Using qualimetric engineering and extremal analysis to optimize a proton exchange membrane fuel cell stack

The optimal configuration of the proton exchange membrane fuel-cell (PEMFC) stack has received attention recently because of its potential use as an isolated energy distributor for household needs. In this work, the original complex problem for generating an optimal PEMFC stack based on the number of cell units connected in series and parallel arrangements as well as on the cell area is revisited. A qualimetric engineering strategy is formulated which is based on quick profiling the PEMFC stack voltage response. Stochastic screening is initiated by employing an L9(33) Taguchi-type OA for partitioning numerically the deterministic expression of the output PEMFC stack voltage such that to facilitate the sizing of the magnitude of the individual effects. The power and current household specifications for the stack system are maintained at the typical settings of 200W at 12V, respectively. The minimization of the stack total-area requirement becomes explicit in this work. The relationship of cell voltage against cell area is cast into a power-law model by regression fitting that achieves a coefficient of determination value of 99.99%. Thus, the theoretical formulation simplifies into a non-linear extremal problem with a constrained solution due to a singularity which is solved analytically. The optimal solution requires 22 cell units connected in series where each unit is designed with an area value of 151.4cm2. It is also demonstrated how to visualize the optimal solution using the graphical method of operating lines. The total area of 3270.24cm2 becomes a new benchmark for the optimal design of the studied PEMFC stack configuration. It is discussed the viability of our solution with respect to the performance of the main stack cost driver, i.e. the platinum loading costs. In conclusion, the proposed technique is simple and practical offering more accuracy, convenience and flexibility when compared with other competing algorithmic schemes.

[1]  N. Jenkins,et al.  Proton exchange membrane (PEM) fuel cell stack configuration using genetic algorithms , 2004 .

[2]  Luis R. Núñez-Rivas,et al.  Conceptual design of offshore platform supply vessel based on hybrid diesel generator-fuel cell power plant , 2014 .

[3]  Uttara Chakraborty,et al.  A new stochastic algorithm for proton exchange membrane fuel cell stack design optimization , 2012 .

[4]  Bent Sørensen.  Fuel cells: Optimism gone – Hard work still there☆ , 2013 .

[5]  Genichi Taguchi.  System Of Experimental Design: Engineering Methods To Optimize Quality And Minimize Costs , 1987 .

[6]  Chung-Feng Jeffrey Kuo,et al.  Using the Taguchi method and grey relational analysis to optimize the flat-plate collector process w , 2011 .

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

[8]  Arindam Majumder,et al.  Multi objective optimization of performance parameters of a single cylinder diesel engine running with hydrogen using a Taguchi-fuzzy based approach , 2013 .

[9]  James Larminie,et al.  Fuel Cell Systems Explained , 2000 .

[10]  Xuhui Wen,et al.  Fuel cell power system and high power DC-DC converter , 2004 .

[11]  Horng Wen Wu,et al.  Analysis of operating parameters considering flow orientation for the performance of a proton exchange membrane fuel cell using the Taguchi method , 2010 .

[12]  Uday K. Chakraborty,et al.  PEM fuel cell modeling using differential evolution , 2012 .

[13]  S. Basu.  Recent trends in fuel cell science and technology , 2007 .

[14]  George J. Besseris,et al.  Profiling effects in industrial data mining by non-parametric DOE methods: An application on screening checkweighing systems in packaging operations , 2012, Eur. J. Oper. Res..

[15]  Remzi Can Samsun,et al.  Evaluation of multifunctional fuel cell systems in aviation using a multistep process analysis methodology , 2013 .

[16]  Hui Wen Ku,et al.  Effects of modified flow field on optimal parameters estimation and cell performance of a PEM fuel cell with the Taguchi method , 2012 .

[17]  James F. Miller,et al.  Key challenges and recent progress in batteries, fuel cells, and hydrogen storage for clean energy systems , 2006 .

[18]  Sreenivas Jayanti,et al.  A conceptual model of a high-efficiency, stand-alone power unit based on a fuel cell stack with an integrated auto-thermal ethanol reformer , 2013 .

[19]  B. Sundén,et al.  Analysis of multi-phase transport phenomena with catalyst reactions in polymer electrolyte membrane , 2011 .

[20]  Ning Wang,et al.  A novel P systems based optimization algorithm for parameter estimation of proton exchange membrane fuel cell model , 2012 .

[21]  Carlos Andrés Ramos-Paja,et al.  A perturbation strategy for fuel consumption minimization in polymer electrolyte membrane fuel cells: Analysis, Design and FPGA implementation , 2014 .

[22]  Jocelyn Wishart,et al.  Computational design and optimization of fuel cells and fuel cell systems: A review , 2011 .

[23]  Maxime Wack,et al.  Robust control of the PEM fuel cell air-feed system via sub-optimal second order sliding mode , 2013 .

[24]  George J. Besseris,et al.  Multi-response multi-factorial master ranking in non-linear replicated-saturated DOE for qualimetrics , 2012 .

[25]  Genichi Taguchi,et al.  Taguchi's Quality Engineering Handbook , 2004 .

[26]  Frano Barbir,et al.  PEM Fuel Cells: Theory and Practice , 2012 .

[27]  Yan Wang,et al.  Optimization of the polypyrrole-coating parameters for proton exchange membrane fuel cell bipolar plates using the Taguchi method , 2008 .

[28]  Young-Bae Kim,et al.  Channel geometry optimization of a polymer electrolyte membrane fuel cell using genetic algorithm , 2015 .

[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.

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

[31]  M. Sedighizadeh,et al.  Parameter Optimization for a Pemfc Model With Particle Swarm Optimization , 2011 .

[32]  Linda Barelli,et al.  Dynamic analysis of PEMFC-based CHP systems for domestic application , 2012 .

[33]  Fabien Harel,et al.  Design of experiment techniques for fuel cell characterisation and development , 2009 .

[34]  George J. Besseris,et al.  Order Statistics for a Two-Level, Eight-Run Saturated-Unreplicated Fractional-Factorial Screening , 2009 .

[35]  George J. Besseris,et al.  Non‐linear nonparametric quality screening in low sampling testing , 2010 .

[36]  Stephan Schmid,et al.  Fuel cells for automotive powertrains―A techno-economic assessment , 2009 .

[37]  Evangelos Rikos,et al.  Use of model predictive control for experimental microgrid optimization , 2014 .

[38]  Joonsoo Bae,et al.  Optimization of operating parameters for liquid-cooled PEM fuel cell stacks using Taguchi method , 2012 .

[39]  Seo Young Kim,et al.  Performance evaluation of a polymer electrolyte membrane fuel cell system for powering portable freezer , 2013 .

[40]  Akin Tascikaraoglu,et al.  The assessment of the contribution of short-term wind power predictions to the efficiency of stand-alone hybrid systems , 2012 .

[41]  J. S. Hunter,et al.  Statistics for Experimenters: Design, Innovation, and Discovery , 2006 .

[42]  Martin S. Miller,et al.  A review of polymer electrolyte membrane fuel cell stack testing , 2011 .

[43]  Shan-Jen Cheng,et al.  Use of metamodeling optimal approach promotes the performance of proton exchange membrane fuel cell (PEMFC) , 2013 .

[44]  Linda Barelli,et al.  Optimization of a PEMFC/battery pack power system for a bus application , 2012 .

[45]  Horng-Wen Wu,et al.  Combustion characteristics and optimal factors determination with Taguchi method for diesel engines port-injecting hydrogen , 2012 .