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A control method of proton exchange membrane fuel cell gas supply system based on fuzzy neural network proportion integration differentiation algorithm

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  • Fu, Jianqin
  • Qin, Boquan
  • Wu, Yue
  • He, Tingpu
  • Zhang, Guanjie
  • Sun, Xilei

Abstract

With the rapid development of hydrogen fuel cell technology, the requirements for test equipment are continually advancing. In this study, a test system for a 300 kW-class proton exchange membrane fuel cell (PEMFC) was designed and constructed, and a simulation model for the gas supply system was established using MATLAB/Simulink. On this basis, the fuzzy neural network proportion integration differentiation (FNN-PID) algorithm was proposed to optimize the control of the gas supply system. The results indicate that the developed test system features a wide measuring range, high accuracy and excellent flexibility, enabling real-time monitoring, control and alarm functions for key parameters such as temperature, flow and pressure. Simulink simulations demonstrate that the FNN-PID algorithm exhibits superior control performance, with the fastest response speed and minimal overshoot. Test verification confirms that the FNN-PID algorithm outperforms the other two control algorithms, providing shorter regulation times, reduced overshoot, faster response speeds and enhanced anti-interference capabilities. Specifically, the FNN-PID algorithm reduces the regulation time for inlet pressure control by approximately 42 % compared to the conventional PID (C-PID) algorithm. These findings provide valuable methodological guidance for achieving real-time, efficient, stable and accurate testing of fuel cell systems.

Suggested Citation

  • Fu, Jianqin & Qin, Boquan & Wu, Yue & He, Tingpu & Zhang, Guanjie & Sun, Xilei, 2025. "A control method of proton exchange membrane fuel cell gas supply system based on fuzzy neural network proportion integration differentiation algorithm," Energy, Elsevier, vol. 315(C).
    • Handle: RePEc:eee:energy:v:315:y:2025:i:c:s0360544224041331
    DOI: 10.1016/j.energy.2024.134355
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    References listed on IDEAS

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