电堆工作过程中各个单体电压的一致性是提高其性能的关键,在3 kW电堆台架实验数据的基础上,利用人工神经网络（ANN）模型,对空气流量、氢气压力、过量空气系数等气体供给参数进行优化,并对优化前后电堆的功率特性、一致性特性进行对比分析。结果表明：当氢气入口压力为0.128 MPa、空气入口流量为11 g/s、过量空气系数为6.4时,电堆电压差异系数CV及功率参数可达到最佳。
Abstract
The consistency of each cell's voltage is the key to improving PEMFC stack performance. Based on the experimental data of 3 kW stack, this paper utilizes an artificial neural network（ANN） model to optimize the gas supply parameters, including airflow rate, hydrogen pressure and excess air coefficient. Simultaneously, the power and consistency characteristics of the stack before and after optimization are compared and analyzed. The results show that the CV and power parameters can achieve best when the inlet pressure of hydrogen is 0.128 MPa, the inlet flow rate of air is 11 g/s, and the excess air coefficient is 6.4.
关键词
质子交换膜燃料电池 /
耐久性 /
一致性 /
人工神经网络 /
水桶效应
Key words
proton exchange membrane fuel cell /
durability /
consistency /
artificial neural network /
bucket effect
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基金
国家重点研发计划（2017YFB0102802）
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