10.1007/s40095-022-00483-8

Dynamic modeling of a proton exchange membrane fuel cell using chaotic binary shark smell optimizer from electrical and thermal viewpoints

  • Shupeng Zhao1,
  • Weilian Sun*1,
  • Jie Li1,
  • Yi Gong1,
  1. College of Mechanical and Electrical Engineering, Zhengzhou University of Industrial Technology, Zhengzhou, Henan, 451100, CN

Published in Issue 2022-03-03

How to Cite

Zhao, S., Sun, W., Li, J., & Gong, Y. (2022). Dynamic modeling of a proton exchange membrane fuel cell using chaotic binary shark smell optimizer from electrical and thermal viewpoints. International Journal of Energy and Environmental Engineering, 13(3 (September 2022). https://doi.org/10.1007/s40095-022-00483-8
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Abstract

Abstract Proton exchange membrane fuel cells (PEMFC) can be considered as one of the most effective devices in power generation, which converts hydrogen to electrical power directly. The scalability of this system for stationary and mobile utilities is the main advantage of the PEMFC. But still, this technology is not fully mature. Nevertheless, there is a high demand to research in the field of fault detection and monitoring for PEMFC to promote its lifetime and performance and also decrease its operating expenses. Hence, an accurate model for these devices is essential. The accuracy of these models should be sufficient for providing the PEM fuel cell treatment to detect its various internal signals. The present paper aims to model the PEMFC system. The considered fuel cell utilizes the exit temperature in a closed loop. Therefore, the model should provide the thermal and electrical conditions. The Nexa-Ballard fuel cell with the 1.2 kW capacity is regarded here; thus, optimally obtaining the parameters is essential for presenting an accurate behavior. This paper presented four optimization methods in this regard. From these algorithms, the results of three methods are taken from other works, and the chaotic binary shark smell optimizer is suggested here. Eventually, validation of the proposed model by the obtained coefficients is performed using experimental data.

Keywords

  • Proton exchange membrane fuel cells,
  • Parameter identification,
  • Chaotic binary shark smell optimizer,
  • Thermal and electrical parameters

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