Use of metamodeling optimal approach promotes the performance of proton exchange membrane fuel cell (PEMFC)

The main purpose of this paper is to realize a metamodeling optimal approach that can be employed cost-efficiently and systematically to improve the performance of power density in PEMFC. First, an power density database is generated that corresponds to different levels of PEMFC unit operating parameters (factors) using the Design of Experiment (DoE) scheme, screening experiments, and Taguchi Orthogonal Array (OA). Then, metamodel is constructed by Radial Basis Function Neural Network (RBFNN) to represent the PEMFC system as a nonlinear complex model. The cross-validation procedure is implemented to prove the metamodel correctness and generalization. Moreover, Genetic Algorithm (GA) is applied to avoid local point and reduce time consumption to search the global optimum in promoting the performance of design factors. The proposed optimization methodology from experimental results provides an effective and economical approach to improve the performance of fuel cell unit and can be easy extended to the fuel cell stack system in energy applications.