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Fractional Order Fuzzy PID Control of Automotive PEM Fuel Cell Air Feed System Using Neural Network Optimization Algorithm

Author

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  • Mahmoud S. AbouOmar

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China
    Industrial Electronics and Control Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt)

  • Hua-Jun Zhang

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Yi-Xin Su

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

Abstract

The air feeding system is one of the most important systems in the proton exchange membrane fuel cell (PEMFC) stack, which has a great impact on the stack performance. The main control objective is to design an optimal controller for the air feeding system to regulate oxygen excess at the required level to prevent oxygen starvation and obtain the maximum net power output from the PEMFC stack at different disturbance conditions. This paper proposes a fractional order fuzzy PID controller as an efficient controller for the PEMFC air feed system. The proposed controller was then employed to achieve maximum power point tracking for the PEMFC stack. The proposed controller was optimized using the neural network algorithm (NNA), which is a new metaheuristic optimization algorithm inspired by the structure and operations of the artificial neural networks (ANNs). This paper is the first application of the fractional order fuzzy PID controller to the PEMFC air feed system. The NNA algorithm was also applied for the first time for the optimization of the controllers tested in this paper. Simulation results showed the effectiveness of the proposed controller by improving the transient response providing a better set point tracking and disturbance rejection with better time domain performance indices. Sensitivity analyses were carried-out to test the robustness of the proposed controller under different uncertainty conditions. Simulation results showed that the proposed controller had good robustness against parameter uncertainty in the system.

Suggested Citation

  • Mahmoud S. AbouOmar & Hua-Jun Zhang & Yi-Xin Su, 2019. "Fractional Order Fuzzy PID Control of Automotive PEM Fuel Cell Air Feed System Using Neural Network Optimization Algorithm," Energies, MDPI, vol. 12(8), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1435-:d:222696
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    References listed on IDEAS

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    1. Pathapati, P.R. & Xue, X. & Tang, J., 2005. "A new dynamic model for predicting transient phenomena in a PEM fuel cell system," Renewable Energy, Elsevier, vol. 30(1), pages 1-22.
    2. Yu-Shan Cheng & Yi-Hua Liu & Holger C. Hesse & Maik Naumann & Cong Nam Truong & Andreas Jossen, 2018. "A PSO-Optimized Fuzzy Logic Control-Based Charging Method for Individual Household Battery Storage Systems within a Community," Energies, MDPI, vol. 11(2), pages 1-18, February.
    3. Han, Jaeyoung & Yu, Sangseok & Yi, Sun, 2017. "Adaptive control for robust air flow management in an automotive fuel cell system," Applied Energy, Elsevier, vol. 190(C), pages 73-83.
    4. Carlos Robles Algarín & John Taborda Giraldo & Omar Rodríguez Álvarez, 2017. "Fuzzy Logic Based MPPT Controller for a PV System," Energies, MDPI, vol. 10(12), pages 1-18, December.
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    Cited by:

    1. Wang, Xuechao & Chen, Jinzhou & Quan, Shengwei & Wang, Ya-Xiong & He, Hongwen, 2020. "Hierarchical model predictive control via deep learning vehicle speed predictions for oxygen stoichiometry regulation of fuel cells," Applied Energy, Elsevier, vol. 276(C).
    2. Inés Tejado & Blas M. Vinagre & José Emilio Traver & Javier Prieto-Arranz & Cristina Nuevo-Gallardo, 2019. "Back to Basics: Meaning of the Parameters of Fractional Order PID Controllers," Mathematics, MDPI, vol. 7(6), pages 1-16, June.
    3. Youjie Ma & Long Tao & Xuesong Zhou & Wei Li & Xueqi Shi, 2019. "Analysis and Control of Wind Power Grid Integration Based on a Permanent Magnet Synchronous Generator Using a Fuzzy Logic System with Linear Extended State Observer," Energies, MDPI, vol. 12(15), pages 1-19, July.
    4. Adam Polak, 2020. "Simulation of Fuzzy Control of Oxygen Flow in PEM Fuel Cells," Energies, MDPI, vol. 13(9), pages 1-26, May.
    5. Miao Qian & Jie Li & Zhong Xiang & Chao Yan & Xudong Hu, 2019. "Effect of Pin Diameter Degressive Gradient on Heat Transfer in a Microreactor with Non-Uniform Pin-Fin Array under Low Reynolds Number Conditions," Energies, MDPI, vol. 12(14), pages 1-12, July.
    6. Noureddine Boutchich & Ayoub Moufid & Mohammed Bennani & Soumia El Hani, 2023. "Optimal Neural Network PID Approach for Building Thermal Management," Energies, MDPI, vol. 16(15), pages 1-14, July.
    7. Reza Ghasemi & Mehdi Sedighi & Mostafa Ghasemi & Bita Sadat Ghazanfarpoor, 2023. "Design of a Fuzzy Adaptive Voltage Controller for a Nonlinear Polymer Electrolyte Membrane Fuel Cell with an Unknown Dynamical System," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
    8. Abel Rubio & Wilton Agila & Leandro González & Jonathan Aviles-Cedeno, 2023. "Distributed Intelligence in Autonomous PEM Fuel Cell Control," Energies, MDPI, vol. 16(12), pages 1-25, June.
    9. Anastasios Dounis, 2019. "Special Issue “Intelligent Control in Energy Systems”," Energies, MDPI, vol. 12(15), pages 1-9, August.

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