质子交换膜燃料电池的分数阶非线性状态空间模型研究

针对质子交换膜燃料电池(PEMFC)发电过程中的分数阶和非线性特性,本文提出了一种分数阶子空间辨识方法建立了PEMFC非线性状态空间模型.首先,为了降低建模复杂度,采用典型相关分析法和相关分析法确定了模型输入变量;其次,将分数阶微分理论与Hammerstein模型子空间辨识方法相结合,采用Poisson矩函数对输入输出数据进行预处理,构造了子空间辨识方法的输入输出矩阵,并引入分数阶短时记忆法减少辨识算法计算量;最后,选取多项式作为Hammerstein模型前端静态非线性环节,采用模糊遗传算法优化系统分数阶阶次和系数矩阵.仿真结果验证了算法的有效性,改进的辨识算法可以明显减小计算时间,所得PEMFC辨识模型能够准确地描述PEMFC的动态过程.

Fractional nonlinear subspace modeling for proton exchange membrane fuel cell

Aiming at the fractional and nonlinear characteristics of proton exchange membrane fuel cell (PEMFC), a fractional subspace identification method is proposed to establish a nonlinear state space model of PEMFC. Firstly, in order to reduce the modeling complexity, the model input variables are determined by the typical correlation analysis method and correlation analysis method. Secondly, combined with the fractional differential theory and the Hammerstein model subspace identification method, a Poisson moment function is adopted to preprocess the input and output data to construct the input and output matrix of the subspace identification method. Then, a fractional order short-term memory method is introduced to reduce the computational complexity of the identification algorithm. Finally, the polynomial is selected as the static nonlinear link in the front of the Hammerstein model, and a fuzzy genetic algorithm is adopted to optimize the system of fractional orders and coefficient matrices. The simulation results verified the effectiveness of the proposed identification method which can shorten the computation time, and the obtained PEMFC identification model can accurately describe the dynamic process of PEMFC.