This paper presents a method for combining sequential neural-network approximation and orthogonal arrays (SNAOA) in determining the operation parameters on the performance of the proton exchange membrane fuel cell (PEMFC). An orthogonal array was first conducted to obtain the initial solution set. The results obtained from the orthogonal array were then used as the experimental data for response surface methodology (RSM) that could predict the operation parameters at discrete levels. The set was then treated as the initial training sample. Next, a back-propagation sequential neural network was trained to simulate the feasible domain for seeking optimal operation parameters of PEMFC. The size of the training sample was greatly reduced due to the use of the orthogonal array. In addition, a restart strategy was also incorporated into the SNAOA so that the searching process may have a better opportunity to reach a near global optimum. The objective is to maximize the output power of the PEMFC. The results show that, the uniform cell reaction was achieved using the split flow field in a PEMFC at different temperatures and flow ratios. The prediction model of major operation parameters influencing the performance of a PEMFC with a split flow field was also developed using the SNAOA. The optimal parameters were an operating temperature of 78C, an anode humidification temperature of 72C, an anode flow rate of 296 sccm, a cathode flow rate of 295 sccm, a split flow rate of 145 sccm and a split point 44%.

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