e-MLP-based modeling of high-power PEM fuel cell stacks

Proton exchange membrane (PEM) fuel cell is one of the promising alternative energy sources with high potential to replace current fossil fuel-based economy. Performance of PEM fuel cells is dependent on several input variables, e.g., electric load, air mass flow, hydrogen mass flow, nitrogen mass flow, cathode water injection rate, etc., in a complex manner. We propose a novel enhanced multilayer perceptron (e-MLP) for modeling the high-power fuel cell stack for estimation of stack voltage and cathode temperature under variation of seven input variables. Essentially, in the proposed e-MLP, pre-processing of input data is first carried out by a nonlinear transformation using orthogonal polynomials. By extensive simulations we have shown that the proposed e-MLP-based model is not only more computationally efficient, but also provides better estimation results in terms of mean square error and correlation coefficient, compared to a recently reported 4-layer MLP-based model.