Model Structure Optimization for Fuel Cell Polarization Curves

The applications of evolutionary optimizers such as genetic algorithms, differential evolution, and various swarm optimizers to the parameter estimation of the fuel cell polarization curve models have increased. This study takes a novel approach on utilizing evolutionary optimization in fuel cell modeling. Model structure identification is performed with genetic algorithms in order to determine an optimized representation of a polarization curve model with linear model parameters. The optimization is repeated with a different set of input variables and varying model complexity. The resulted model can successfully be generalized for different fuel cells and varying operating conditions, and therefore be readily applicable to fuel cell system simulations.

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

[2]  Mohammed Jourdani,et al.  Three-Dimensional PEM Fuel Cells Modeling using COMSOL Multiphysics , 2017 .

[3]  J. C. Amphlett,et al.  Performance modeling of the Ballard Mark IV solid polymer electrolyte fuel cell. II: Empirical model development , 1995 .

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

[5]  X. D. Xue,et al.  Unified mathematical modelling of steady-state and dynamic voltage–current characteristics for PEM fuel cells , 2006 .

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

[7]  C. R. Derouin,et al.  N 87-29937 ADVANCES IN SOLID POLYMER ELECTROLYTE FUEL CELL TECHNOLOGY WITH LOW-PLATINUM-LOADING ELECTRODES , 2004 .

[8]  M. Valentini,et al.  A new semi-empirical approach to performance curves of polymer electrolyte fuel cells , 2002 .

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

[10]  Uday K. Chakraborty,et al.  Static and dynamic modeling of solid oxide fuel cell using genetic programming , 2009 .

[11]  C. Chamberlin,et al.  Modeling of Proton Exchange Membrane Fuel Cell Performance with an Empirical Equation , 1995 .

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

[13]  Kauko Leiviskä,et al.  Validation of genetic algorithm results in a fuel cell model , 2010 .

[14]  Markku Ohenoja,et al.  Identification of electrochemical model parameters in PEM fuel cells , 2009, 2009 International Conference on Power Engineering, Energy and Electrical Drives.

[15]  Luciane Neves Canha,et al.  An electrochemical-based fuel-cell model suitable for electrical engineering automation approach , 2004, IEEE Transactions on Industrial Electronics.

[16]  Chang-Bock Chung,et al.  Performance prediction and analysis of a PEM fuel cell operating on pure oxygen using data-driven models: A comparison of artificial neural network and support vector machine , 2016 .

[17]  Kauko Leiviskä,et al.  Genetic Algorithms in Model Structure Identification for Fuel Cell Polarization Curve , 2018, 2018 5th International Conference on Control, Decision and Information Technologies (CoDIT).

[18]  Detlef Stolten,et al.  Analytical and Numerical Analysis of PEM Fuel Cell Performance Curves , 2005 .

[19]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[20]  G. Maggio,et al.  An empirical equation for polymer electrolyte fuel cell (PEFC) behaviour , 1999 .

[21]  J. H. Lee,et al.  Modeling electrochemical performance in large scale proton exchange membrane fuel cell stacks , 1998 .

[22]  Kauko Leiviskä,et al.  Differential Evolution in Parameter Identification - Fuel Cell as an Example , 2012, ICINCO.

[23]  J. C. Amphlett.  Performance Modeling of the Ballard Mark IV Solid Polymer Electrolyte Fuel Cell , 1995 .

[24]  Cuimei Bo,et al.  Modeling and optimization for proton exchange membrane fuel cell stack using aging and challenging P systems based optimization algorithm , 2016 .

[25]  Jennie Malboeuf.  Algorithm , 1994, Neurology.

[26]  Uday K. Chakraborty,et al.  Reversible and Irreversible Potentials and an Inaccuracy in Popular Models in the Fuel Cell Literature , 2018, Energies.

[27]  Omar Z. Sharaf,et al.  An overview of fuel cell technology: Fundamentals and applications , 2014 .