Prediction and analysis of the cathode catalyst layer performance of proton exchange membrane fuel cells using artificial neural network and statistical methods
Prediction and analysis of the cathode catalyst layer performance of proton exchange membrane fuel cells using artificial neural network and statistical methods
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DOI：
10.1016/j.jpowsour.2010.12.061
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发表时间：
2011-04
影响因子：
9.2
通讯作者：
N. Khajeh-Hosseini-Dalasm;S. Ahadian;K. Fushinobu;K. Okazaki;Y. Kawazoe
中科院分区：
文献类型：
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作者：
N. Khajeh-Hosseini-Dalasm;S. Ahadian;K. Fushinobu;K. Okazaki;Y. Kawazoe
建立了研究质子交换膜燃料电池(PEMFC)阴极催化层性能的数学模型。在CL凝聚模型中引入了影响阴极CL性能的众多参数，即饱和度和8个结构参数，即覆盖凝聚体的离聚体膜厚、凝聚体半径、铂和碳负载量、膜含量、气体扩散层渗透量和CL厚度。首次将人工神经网络(ANN)方法与统计方法相结合用于化学发光性能的建模、预测和分析，该性能由激活过电位表示。为了建立命名参数与激活过电位之间的关系，构建了人工神经网络。对训练好的神经网络得到的数据进行了统计分析，即均值分析(ANOM)和方差分析(ANOVA)，得到了结构参数的敏感因素及其相互组合，并得出了最佳性能。
A mathematical model was developed to investigate the cathode catalyst layer (CL) performance of a proton exchange membrane fuel cell (PEMFC). A numerous parameters influencing the cathode CL performance are implemented into the CL agglomerate model, namely, saturation and eight structural parameters, i.e., ionomer film thickness covering the agglomerate, agglomerate radius, platinum and carbon loading, membrane content, gas diffusion layer penetration content and CL thickness. For the first time, an artificial neural network (ANN) approach along with statistical methods were employed for modeling, prediction, and analysis of the CL performance, which is denoted by activation overpotential. The ANN was constructed to build the relationship between the named parameters and activation overpotential. Statistical analysis, namely, analysis of means (ANOM) and analysis of variance (ANOVA) were done on the data obtained by the trained neural network and resulted in the sensitivity factors of structural parameters and their mutual combinations as well as the best performance.