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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. <\/jats:p>","DOI":"10.1177\/01423312221143776","type":"journal-article","created":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T12:22:57Z","timestamp":1674562977000},"page":"1570-1578","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":15,"title":["Thermal monitoring of lithium-ion batteries based on machine learning and fibre Bragg grating sensors"],"prefix":"10.1177","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4447-9319","authenticated-orcid":false,"given":"Shiyun","family":"Liu","sequence":"first","affiliation":[{"name":"School of Electronic and Electrical Engineering, University of Leeds, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6657-0522","authenticated-orcid":false,"given":"Kang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic and Electrical Engineering, University of Leeds, UK"}]}],"member":"179","published-online":{"date-parts":[[2023,1,24]]},"reference":[{"key":"bibr1-01423312221143776","doi-asserted-by":"publisher","DOI":"10.1016\/j.elecom.2005.04.018"},{"key":"bibr2-01423312221143776","doi-asserted-by":"publisher","DOI":"10.1002\/0471716243"},{"key":"bibr3-01423312221143776","doi-asserted-by":"publisher","DOI":"10.1088\/0957-0233\/26\/1\/015102"},{"key":"bibr4-01423312221143776","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2015.04.118"},{"key":"bibr5-01423312221143776","doi-asserted-by":"publisher","DOI":"10.1016\/j.ohx.2018.04.001"},{"key":"bibr6-01423312221143776","doi-asserted-by":"publisher","DOI":"10.3390\/en10070838"},{"key":"bibr7-01423312221143776","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2021.111514"},{"key":"bibr8-01423312221143776","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhydene.2022.01.008"},{"key":"bibr9-01423312221143776","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2014.04.038"},{"key":"bibr10-01423312221143776","doi-asserted-by":"publisher","DOI":"10.3390\/molecules27103119"},{"key":"bibr11-01423312221143776","doi-asserted-by":"publisher","DOI":"10.3390\/s111009942"},{"key":"bibr12-01423312221143776","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2005.852557"},{"key":"bibr13-01423312221143776","doi-asserted-by":"publisher","DOI":"10.1177\/0142331220966425"},{"key":"bibr14-01423312221143776","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.116410"},{"key":"bibr15-01423312221143776","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2022.119787"},{"key":"bibr16-01423312221143776","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2013.04.117"},{"key":"bibr17-01423312221143776","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2019.226879"},{"key":"bibr18-01423312221143776","first-page":"1","volume":"2018","author":"Liu K","year":"2018","journal-title":"Complexity"},{"key":"bibr19-01423312221143776","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2941747"},{"key":"bibr20-01423312221143776","doi-asserted-by":"publisher","DOI":"10.3390\/en15113930"},{"key":"bibr21-01423312221143776","unstructured":"MathWorks (2022) A method for describing nonlinear relationships and making predictions from experimental data. 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