Aug 5, 2026

Upload by Jiven

Lithium-ion batteries are widely used in portable electronics, electric vehicles, and energy storage, but safe operation under both normal and abnormal conditions remains a central challenge. Effective temperature management is essential to improve lifetime performance and avoid thermal failures. This work investigates fibre Bragg grating (FBG) sensor technology combined with machine learning for battery temperature monitoring. Linear and nonlinear models are evaluated to estimate temperature variations reliably and accurately.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Temperature management challenges

Limitations of conventional thermal sensing

Existing sensorless and optical fibre approaches

Article-Measurement Thermal monitoring of lithium-ion batteries based on machine learning and fibre Bragg grating sensors Transactions of the Institute of Measurement and Control 1–9 􀀂 The Author(s) 2023 Article reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/01423312221143776](DOI: 10.1177/01423312221143776)[ ](DOI: 10.1177/01423312221143776)[journals.sagepub.com/home/tim](journals.sagepub.com/home/tim) Shiyun Liu and Kang Li 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. Keywords Lithium-ion battery thermal management, FBG sensor, fast recursive algorithm, linear/nonlinear model Introduction Lithium-ion batteries (LiBs) are one of the most promising energy storage techniques in power systems and mobile facilities by virtue of its higher power and energy densities, longer cycle life and lower self-discharge rate qualities (Kang et al., 2014) . These features are highly suitable for various types of portable electronics devices, electrical vehicles (EV), energy storage applications, aircrafts and even aerospace applications (Li et al., 2020, 2021) . However, the extensive applications are usually operated under a wide range of extreme environments, like high-altitude/latitude, elevated temperatures and high charge/discharge rates (Richardson, 2016) . This is likely to cause a series of issues in battery thermal management. For instance, when the environment temperature is below 0􀀃 C, the depth of discharge (DoD) and the power output of the battery decrease significantly (Xie et al., 2022) . However, operating at high temperatures (40􀀃 C) will also accelerate the chemical or mechanical reaction happening between electrolyte and the electrodes (Amine et al., 2005) . In this case, solid electrolyte interphase (SEI) will be formed, which leads to the impedance increase at the anode and active lithium reduction in the battery (Liu et al., 2020) . Furthermore, the decomposition of electrolyte results in active lithium loss. The aforementioned aging mechanisms lead to capacity fade (Liu et al., 2020) . If the generated heat cannot be sufficiently dissipated, the battery internal temperature goes beyond the acceptable scope rapidly and then results in a great many exothermic events within the battery, such as fires, venting and electrolyte leakage (Feng et al., 2015; Liao et al., 2019) . Moreover, thenonuniform thermal distribution inside the battery will result in the issues of inconsistent electrochemical processes and thus decrease the battery pack cycle life (Olabi et al., 2022) . In this sense, maintaining the operating temperature of battery under safe and optimal conditions is extremely important. It is necessary to monitor and predict temperature variations through a reliable and effective method. The conventional ways to detect the thermal features are to use flexible thin-film thermocouples (TFTCs) (Li et al., 2013) or micro-thin-film resistance temperature detectors (RTD) (Lee et al., 2011) attached to the surface or embedded into battery

FAQs

Why is temperature monitoring critical for lithium-ion battery safety?

Battery operation must remain within safe and optimal temperature ranges, since abnormal temperatures accelerate aging and can trigger severe thermal events such as fires, venting, or electrolyte leakage.

What sensing approach is used for temperature monitoring in the paper?

The paper explores fibre Bragg grating (FBG) sensors coupled with machine learning to estimate battery temperature variations.

How do the results assess temperature estimation performance?

The study compares linear and nonlinear machine learning models and confirms that the proposed methods estimate temperature variations reliably and accurately.

Aug 5, 2026

Upload by Jiven