The parameter identification of the Nexa 1.2 kW PEMFC's model using particle swarm optimization

• We present a fast heuristic based offline parameter identification approach for PEMFCs.
• We identify 18 modeling parameters of the fuel cell system using basic fitting.
• We optimize the model using particle swarm optimization.
• We validate the results using actual experimental data.
• The obtained results are excellent while requiring minimal mathematical effort.

People's extensive and ignorant lifestyles impose an increasing amount of destruction on the environment, which lead to an increased governmental and research interest towards the development and use of green technology such as fuel cells. Fuel cells are recently receiving a major share of research interest due to their promising features. This paper presents an offline parameter identification approach based on particle swarm optimization (PSO) to identify the mathematical modeling parameters of the Nexa 1.2 kW proton exchange membrane fuel cell (PEMFC) system. The goal of this work is not to get a new technique in modeling, but rather to obtain a very good model of the PEMFC system using a simple and fast heuristic approach that requires minimal mathematical effort. This model can then be utilized to perform further analysis and fault diagnosis studies on PEMFCs. The proposed approach uses basic fitting to determine some of the initial values for the PSO, while the rest of the initial values are set to be chosen randomly. The developed model is then successfully validated using actual experimental data sets.