基于ROLS算法的RBF神经网络燃料电池电特性建模

Electric-characteristic Modeling of a Fuel Cell Based on ROLS Algorithm and RBF (Radial Based Function) Neural Network Identification Technique

提出了一种基于ROLS算法的RBF神经网络辨识建立直接甲醇燃料电池(DMFC)电特性模型的新方法。以电池的工作温度为输入量,电池的电压/电流密度为输出量,利用1200组实验数据作为训练和测试样本,建立了在不同工作温度下,电池的电压/电流密度动态响应模型。仿真结果表明采用RBF神经网络辨识建模的方法是有效的,建立的模型精度较高。

An innovative method is presented for the electric-characteristic modeling of a direct methanol fuel cell (DMFC) through the use of ROLS algorithm-based RBF (radial based function) neural network identification technique. With the operating temperature of the cell serving as an input and the voltage/electric current density of the cell serving as an output 1200 groups of experimental data were utilized as training and test samples to set up under various operating temperatures a dynamic response model of the cell voltage/electric current density. Simulation results indicate that the modeling method by using the RBF neural network identification technique is effective with the established model featuring a relative high precision.