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Accurate estimation of the state-of-charge (SOC) of a battery pack is important in managing the health and safety of battery packs. This paper proposes a compact radial basis function (RBF) neural model to estimate the state-of-charge (SOC) of lithium battery packs. Firstly, a suitable input set strongly correlated with the package SOC is identified from directly measured voltage, current, and temperature signals by a fast recursive algorithm (FRA). Secondly, a RBF neural model for battery pack SOC estimation is constructed using the FRA strategy to prune redundant hidden layer neurons. Then, the particle swarm optimization (PSO) algorithm is used to optimize the kernel parameters. Finally, a conventional RBF neural network model, an improved RBF neural model using the two stage method, and a least squares support vector machine (LSSVM) model are also used to estimate the battery SOC as a comparative study. Simulation results show that generalization error of SOC estimation using the novel RBF neural network model is less than half of that using other methods. Furthermore, the model training time is much less than the LSSVM method and the improved RBF neural model using the two-stage method.<\/jats:p>","DOI":"10.1155\/2020\/8840240","type":"journal-article","created":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T14:57:58Z","timestamp":1606834678000},"page":"1-10","source":"Crossref","is-referenced-by-count":25,"title":["State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9128-7240","authenticated-orcid":true,"given":"Li","family":"Zhang","sequence":"first","affiliation":[{"name":"Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1123-0306","authenticated-orcid":true,"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 200072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2979-1507","authenticated-orcid":true,"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 200072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihuan","family":"Li","sequence":"additional","affiliation":[{"name":"University of Leeds, Leeds LS2 9JT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minrui","family":"Fei","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanjun","family":"Guo","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kang","family":"Li","sequence":"additional","affiliation":[{"name":"University of Leeds, Leeds LS2 9JT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2015.02.099"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2019.05.042"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2019.04.065"},{"issue":"1","key":"4","first-page":"199","article-title":"Improved real-time state-of-charge estimation of lifepo4 battery based on a novel thermoelectric model","volume":"64","author":"Z. 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