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Adaptive control for robust air flow management in an automotive fuel cell system

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  • Han, Jaeyoung
  • Yu, Sangseok
  • Yi, Sun

Abstract

Surge control in a centrifugal air compressor is crucial to ensure the reliable operation of a fuel cell system, but few studies have reported on compressor surges in fuel cell systems. This study presents an effective surge control algorithm with a surge predictive compressor model under various operating conditions of an automotive fuel cell system. Unlike previous studies on the air management systems of an automotive fuel cell, this study presents an analytic compressor model which is a nonlinear dynamic model with surge prediction capability. In this study, a model reference adaptive control (MRAC) is introduced, to avoid compressor surge during the dynamic operation of a fuel cell system. The adaptive control was compared with the nominal feedback control in the air management system of an automotive fuel cell operating within normal conditions, under transient and steady-state responses. Also, when a surge was detected in the system, the adaptive control algorithm was fast enough to recover the air mass flow rate to normal ranges. Based on these results, it can be concluded that the MRAC algorithm shows better performance than the nominal feedback control algorithm with respect to the transient behaviors and surge recovery of an automotive fuel cell system.

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  • 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.
    • Handle: RePEc:eee:appene:v:190:y:2017:i:c:p:73-83
    DOI: 10.1016/j.apenergy.2016.12.115
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    3. Nicu Bizon & Mihai Oproescu, 2018. "Experimental Comparison of Three Real-Time Optimization Strategies Applied to Renewable/FC-Based Hybrid Power Systems Based on Load-Following Control," Energies, MDPI, vol. 11(12), pages 1-32, December.
    4. Liu, Ze & Zhang, Baitao & Xu, Sichuan, 2022. "Research on air mass flow-pressure combined control and dynamic performance of fuel cell system for vehicles application," Applied Energy, Elsevier, vol. 309(C).
    5. Sangeetha, Thangavel & Chen, Po-Tuan & Yan, Wei-Mon & Huang, K. David, 2020. "Enhancement of air-flow management in Zn-air fuel cells by the optimization of air-flow parameters," Energy, Elsevier, vol. 197(C).
    6. Bizon, Nicu, 2017. "Energy optimization of fuel cell system by using global extremum seeking algorithm," Applied Energy, Elsevier, vol. 206(C), pages 458-474.
    7. Bizon, Nicu, 2019. "Fuel saving strategy using real-time switching of the fueling regulators in the proton exchange membrane fuel cell system," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    8. Wu, Xiao-long & Xu, Yuan-wu & Zhao, Dong-qi & Zhong, Xiao-bo & Li, Dong & Jiang, Jianhua & Deng, Zhonghua & Fu, Xiaowei & Li, Xi, 2020. "Extended-range electric vehicle-oriented thermoelectric surge control of a solid oxide fuel cell system," Applied Energy, Elsevier, vol. 263(C).
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    10. Lu Zhang & Yongfeng Liu & Pucheng Pei & Xintong Liu & Long Wang & Yuan Wan, 2022. "Variation Characteristic Analysis of Water Content at the Flow Channel of Proton Exchange Membrane Fuel Cell," Energies, MDPI, vol. 15(9), pages 1-20, April.
    11. 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.
    12. Hu, Haowen & Ou, Kai & Yuan, Wei-Wei, 2023. "Fused multi-model predictive control with adaptive compensation for proton exchange membrane fuel cell air supply system," Energy, Elsevier, vol. 284(C).
    13. Li, Yuehua & Pei, Pucheng & Ma, Ze & Ren, Peng & Huang, Hao, 2020. "Analysis of air compression, progress of compressor and control for optimal energy efficiency in proton exchange membrane fuel cell," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    14. 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).
    15. Vu, Hoang Nghia & Truong Le Tri, Dat & Nguyen, Huu Linh & Kim, Younghyeon & Yu, Sangseok, 2023. "Multifunctional bypass valve for water management and surge protection in a proton-exchange membrane fuel cell supply-air system," Energy, Elsevier, vol. 278(C).
    16. Zhuang Tian & Zheng Wei & Jinhui Wang & Yinxiang Wang & Yuwei Lei & Ping Hu & S. M. Muyeen & Daming Zhou, 2023. "Research Progress on Aging Prediction Methods for Fuel Cells: Mechanism, Methods, and Evaluation Criteria," Energies, MDPI, vol. 16(23), pages 1-21, November.
    17. Hou, Junbo & Yang, Min & Ke, Changchun & Zhang, Junliang, 2020. "Control logics and strategies for air supply in PEM fuel cell engines," Applied Energy, Elsevier, vol. 269(C).
    18. 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.
    19. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Feedback linearization-based MIMO model predictive control with defined pseudo-reference for hydrogen regulation of automotive fuel cells," Applied Energy, Elsevier, vol. 293(C).
    20. Bizon, Nicu & Thounthong, Phatiphat, 2018. "Real-time strategies to optimize the fueling of the fuel cell hybrid power source: A review of issues, challenges and a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1089-1102.
    21. Sun, Li & Shen, Jiong & Hua, Qingsong & Lee, Kwang Y., 2018. "Data-driven oxygen excess ratio control for proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 231(C), pages 866-875.
    22. Nicu Bizon & Phatiphat Thounthong, 2021. "A Simple and Safe Strategy for Improving the Fuel Economy of a Fuel Cell Vehicle," Mathematics, MDPI, vol. 9(6), pages 1-29, March.

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