Parameter optimization for a PEMFC model with a hybrid genetic algorithm Mo, Zhi-Jun [Shanghai Jiaotong University (China). Fuel Cell Institute]; Guangxi University, Nanning (China). School of Electrical Engineering]; Zhu, Xin-Jian; Cao, Guang-Yi [Shanghai Jiaotong University (China). Fuel Cell Institute]; Wei, Ling-Yun [Shanghai Jiaotong University (China). State Key Lab. of Vibration, Shock and Noise] 30 DIRECT ENERGY CONVERSION; PROTON EXCHANGE MEMBRANE FUEL CELLS; OPTIMIZATION; ALGORITHMS; DESIGN; PARAMETRIC ANALYSIS Many steady-state models of polymer electrolyte membrane fuel cells (PEMFC) have been developed and published in recent years. However, models which are easy to be solved and feasible for engineering applications are few. Moreover, rarely the methods for parameter optimization of PEMFC stack models were discussed. In this paper, an electrochemical-based fuel cell model suitable for engineering optimization is presented. Parameters of this PEMFC model are determined and optimized by means of a niche hybrid genetic algorithm (HGA) by using stack output-voltage, stack demand current, anode pressure and cathode pressure as input-output data. This genetic algorithm is a modified method for global optimization. It provides a new architecture of hybrid algorithms, which organically merges the niche techniques and Nelder-Mead's simplex method into genetic algorithms (GAs). Calculation results of this PEMFC model with optimized parameters agreed with experimental data well and show that this model can be used for the study on the PEMFC steady-state performance, is broader in applicability than the earlier steady-state models. HGA is an effective and reliable technique for optimizing the model parameters of PEMFC stack. (author) United Kingdom 2005-07-01 English Journal Article Journal Name: International Journal of Energy Research; Journal Volume: 30; Journal Issue: 8 Medium: X; Size: page(s) 585-597 https://doi.org/ [] 8 30 Journal ID: ISSN 0363-907X; IJERDN; TRN: GB0551632 GB 2010-12-31 20846878