Parameter extraction of PEMFC via Bayesian regularization neural network based meta-heuristic algorithms

Bo Yang, Danyang Li, Chunyuan Zeng, Yijun Chen, Zhengxun Guo, Jingbo Wang, Hongchun Shu, Tao Yu and Jiawei Zhu

Energy, 2021, vol. 228, issue C

Abstract: It is essential to establish an accurate model for precise and reliable evaluation of the characteristics of proton exchange membrane fuel cell (PEMFC). However, the inherent multi-variable, multi-peak, and nonlinear features of PEMFC seriously increase the difficulty and complexity of its parameter extraction. Besides, noised data, which is inevitable in various operation conditions, usually hinders meta-heuristic algorithms (MhAs) to obtain high-quality PEMFC parameters. For the sake of solving these obstacles, a Bayesian regularized neural network (BRNN) based parameter extraction strategy of PEMFC is proposed. Furthermore, performance of the proposed approach is thoroughly evaluated and analyzed through a comprehensive comparison with several advanced MhAs under various operation conditions. Lastly, simulation results verified that BRNN based MhAs (BRNN-MhAs) can effectively extract the parameters of PEMFC with higher accuracy, faster speed, and enhanced stability. In particular, the accuracy of parameter extraction of PEMFC is growing by 34.18%.

Keywords: PEMFC; Hydrogen; Parameter extraction; Meta-heuristic algorithm; Bayesian regularized neural network (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544221008410
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:228:y:2021:i:c:s0360544221008410

DOI: 10.1016/j.energy.2021.120592

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 ().