Semi-empirical PEM fuel cells model using whale optimization algorithm

Abstract The accuracy of the fuel cell model (FC) plays an important role, since it affects the simulation results. Obviously, this can appear in different systems of distributed generations and hybrid microgrids. This article presents a new application of the whale optimization approach (WOA) to obtain unidentified parameters of the proton exchange membrane (PEM) FC model. The ultimate objective of the current work is to develop a precise PEMFC model, which provides true results of modeling and simulation of these FCs. In this sense, the I-V characteristics of the PEMFC are non-linear, including seven unknown parameters due to the lack of data from the manufacturer. This problem can be expressed mathematically as a non-linear optimization problem, where the sum of the error squared between the measured FC voltage and its output voltage model defines the fitness value. WOA is applied directly to minimize the objective function under the predefined constraints. The estimated PEMFC model is certified by the numerical results shown, which are performed under various conditions of temperature and regulating pressures. The value of the proposed WOA-based PEMFC model is appraised by comparing the demonstrated results with the empirical results of several typical PEMFCs such as Ballard Mark V, the AVISTA SR-12 PEM generator, the 250 W and the Horizon H-12 stacks. The results based on WOA are compared with other results based on optimization competing methods. The application of WOA can lead to the creation of a precise PEMFC model.

[1]  Q. Niu,et al.  A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells , 2014 .

[2]  A. Khellaf,et al.  Analytical modelling and experimental validation of proton exchange membrane electrolyser for hydrogen production , 2017 .

[3]  Yu He,et al.  Parameter extraction of solar photovoltaic models using an improved whale optimization algorithm , 2018, Energy Conversion and Management.

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

[5]  Attia A. El-Fergany,et al.  Performance enhancement of autonomous system comprising proton exchange membrane fuel cells and switched reluctance motor , 2018, Energy.

[6]  Ning Wang,et al.  Parameter identification of PEMFC model based on hybrid adaptive differential evolution algorithm , 2015 .

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

[8]  Leandro dos Santos Coelho,et al.  A backtracking search algorithm combined with Burger's chaotic map for parameter estimation of PEMFC electrochemical model , 2014 .

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

[10]  Alireza Rezazadeh,et al.  An Innovative Global Harmony Search Algorithm for Parameter Identification of a PEM Fuel Cell Model , 2012, IEEE Transactions on Industrial Electronics.

[11]  Soosan Rowshanzamir,et al.  Modelling and simulation of the steady-state and dynamic behaviour of a PEM fuel cell , 2010 .

[12]  Attia A. El-Fergany,et al.  Optimized Parameters of SOFC for steady state and transient simulations using interior search algorithm , 2019, Energy.

[13]  K. Jiao,et al.  Sensitivity analysis of uncertain parameters based on an improved proton exchange membrane fuel cell analytical model , 2018 .

[14]  Yılser Devrim,et al.  Investigation of micro-combined heat and power application of PEM fuel cell systems , 2018 .

[15]  Ning Wang,et al.  Hybrid artificial bee colony algorithm for parameter estimation of proton exchange membrane fuel cell , 2013 .

[16]  N. Rajasekar,et al.  Application of flower pollination algorithm for enhanced proton exchange membrane fuel cell modelling , 2019, International Journal of Hydrogen Energy.

[17]  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.

[18]  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.

[19]  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 .

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

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

[23]  Maher A.R. Sadiq Al-Baghdadi,et al.  Modelling of proton exchange membrane fuel cell performance based on semi-empirical equations , 2005 .

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

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

[26]  Attia A. El-Fergany,et al.  Steady-state and dynamic models of solid oxide fuel cells based on Satin Bowerbird Optimizer , 2018 .

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

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

[29]  Kang Li,et al.  An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models , 2014 .

[30]  Qi Li,et al.  Seeker optimization algorithm for global optimization: A case study on optimal modelling of proton exchange membrane fuel cell (PEMFC) , 2011 .

[31]  S. Jakubek,et al.  Online estimation of the electrochemical impedance of polymer electrolyte membrane fuel cells using broad-band current excitation , 2018, Journal of Power Sources.

[32]  Jiong Shen,et al.  Efficiency analysis and control of a grid-connected PEM fuel cell in distributed generation , 2019, Energy Conversion and Management.

[33]  Wei Guo,et al.  Optimization of critical parameters of PEM fuel cell using TLBO-DE based on Elman neural network , 2019, Energy Conversion and Management.

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

[35]  Hany M. Hasanien,et al.  Whale optimisation algorithm for automatic generation control of interconnected modern power systems including renewable energy sources , 2017 .

[36]  Hany M. Hasanien,et al.  Performance improvement of photovoltaic power systems using an optimal control strategy based on whale optimization algorithm , 2018 .

[37]  Alin Mazare,et al.  Optimization of the proton exchange membrane fuel cell hybrid power system for residential buildings , 2018 .

[38]  Mohamed Abd Elaziz,et al.  Parameter estimation of solar cells diode models by an improved opposition-based whale optimization algorithm , 2018, Energy Conversion and Management.

[39]  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.

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

[41]  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.

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

[43]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[44]  Adel Akbarimajd,et al.  A novel intelligent-based method to control the output voltage of Proton Exchange Membrane Fuel Cell , 2019, Energy Conversion and Management.

[45]  Hany M. Hasanien,et al.  Parameters estimation of single‐ and multiple‐diode photovoltaic model using whale optimisation algorithm , 2018, IET Renewable Power Generation.