Investigation of polarization curve models in proton exchange membrane fuel cell (PEMFC) with particle swarm optimization(PSO) algorithm

The use of clean energy is one of the most important concerns of human beings today, and in the meantime, fuel cells have always been a part of the desired equipment in this branch. Fuel cells with proton exchange membrane (PEMFC) convert chemical energy within hydrogen (or fuel) directly into electricity, water and heat at low operation temperatures. Using polarization curve of fuel cells such as PEM fuel cells, which describes the relationship between output voltage and current density for PEM fuel cells, it can be used in applied and industrial researches. In addition, the use of particle swarm intelligence optimization (PSO) algorithms is very desirable to obtain the fitting parameters of the models. Therefore, in this study, we tried to evaluate four polarization curve models and the weaknesses and advantages of using these algorithms are investigated. According to the results, it can be noted that the use of PSO algorithm in two cases of Kim model and Hao model has increased compliance with experimental data compared to other curve fitting methods.