Correlation analysis and prediction of PEM fuel cell voltage during start-stop operation based on real-world driving data

Ruike Huang, Yiqiang Peng, Jibin Yang, Xiaohui Xu and Pengyi Deng

Energy, 2022, vol. 260, issue C

Abstract: The accurate prediction of fuel cell voltage is critical for fuel cell health management, which directly affects the performance and cost of fuel cell vehicles. This paper proposes analysis and prediction approaches for the start-stop voltage of fuel cell based on data from a proton exchange membrane fuel cell (PEMFC) bus operated under real traffic conditions in Chengdu, China. First, three correlative analysis methods are used to identify the correlation between the PEMFC voltage and other variables during start-stop operation. Then, the original sequence of PEMFC voltage at start-stop is processed using the grey model, residual grey model, logarithmic-power function grey model, and cotangent function grey model. Four grey prediction models are combined and optimized based on a backpropagation neural network (BPNN). Finally, an organic grey BPNN model (OGNNM) is established to predict the PEMFC voltage during start-stop operation and analyze its accuracy. Results indicate that the PEMFC voltage changes sharply during start-stop, which is closely connected to the current, hydrogen gas tank pressure, and PEMFC stack temperature. In addition, the proposed OGNNM has higher prediction precision than the other data-driven models that provides a basis for the control of fuel cell hybrid systems.

Keywords: PEMFC bus; Correlation analysis; Start-stop voltage prediction; Logarithmic-power and cotangent function transmissions; Organic grey BP neural network (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422201831X
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:260:y:2022:i:c:s036054422201831x

DOI: 10.1016/j.energy.2022.124930

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 ().