Parameter identification of proton exchange membrane fuel cells using an improved salp swarm algorithm

• Published in: Energy conversion and management 2020-11, Vol.224, p.113341, Article 113341
• Main Authors: Sultan, Hamdy M., Menesy, Ahmed S., Kamel, Salah, Selim, Ali, Jurado, Francisco
• Format: Article
• Language: English
• Subjects: Algorithms; Electrochemistry; Energy sources; Fuel cells; Fuel technology; Improved salp swarm algorithm; Mathematical models; Objective function; Optimization; Optimization techniques; Parameter identification; Parameters identification; PEMFC; Proton exchange membrane fuel cells; Protons; Renewable energy sources; Simulation; Stacks; Statistical analysis
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2020.113341

•Proton exchange membrane fuel cells.•An improved Salp Swarm Algorithm (SSA) is proposed to enhance the conventional SSA.•The proposed ISSA has been utilized for identifying the unknown parameter values.•The Sum of Squared Errors (SSE) is adopted as the objective function.•Statistical analysis of proposed ISSA compared with the conventional SSA is carried out. Recently, Proton Exchange Membrane Fuel Cells (PEMFCs) become one of the most promising friendly renewable energy sources. Therefore, developing a mathematical model for the PEMFC is an urgent necessity for simulation and evaluation of the processes occurring inside the fuel cell (FC) stack. In this paper, a precis model, which can stimulate the electrical and electrochemical phenomenon of the PEMFC is introduced. Improved salp swarm algorithm (ISSA) is proposed to enhance the performance of the conventional SSA and avoid getting stuck on local optimum. The proposed ISSA has been utilized for identifying the unknown parameter values of PEMFC stack models. The proposed ISSA is validated on four different FC stacks and a comparison between the computed and measured results has been accomplished. The Sum of Squared Errors (SSE) between experimental and estimated voltages is adopted as the objective function which has to be minimized. For validating the goodness of the ISSA, the generated values of the unknown parameters and the value of SSE using the ISSA-based PEMFC model are compared with the corresponding ones obtained by other optimization techniques. Furthermore, statistical analysis of proposed ISSA compared with the conventional SSA is carried out for all the PEMFC stacks involved in this work. The simulation results under various conditions of operation and the statistical results proved the stability and reliability of ISSA in comparison with recently utilized algorithms.