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

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

Energy, 2025, vol. 315, issue C

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.

Keywords: PEMFC; Gas supply system; FNN-PID algorithm; Test verification (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224041331
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:315:y:2025:i:c:s0360544224041331

DOI: 10.1016/j.energy.2024.134355

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().