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Neural Network Predictive Control for Vanadium Redox Flow Battery

Author

Listed:
  • Hai-Feng Shen
  • Xin-Jian Zhu
  • Meng Shao
  • Hong-fei Cao

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.

Suggested Citation

  • Hai-Feng Shen & Xin-Jian Zhu & Meng Shao & Hong-fei Cao, 2013. "Neural Network Predictive Control for Vanadium Redox Flow Battery," Journal of Applied Mathematics, John Wiley & Sons, vol. 2013(1).
    • Handle: RePEc:wly:jnljam:v:2013:y:2013:i:1:n:538237
    DOI: 10.1155/2013/538237
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    References listed on IDEAS

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    1. Huang, Ke-Long & Li, Xiao-gang & Liu, Su-qin & Tan, Ning & Chen, Li-quan, 2008. "Research progress of vanadium redox flow battery for energy storage in China," Renewable Energy, Elsevier, vol. 33(2), pages 186-192.
    2. Vynnycky, M., 2011. "Analysis of a model for the operation of a vanadium redox battery," Energy, Elsevier, vol. 36(4), pages 2242-2256.
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