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. 30 kW                    .      VIP(Variable Importance on PLS Projections)    ,       .  ,     (cathode)    ,     .                  .               ,    ,          . Abstract − We present two data-driven modeling methods, partial least square (PLS) and artificial neural network (ANN),to predict the major operating and performance variables of a polymer electrolyte membrane (PEM) fuel cell stack. PLSand ANN models were constructed using the experimental data obtained from the testing of a 30 kW-class PEM fuel cellstack, and then were compared with each other in terms of their prediction and computational performances. To reducethe complexity of the models, we combined a variables importance on PLS projection (VIP) as a variable selectionmethod into the modeling procedure in which the predictor variables are selected from a set of input operation variables.The modeling results showed that the ANN models outperformed the PLS models in predicting the average cell voltageand cathode outlet temperature of the fuel cell stack. However, the PLS models also offered satisfactory prediction per-formances although they can only capture linear correlations between the predictor and output variables. Depending onthe degree of modeling accuracy and speed, both ANN and PLS models can be employed for performance predictions,offline and online optimizations, controls, and fault diagnoses in the field of PEM fuel cell designs and operations. Key words: Fuel Cell, Polymer Electrolyte Membrane, Back-box Modeling, Partial Least Squares, Artificial Neural Networks

Modeling of a PEM Fuel Cell Stack using Partial Least Squares and Artificial Neural Networks