Proton Exchange Membrane Fuel Cells Modeling Using Chaos Game Optimization Technique

For the precise simulation performance, the accuracy of fuel cell modeling is important. Therefore, this paper presents a developed optimization method called Chaos Game Optimization Algorithm (CGO). The developed method provides the ability to accurately model the proton exchange membrane fuel cell (PEMFC). The accuracy of the model is tested by comparing the simulation results with the practical measurements of several standard PEMFCs such as Ballard Mark V, AVISTA SR-12.5 kW, and 6 kW of the Nedstack PS6 stacks. The complexity of the studied problem stems from the nonlinearity of the PEMFC polarization curve that leads to a nonlinear optimization problem, which must be solved to determine the seven PEMFC design variables. The objective function is formulated mathematically as the total error squared between the laboratory measured terminal voltage of PEMFC and the estimated terminal voltage yields from the simulation results using the developed model. The CGO is used to find the best way to fulfill the preset requirements of the objective function. The results of the simulation are tested under different temperature and pressure conditions. Moreover, the results of the proposed CGO simulations are compared with alternative optimization methods showing higher accuracy.

[1]  Ahmed Fathy,et al.  A novel approach based on hybrid vortex search algorithm and differential evolution for identifying the optimal parameters of PEM fuel cell , 2020 .

[2]  Hany M. Hasanien,et al.  A novel hybrid GWO-PSO optimization technique for optimal reactive power dispatch problem solution , 2020 .

[3]  A. A. El-Fergany,et al.  Improved performance of PEM fuel cells stack feeding switched reluctance motor using multi-objective dragonfly optimizer , 2018, Neural Computing and Applications.

[4]  Hany M. Hasanien,et al.  Semi-empirical PEM fuel cells model using whale optimization algorithm , 2019, Energy Conversion and Management.

[5]  D. B. Talange,et al.  Modeling and performance evaluation of PEM fuel cell by controlling its input parameters , 2017 .

[6]  Gamze Karanfil,et al.  Importance and applications of DOE/optimization methods in PEM fuel cells: A review , 2019, International Journal of Energy Research.

[7]  Attia A. El-Fergany,et al.  Extracting optimal parameters of PEM fuel cells using Salp Swarm Optimizer , 2018 .

[8]  S. Chan,et al.  Evaluation criterion of different flow field patterns in a proton exchange membrane fuel cell , 2020, Energy Conversion and Management.

[9]  P. Pei,et al.  Optimal interval of air stoichiometry under different operating parameters and electrical load conditions of proton exchange membrane fuel cell , 2020 .

[10]  Ziad M. Ali,et al.  A Secured Energy Management Architecture for Smart Hybrid Microgrids Considering PEM-Fuel Cell and Electric Vehicles , 2020, IEEE Access.

[11]  Attia A. El-Fergany,et al.  Steady-State Modeling of Fuel Cells Based on Atom Search Optimizer , 2019, Energies.

[12]  Minfang Han,et al.  Comparison of off-gas utilization modes for solid oxide fuel cell stacks based on a semi-empirical parametric model , 2020 .

[13]  Hany M. Hasanien,et al.  Transient search optimization: a new meta-heuristic optimization algorithm , 2020, Applied Intelligence.

[14]  A. Gandomi,et al.  Imperialist competitive algorithm combined with chaos for global optimization , 2012 .

[15]  Haozhong Huang,et al.  Design and modeling of PEM fuel cell based on different flow fields , 2020 .

[16]  Pierre R. Roberge,et al.  Development and application of a generalised steady-state electrochemical model for a PEM fuel cell , 2000 .

[17]  Zehui Shao,et al.  Shark Smell Optimizer applied to identify the optimal parameters of the proton exchange membrane fuel cell model , 2019, Energy Conversion and Management.

[18]  A. Sohani,et al.  Application based multi-objective performance optimization of a proton exchange membrane fuel cell , 2020 .

[19]  Siamak Talatahari,et al.  Optimization of constrained mathematical and engineering design problems using chaos game optimization , 2020, Comput. Ind. Eng..

[20]  Hany M. Hasanien,et al.  Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells , 2019, International Journal of Energy Research.

[21]  N. Rajasekar,et al.  A comprehensive review on parameter estimation techniques for Proton Exchange Membrane fuel cell modelling , 2018, Renewable and Sustainable Energy Reviews.

[22]  Emmanuel Godoy,et al.  Identification of a PEMFC fractional order model , 2017 .

[23]  Sousso Kelouwani,et al.  Benchmark of proton exchange membrane fuel cell parameters extraction with metaheuristic optimization algorithms , 2019, Energy.

[24]  Hany M. Hasanien,et al.  Equilibrium optimizer for parameter extraction of a fuel cell dynamic model , 2021 .

[25]  I. Saleh,et al.  Simplified mathematical model of proton exchange membrane fuel cell based on horizon fuel cell stack , 2016 .

[26]  Sachin Chugh,et al.  Experimental and modelling studies of low temperature PEMFC performance , 2020, International Journal of Hydrogen Energy.

[27]  Mohamed Derbeli,et al.  Robust high order sliding mode control for performance improvement of PEM fuel cell power systems , 2020 .

[28]  Guobin Zhang,et al.  Three-dimensional multi-phase simulation of PEMFC at high current density utilizing Eulerian-Eulerian model and two-fluid model , 2018, Energy Conversion and Management.

[29]  Z. Geem,et al.  Parameter Estimation for a Proton Exchange Membrane Fuel Cell Model Using GRG Technique , 2016 .

[30]  Attia A. El-Fergany,et al.  Electrical characterisation of proton exchange membrane fuel cells stack using grasshopper optimiser , 2018 .