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Journal of Sensors
Volume 2017, Article ID 9640546, 10 pages
Research Article

Power Cell SOC Modelling for Intelligent Virtual Sensor Implementation

1Department of Industrial Engineering, University of A Coruña, A Coruña, Spain
2Department of Construction and Manufacturing Engineering, University of Oviedo, Oviedo, Spain
3Department of Computer Science and System Engineering, University of La Laguna, Tenerife, Spain
4Department of Mining Exploitation and Prospecting, University of Oviedo, Oviedo, Spain

Correspondence should be addressed to Fernando Sánchez-Lasheras; se.ivoinu@odnanrefzehcnas

Received 8 February 2017; Revised 15 June 2017; Accepted 10 July 2017; Published 28 August 2017

Academic Editor: Eduard Llobet

Copyright © 2017 José-Luis Casteleiro-Roca et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Batteries are one of the principal components in electric vehicles and mobile electronic devices. They operate based on electrochemical reactions, which are exhaustively tested to check their behavior and to determine their characteristics at each working point. One remarkable issue of batteries is their complex behavior. 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.