Identifying Optimal Parameters of Proton Exchange Membrane Fuel Cell Using Water Cycle Algorithm

Recently, Fuel Cells (FCs) are considered a promising alternative to the conventional energy sources. Proton Exchange Membrane Fuel Cell (PEMFC) is the popular used type among different FC stacks. The mathematical model for PEMFC is an important issue for understanding the operation of FC and simulating its electrical and electrochemical characteristics. In this paper, the application of Water Cycle Algorithm (WCA) is developed to solve the optimization problem of estimating the unknown parameters of PEMFC model. The effectiveness of WCA is validated using two different commercial FC stacks. In addition, statistical analysis based on different metrics has been conducted to validate the goodness and stability of developed algorithm in solving the optimization problem. The results obtained from the WCA are compared with those obtained by other optimization techniques tackled with the same FC stacks. Moreover, the accuracy of optimized parameters is proved by the dynamic performance of PEMFC stacks under different operating scenarios of cell temperature and reactants’ pressures. The obtained results show the effectiveness of developed algorithm as a good competitor to other recent optimization techniques in extracting the unknown parameters of PEMFC stacks.

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

[2]  J. C. Amphlett,et al.  A model predicting transient responses of proton exchange membrane fuel cells , 1996 .

[3]  Salah Kamel,et al.  Effective Parameter Extraction of Different Polymer Electrolyte Membrane Fuel Cell Stack Models Using a Modified Artificial Ecosystem Optimization Algorithm , 2020, IEEE Access.

[4]  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.

[5]  Oguz Emrah Turgut,et al.  Optimal proton exchange membrane fuel cell modelling based on hybrid Teaching Learning Based Optimization – Differential Evolution algorithm , 2016 .

[6]  Horng-Wen Wu.  A review of recent development: Transport and performance modeling of PEM fuel cells , 2016 .

[7]  Ali Keyhani,et al.  Neural Network Modeling of Proton Exchange Membrane Fuel Cell , 2010, IEEE Transactions on Energy Conversion.

[8]  A. T. de Almeida,et al.  A parameter optimized model of a Proton Exchange Membrane fuel cell including temperature effects , 2008 .

[9]  Ardeshir Bahreininejad,et al.  Water cycle algorithm for solving multi-objective optimization problems , 2014, Soft Computing.

[10]  Ning Wang,et al.  An adaptive RNA genetic algorithm for modeling of proton exchange membrane fuel cells , 2013 .

[11]  Ahmad Khairi Abdul Wahab,et al.  Dynamic fuzzy cognitive network approach for modelling and control of PEM fuel cell for power electric bicycle system , 2017 .

[12]  Noradin Ghadimi,et al.  Hybrid PSOTVAC/BFA technique for tuning of robust PID controller of fuel cell voltage , 2016 .

[13]  P. Moseley.  Fuel Cell Systems Explained , 2001 .

[14]  Xianguo Li,et al.  Mathematical modeling of proton exchange membrane fuel cells , 2001 .

[15]  Seyezhai Ramalingam,et al.  A review of optimization algorithms for the modeling of proton exchange membrane fuel cell , 2016 .

[16]  Ardeshir Bahreininejad,et al.  Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems , 2012 .

[17]  Attia Abdelaziz Hussien Ali.  Electrical characterisation of proton exchange membrane fuel cells stack using grasshopper optimiser , 2018 .

[18]  Alireza Rezazadeh,et al.  Artificial immune system-based parameter extraction of proton exchange membrane fuel cell , 2011 .

[19]  Ahmed Fathy,et al.  Multi-Verse Optimizer for Identifying the Optimal Parameters of PEMFC Model , 2018 .

[20]  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.

[21]  Ning Wang,et al.  Cuckoo search algorithm with explosion operator for modeling proton exchange membrane fuel cells , 2019, International Journal of Hydrogen Energy.

[22]  Salah Kamel,et al.  Developing and Applying Chaotic Harris Hawks Optimization Technique for Extracting Parameters of Several Proton Exchange Membrane Fuel Cell Stacks , 2020, IEEE Access.

[23]  Meiying Ye,et al.  Parameter identification for proton exchange membrane fuel cell model using particle swarm optimization , 2009 .

[24]  Zhi Wang,et al.  Parameter estimation of proton exchange membrane fuel cells using eagle strategy based on JAYA algorithm and Nelder-Mead simplex method , 2019, Energy.

[25]  Abel Hernandez-Guerrero,et al.  Numerical modeling of a proton exchange membrane fuel cell with tree-like flow field channels based on an entropy generation analysis , 2017 .

[26]  Xin-Jian Zhu,et al.  Parameter optimization for a PEMFC model with a hybrid genetic algorithm , 2006 .

[27]  M. A. Elhameed,et al.  Effective parameters’ identification for polymer electrolyte membrane fuel cell models using grey wolf optimizer , 2017 .

[28]  Alireza Rezazadeh,et al.  A new heuristic optimization algorithm for modeling of proton exchange membrane fuel cell: bird mating optimizer , 2013 .

[29]  Alireza Rezazadeh,et al.  A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters , 2011, Journal of Zhejiang University SCIENCE C.