Neural Network Modeling for Proton Exchange Membrane Fuel Cell (PEMFC)

This paper presents the artificial intelligence techniques to control a proton exchange membrane fuel cell system process using particularly a methodology of dynamic neural network. The focus of this study is to derive a non-parametric empirical model. Also it will include process variations to estimate the performance of fuel cells without extensive calculations. The ANN model has been validated with experimental results. Experimental results are used and presented for identifying the proposed approach, which is useful in improving performance for PEMFC and developing electrical system on advanced vehicles and ships. All experimental data are fitted very well with the ANN models over a wide operating range. The ANN models can be used to investigate the influence of process variables for design optimization of fuel cells, stacks, and complete fuel cell power system.