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Journal of Applied Mathematics
Volume 2013, Article ID 538237, 7 pages
http://dx.doi.org/10.1155/2013/538237
Research Article

Neural Network Predictive Control for Vanadium Redox Flow Battery

1Automation Department, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2Institute of Fuel Cell, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received 6 July 2013; Revised 23 September 2013; Accepted 25 September 2013

Academic Editor: Baocang Ding

Copyright © 2013 Hai-Feng Shen 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.

Abstract

The vanadium redox flow battery (VRB) is a nonlinear system with unknown dynamics and disturbances. The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system. Too low or too high flowrate is unfavorable for the safety and performance of VRB. This paper presents a neural network predictive control scheme to enhance the overall performance of the battery. A radial basis function (RBF) network is employed to approximate the dynamics of the VRB system. The genetic algorithm (GA) is used to obtain the optimum initial values of the RBF network parameters. The gradient descent algorithm is used to optimize the objective function of the predictive controller. Compared with the constant flowrate, the simulation results show that the flowrate optimized by neural network predictive controller can increase the power delivered by the battery during the discharge and decrease the power consumed during the charge.