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Many approaches have been developed for battery SOC estimation based on the measurable electrical signals. However, the electrical measurements are usually corrupted by sensor noise and are also limited by insulation, corrosion, and electromagnetic compatibility. To address these problems, fiber Bragg grating (FBG) sensors are used to replace thermocouple sensors, the battery SOC therefore can be estimated based on the measurement of the strain information. In order to train the SOC estimation model for lithium-ion batteries, an FBG sensor\u2013based battery SOC estimator, namely, ELM-RBFNN, is developed using the radial basis function neural network (RBFNN) identified by extreme learning machine (ELM). According to the ELM principle, the ELM-RBFNN model randomly selects the radial basis function (RBF) centers and determines the parameters of the hidden-layer nodes without any manual adjustment. The ELM-RBFNN estimation model has the advantages of fast learning speed, global optimization, and excellent generalization. Experimental results confirm that compared to the linear estimator and the polynomial estimator, the developed SOC estimator is able to produce more accurate results.<\/jats:p>","DOI":"10.1177\/01423312221144475","type":"journal-article","created":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T01:33:41Z","timestamp":1674005621000},"page":"3088-3097","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Extreme learning machine\u2013radial basis function neural network\u2013based state-of-charge estimation of lithium-ion batteries assisted with fiber Bragg grating sensor measurements"],"prefix":"10.1177","volume":"47","author":[{"given":"Yi","family":"Yang","sequence":"first","affiliation":[{"name":"State Grid Jiangsu Electric Power Research Institute, China"}]},{"given":"Qi","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Grid Jiangsu Electric Power Research Institute, China"}]},{"given":"Min","family":"Zheng","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University Shanghai, China"}]},{"given":"Qun","family":"Niu","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9128-7240","authenticated-orcid":false,"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University Shanghai, China"}]},{"given":"Dajun","family":"Du","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University Shanghai, China"}]},{"given":"Lei","family":"Gao","sequence":"additional","affiliation":[{"name":"State Grid Jiangsu Electric Power Research Institute, China"}]},{"given":"Xiaodong","family":"Yuan","sequence":"additional","affiliation":[{"name":"State Grid Jiangsu Electric Power Research Institute, China"}]}],"member":"179","published-online":{"date-parts":[[2023,1,17]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPEL.2013.2243918"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPEL.2014.2361755"},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/ente.201500514"},{"key":"e_1_3_2_5_1","first-page":"384","article-title":"Electrochemical model-based state of charge and capacity estimation for a composite electrode lithium-ion battery","volume":"24","author":"Bartlett A","year":"2016","unstructured":"Bartlett A, Marcicki J, Onori S, et al. 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