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The power cell type under analysis in this research is a LFP (Lithium Iron Phosphate LiFePO4). The purpose of this research is to predict the power cell State of Charge (SOC) by creating a hybrid intelligent model. All the operating points measured from a real system during a capacity confirmation test make up the dataset used to obtain the model. This dataset is clustered to obtain different behavior groups, which are used to develop the final model. Different regression techniques such as polynomial regression, support vector regression (SVR), and artificial neural networks (ANN) have been implemented for each cluster. A combination of these methods is performed to achieve an intelligent model. The SOC of the power cell can be predicted by this hybrid intelligent model, and good results are achieved.<\/jats:p>","DOI":"10.1155\/2017\/9640546","type":"journal-article","created":{"date-parts":[[2017,8,28]],"date-time":"2017-08-28T21:01:54Z","timestamp":1503954114000},"page":"1-10","source":"Crossref","is-referenced-by-count":30,"title":["Power Cell SOC Modelling for Intelligent Virtual Sensor Implementation"],"prefix":"10.1155","volume":"2017","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-9740-6477","authenticated-orcid":true,"given":"Jos\u00e9-Luis","family":"Casteleiro-Roca","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, University of A Coru\u00f1a, A Coru\u00f1a, Spain"}]},{"given":"Esteban","family":"Jove","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, University of A Coru\u00f1a, A Coru\u00f1a, 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