Predictive control of SOFC based on a GA-RBF neural network model

被引：100

作者：

Wu, Xiao-Juan ^{[1
]}

Zhu, Xin-Jian ^{[1
]}

Cao, Guang-Yi ^{[1
]}

Tu, Heng-Yong ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Inst Fuel Cell, Dept Automat, Shanghai 200030, Peoples R China

来源：

JOURNAL OF POWER SOURCES | 2008年 / 179卷 / 01期

关键词：

solid oxide fuel cell (SOFC); fuel utilization; load transient; model predictive control (MPC);

D O I：

10.1016/j.jpowsour.2007.12.036

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

Transients in a load have a significant impact on the performance and durability of a solid oxide fuel cell (SOFC) system. One of the main reasons is that the fuel utilization changes drastically due to the load change. Therefore, in order to guarantee the fuel utilization to operate within a safe range, a nonlinear model predictive control (MPC) method is proposed to control the stack terminal voltage as a proper constant in this paper. The nonlinear predictive controller is based on an improved radial basis function (RBF) neural network identification model. During the process of modeling, the genetic algorithm (GA) is used to optimize the parameters of RBF neural networks. And then a nonlinear predictive control algorithm is applied to track the voltage of the SOFC. Compared with the constant fuel utilization control method, the simulation results show that the nonlinear predictive control algorithm based on the GA-RBF model performs much better. (C) 2007 Elsevier B.V. All rights reserved.

引用

页码：232 / 239

页数：8

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