Modeling of a single cell micro proton exchange membrane fuel cell by a new hybrid neural network method

In this research, a novel hybrid model is developed based on neural network with high capability to predict the behavior of a micro proton exchange membrane fuel cell (micro-PEMFC) at various operational conditions. The proposed model is a combination of Group Method of Data Handling type neural networks and Genetic Algorithm (GMDH-GA). Genetic algorithm was used to optimize the correlation parameters to improve the accuracy of model. Input variables including humidity, temperature, electrical current and the oxygen and hydrogen flow rates were considered to predict cell performance. First, the GMDH-GA model was trained using the experimental input and output data set, then the trained model was tested using an independent data set. The model was implemented to determine the performance curves of a micro-PEMFC at different operating settings. The model could obtain the optimized values for the input variables corresponding to the value of objective function. Results showed a consistency between experimental data and the data made by the model. Therefore, it is indicated that the GMDH-GA method is an effective method, which can predict the performance of micro-cell with high accuracy.