Modeling of a Fuel Cell Stack by Neural Networks Based on Particle Swarm Optimization

This paper presented a nonlinear voltage modeling procedure of a proton exchange membrane fuel cell (PEMFC) stack by neural networks based on particle swarm optimization (PSO). PEMFC stack is a complex nonlinear system which is hard to model by traditional ways. So neural networks based on particle swarm optimization (PSONN) was developed to identify a nonlinear PEMFC stack voltage model. In the paper, the PSO algorithm trained the connection weights and thresholds of neural networks, and a neural networks nonlinear autoregressive model with exogenous inputs was applied in modeling PEMFC stack voltage model. The simulation indicated that the PSONN model can efficiently approach the behavior of a PEMFC stack.