SUMMARYMany steady-state models of polymer electrolyte membrane fuel cells (PEMFC) have been developed and published in recent years. However, models which are easy to be solved and feasible for engineering applications are few. Moreover, rarely the methods for parameter optimization of PEMFC stack models were discussed. In this paper, an electrochemical-based fuel cell model suitable for engineering optimization is presented. Parameters of this PEMFC model are determined and optimized by means of a niche hybrid genetic algorithm (HGA) by using stack output-voltage, stack demand current, anode pressure and cathode pressure as input-output data. This genetic algorithm is a modified method for global optimization. It provides a new architecture of hybrid algorithms, which organically merges the niche techniques and Nelder-Mead's simplex method into genetic algorithms (GAs). Calculation results of this PEMFC model with optimized parameters agreed with experimental data well and show that this model can be used for the study on the PEMFC steady-state performance, is broader in applicability than the earlier steady-state models. HGA is an effective and reliable technique for optimizing the model parameters of PEMFC stack.

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Abstract：A practical method of estimation for the internal-resistance of polymer electrolyte membrane fuel cell (PEMFC) stack was adopted based on radial basis function (RBF) neural networks. In the training process, k-means clustering algorithm was applied to select the network centers of the input training data. Furthermore, an equivalent electrical-circuit model with this internal-resistance was developed for investigation on the stack. Finally using the neural networks model of the equivalent resistance in the PEMFC stack, the simulation results of the estimation of equivalent internal-resistance of PEMFC were presented. The results show that this electrical PEMFC model is effective and is suitable for the study of control scheme, fault detection and the engineering analysis of electrical circuits.