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Adaptive Fuzzy PID Based on Granular Function for Proton Exchange Membrane Fuel Cell Oxygen Excess Ratio Control

Author

Listed:
  • Xiao Tang

    (School of Automation, Central South University, Changsha 410083, China
    Hunan Xiangjiang Artificial Intelligence College, Changsha 410083, China)

  • Chunsheng Wang

    (School of Automation, Central South University, Changsha 410083, China
    Hunan Xiangjiang Artificial Intelligence College, Changsha 410083, China)

  • Yukun Hu

    (Department of Civil, Environment & Geomatic Engineering, University College London, London WC1E 6BT, UK)

  • Zijian Liu

    (School of Automation, Central South University, Changsha 410083, China
    Hunan Xiangjiang Artificial Intelligence College, Changsha 410083, China)

  • Feiliang Li

    (School of Automation, Central South University, Changsha 410083, China
    Hunan Xiangjiang Artificial Intelligence College, Changsha 410083, China)

Abstract

An effective oxygen excess ratio control strategy for a proton exchange membrane fuel cell (PEMFC) can avoid oxygen starvation and optimize system performance. In this paper, a fuzzy PID control strategy based on granular function (GFPID) was proposed. Meanwhile, a proton exchange membrane fuel cell dynamic model was established on the MATLAB/Simulink platform, including the stack model system and the auxiliary system. In order to avoid oxygen starvation due to the transient variation of load current and optimize the parasitic power of the auxiliary system and the stack voltage, the purpose of optimizing the overall operating condition of the system was finally achieved. Adaptive fuzzy PID (AFPID) control has the technical bottleneck limitation of fuzzy rules explosion. GFPID eliminates fuzzification and defuzzification to solve this phenomenon. The number of fuzzy rules does not affect the precision of GFPID control, which is only related to the fuzzy granular points in the fitted granular response function. The granular function replaces the conventional fuzzy controller to realize the online adjustment of PID parameters. Compared with the conventional PID and AFPID control, the feasibility and superiority of the algorithm based on particle function are verified.

Suggested Citation

  • Xiao Tang & Chunsheng Wang & Yukun Hu & Zijian Liu & Feiliang Li, 2021. "Adaptive Fuzzy PID Based on Granular Function for Proton Exchange Membrane Fuel Cell Oxygen Excess Ratio Control," Energies, MDPI, vol. 14(4), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1140-:d:503221
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    References listed on IDEAS

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    Cited by:

    1. Zeng, Tao & Xiao, Long & Chen, Jinrui & Li, Yu & Yang, Yi & Huang, Shulong & Deng, Chenghao & Zhang, Caizhi, 2023. "Feedforward-based decoupling control of air supply for vehicular fuel cell system: Methodology and experimental validation," Applied Energy, Elsevier, vol. 335(C).
    2. Phatiphat Thounthong & Pongsiri Mungporn & Babak Nahid-Mobarakeh & Nicu Bizon & Serge Pierfederici & Damien Guilbert, 2021. "Improved Adaptive Hamiltonian Control Law for Constant Power Load Stability Issue in DC Microgrid: Case Study for Multiphase Interleaved Fuel Cell Boost Converter," Sustainability, MDPI, vol. 13(14), pages 1-17, July.
    3. Javaid, Usman & Mehmood, Adeel & Iqbal, Jamshed & Uppal, Ali Arshad, 2023. "Neural network and URED observer based fast terminal integral sliding mode control for energy efficient polymer electrolyte membrane fuel cell used in vehicular technologies," Energy, Elsevier, vol. 269(C).

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