Statistical design of experiment approach for modeling and optimization of PEM fuel cell

Abstract

ABSTRACTPolymer electrolyte membrane (PEM) fuel cell has many input factors and it is very difficult to find which input factor affects response or output factor significantly. The general method of changing one factor at a time is statistically not correct because the interaction of the factors also affects the response in most of the cases. Mathematical and simulation models are important tools for designing and analysis of fuel cell-based systems. In this paper, first, a protocol for development of a 25-cm2 active area, high performance, PEM fuel cell is presented and then its simulation model is developed using the first principle in MATLAB SIMULINK. Full factorial statistical design of experiment methodology is used to develop first- and second-order Metamodels (Mathematical model of simulation model) for PEM fuel cell to find which input factors affect the response variables significantly. Validation of the Metamodels is checked by various statistical tests, viz, normality, regression analysis, analysis of variance, and lack of fit. Steepest ascent method is used to find the maximum power delivered by PEM fuel cell within the defined ranges of input factors.