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

Reem Salim, Mahmoud Nabag, Hassan Noura, Abbas Fardoun

Research output: Contribution to journalArticlepeer-review

79 Citations (Scopus)


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 1.2kW 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.

Original languageEnglish
Pages (from-to)26-34
Number of pages9
JournalRenewable energy
Publication statusPublished - Oct 1 2015


  • Fuel cells
  • PSO
  • Parameter identification
  • Proton exchange membrane
  • Thermal model
  • Voltage model

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment


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