Fuel Cells with Proton Exchange Membrane Modeling and Control Techniques
Article Sidebar
Main Article Content
Under a creative commons Licenses
Abstract
Comprehensive mathematical models with three distinct controllers (PID, FOPID, and fuzzy + PID) for polymer electrolyte fuel cells (PEFCs) are constructed in this work. The models are made to indirectly control the input hydrogen mass flow rate in order to set the output voltage of the PEMFCs at a predetermined value. The simulation results demonstrate how effectively the es-tablished model fits the task of characterizing a PEFC's performance. While the developed control-lers are capable of stabilizing voltage, the fuzzy + PID controller performs better, exhibiting a re-duced overshoot and a faster response time.
Downloads
Article Details
References
Andersson, M., et al., A review of cell-scale multiphase flow modeling, including water management, in polymer electrolyte fuel cells. Applied Energy, 2016. 180: p. 757-778. DOI: https://doi.org/10.1016/j.apenergy.2016.08.010
Andersson, M., et al., Interface resolving two-phase flow simulations in gas channels relevant for polymer electrolyte fuel cells using the volume of fluid approach. International journal of hydrogen energy, 2018. 43(5): p. 2961-2976. DOI: https://doi.org/10.1016/j.ijhydene.2017.12.129
Outeiro, M. and A. Carvalho. MatLab/Simulink as design tool of PEM Fuel Cells as electrical generation systems. in European Fuel Cell Forum. 2011.
Ahn, J.-W. and S.-Y. Choe, Coolant controls of a PEM fuel cell system. Journal of Power Sources, 2008. 179(1): p. 252-264. DOI: https://doi.org/10.1016/j.jpowsour.2007.12.066
Lü, X., et al., Dynamic modeling and fractional order PI λ D µ control of PEM fuel cell. Int. J. Electrochem. Sci, 2017. 12: p. 7518-7536. DOI: https://doi.org/10.20964/2017.08.12
Justesen, K.K., S.J. Andreasen, and S.L. Sahlin, Modeling of a HTPEM fuel cell using adaptive neuro-fuzzy inference systems. International Journal of Hydrogen Energy, 2015. 40(46): p. 16814-16819. DOI: https://doi.org/10.1016/j.ijhydene.2015.08.061
Benchouia, N.E., et al., An adaptive fuzzy logic controller (AFLC) for PEMFC fuel cell. International Journal of Hydrogen Energy, 2015. 40(39): p. 13806-13819. DOI: https://doi.org/10.1016/j.ijhydene.2015.05.189
Ural, Z. and M.T. Gencoglu. Mathematical models of PEM fuel cells. in 5th International Ege Energy Symposium and Exhibition (IEESE-5), Denizli. 2010.
Geethanjali, R. and R. Sivakumar. Design of intelligent controller for PEM fuel cell. in 2017 Third International Conference on Science Technology Engineering & Management (ICONSTEM). 2017. IEEE. DOI: https://doi.org/10.1109/ICONSTEM.2017.8261316
Saadi, A., et al., Dynamic modeling and experimental analysis of PEMFCs: A comparative study. International Journal of Hydrogen Energy, 2017. 42(2): p. 1544-1557. DOI: https://doi.org/10.1016/j.ijhydene.2016.07.180
Corrêa, J.M., et al., An electrochemical-based fuel-cell model suitable for electrical engineering automation approach. IEEE Transactions on industrial electronics, 2004. 51(5): p. 1103-1112. DOI: https://doi.org/10.1109/TIE.2004.834972
Rivera, D.E., M. Morari, and S. Skogestad, Internal model control: PID controller design. Industrial & engineering chemistry process design and development, 1986. 25(1): p. 252-265. DOI: https://doi.org/10.1021/i200032a041
Aleksei, T., P. Eduard, and B. Juri. A flexible MATLAB tool for optimal fractional-order PID controller design subject to specifications. in Proceedings of the 31st Chinese Control Conference. 2012. IEEE.
Fkirin, M.A. and M.A. Khira, Enhanced antenna positioning control system using adapted dc servo motor and fuzzy-pi controller. IEEE Access, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3313976