THERMAL SCIENCE

International Scientific Journal

Thermal Science - Online First

online first only

Multi-objective optimization of operating parameters of a PEM fuel cell under flooding conditions using the non-dominated sorting genetic algorithm (NSGA-II)

ABSTRACT
In the present study, the performance of a PEMFC is studied under cathode flooding conditions. A two-dimensional model of water and heat management based on the laws of conservation and electrochemical equations is used. The performance of the PEM cell is evaluated on the basis of the computed average current density and its distribution along the channels. Operating parameters are optimized with the objective of maximizing average current density while minimizing its variations. The problem is formulated into a multi-objective form that is solved by the Non-dominated Sorting Genetic Algorithm (NSGA-II) to find the optimal Pareto front. The results of the base case are compared to those of the optimized cell. A 38.94% increase in average current density and a 38.8% decrease in standard deviation are obtained.
KEYWORDS
PAPER SUBMITTED: 2018-02-11
PAPER REVISED: 2018-04-03
PAPER ACCEPTED: 2018-04-16
PUBLISHED ONLINE: 2018-05-12
DOI REFERENCE: https://doi.org/10.2298/TSCI180211144A
REFERENCES
  1. Ait Messaoudene N., et al., Estimation of Indirect CO2 Emissions of a Hydrogen Powered Medium Size Vehicle for the NEDC cycle, International Journal of Energy Research, 34 (2010), pp. 745-756
  2. Malekbala, M.R., Modeling and Control OF A Proton Exchange Membrane Fuel Cell with the Air Compressor According to Requested Electrical Current, Thermal Science, 19 (2015), 6, pp. 2065-2078
  3. Hwang, J.J., et al., Development of a Lightweight Fuel Cell Vehicle, Journal of Power Sources, 141 (2005), 1, pp. 108-115
  4. Cleghorn, S.J.C., et al., A Printed Circuit Board Approach to Measuring Current Distribution in a Fuel Cell, Journal of Applied Electrochemistry, 28 (1998), 7, pp. 663-672
  5. Weng, F.B., et al., Numerical Prediction of Concentration and Current Distributions in PEMFC, Journal of Power Sources, 145 (2005), 2, pp. 546-554
  6. Hwnag, J.J., et al., Experimental and Numerical Studies of Local Current Mapping on a PEM Fuel Cell, International Journal of Hydrogen Energy, 33 (2008), 20, pp. 5718-5727
  7. Pasaogullari, U., Wang, C.Y., Liquid Water Transport in Gas Diffusion Layer of Polymer Electrolyte Fuel Cells, Journal of the Electrochemical Society, 151 (2004), 3, pp. A399-A406
  8. Sedighizadeh, M., et al., Parameter Optimization for a PEMFC Model with Particle Swarm Optimization, International Journal of Engineering & Applied Sciences, 3 (2011), 1, pp. 102-108
  9. Askarzadeh, A., Rezazadeh, A., Optimization of PEMFC Model Parameters with a Modified Particle Swarm Optimization, International Journal of Energy Research, 35 (2011), 14, pp. 1258-1265
  10. Mehrabi, M., et al., Modeling of Proton Exchange Membrane Fuel Cell (PEMFC) Performance by Using Genetic Algorithm-Polynomial Neural Network (GA-PNN) Hybrid System, Proceedings of the ASME, 10th Fuel Cell Science, Engineering and Technology Conference, San Diego, 2012, pp. 1-6
  11. Tafaoli-Masoule, M., et al., Optimum Design Parameters and Operating Condition for Maximum Power of a Direct Methanol Fuel Cell Using Analytical Model and Genetic Algorithm, Energy, 70 (2014), pp. 643-652
  12. Yang, W.J., et al., Channel Geometry Optimization of a Polymer Electrolyte Membrane Fuel Cell Using Genetic Algorithm, Applied Energy, 146 (2015), pp. 1-10
  13. Sun, Z., et al., Parameter Identification of PEMFC Model Based on Hybrid Adaptive Differential Evolution Algorithm, Energy, 90 (2015), pp. 1334-1341
  14. Al-Othman, A.K., et al., Parameter Identification of PEM Fuel Cell Using Quantum-Based Optimization Method, Arabian Journal for Science and Engineering, 40 (2015), 9, pp. 2619-2628
  15. Haghighi, M., Sharifhassan, F., Exergy Analysis and Optimization of a High Temperature Proton Exchange Membrane Fuel Cell Using Genetic Algorithm, Case Studies in Thermal Engineering, 8 (2016), pp. 207-217
  16. Chen, X., et al., Parametric Analysis and Optimization of PEMFC System For Maximum Power and Efficiency Using MOEA/D, Applied Thermal Engineering, 121 (2017), pp. 400-409
  17. Rao, S.S.L., et al., Optimization of Operating Parameters to Maximize The Current Density Without Flooding At The Cathode Membrane Interface of a PEM Fuel Cell Using Taguchi Method And Genetic Algorithm, International Journal of Energy and Environment, 5 (2014), 3, pp. 335-352
  18. Boudouh, M., et al, Experimental Investigation of Convective Boiling in Mini-Channels: Cooling Application of the Proton Exchange Membrane Fuel Cells, Thermal Science, 21 (2017), 1A, pp. 223-232
  19. Karimi, G., et al., Along-Channel Flooding Prediction of Polymer Electrolyte Membrane Fuel Cells, International Journal of Energy Research, 35(2011), 10, pp. 883-896
  20. Jamekhorshid, A., et al., Current Distribution and Cathode Flooding Prediction in a PEM Fuel Cell, Journal of the Taiwan Institute of Chemical Engineers, 42 (2011), 4, pp. 622-631
  21. Nguyen, T.V., White, R.E., A Water and heat Management Model for Proton-Exchange-Membrane Fuel Cells, Journal of the Electrochemical Society, 140 (1993), 8, pp. 2178-2186
  22. Amani, M., Multi-Objective Optimization of Thermophysical Properties of Eco-Friendly Organic Nanofluids, Journal of Cleaner Production, 166 (2017), pp. 350-359
  23. Hemmat Esfe, M., Multi-Objective Optimization of nanofluid Flow in Double Tube Heat Exchangers for Applications In Energy Systems, Energy, 137 (2017), pp. 160-171
  24. Meidanshahi, V., Karimi G., Dynamic Modeling, Optimization and Control of Power Density in a PEM Fuel Cell, Applied Energy, 93 (2012), pp. 98-105
  25. Deb, K., et al., A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6 (2002), 2, pp. 182-197