Neural Network Predictive Control for Vanadium Redox Flow Battery

作者：Shen, Hai Feng^*; Zhu, Xin Jian; Shao, Meng; Cao, Hong fei

来源：Journal of Applied Mathematics, 2013, 2013: 538237.

DOI：10.1155/2013/538237

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 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.

• 出版日期2013
• 单位上海交通大学