Performance degradation decomposition-ensemble prediction of PEMFC using CEEMDAN and dual data-driven model

Changzhi Li, Wei Lin, Hangyu Wu, Yang Li, Wenchao Zhu, Changjun Xie, Hoay Beng Gooi, Bo Zhao and Leiqi Zhang

Renewable Energy, 2023, vol. 215, issue C

Abstract: Proton exchange membrane fuel cells (PEMFCs) are essential modern sustainable energy generation devices. Since such an electrochemical system has a limited lifetime, accurately estimating its performance degradation is critical for practical applications. When a large amount of measurement data is available, many nonlinear forecasting methods can be used to predict the performance degradation of a PEMFC system, and the prediction accuracy can be improved by optimizing the structure and parameters of the algorithm. However, the voltage recovery phenomenon would pose a challenge to the classical data-driven methods. In this work, we propose a novel hybrid data-driven PEMFC performance prediction framework by exploring the extensive degradation information buried in the voltage decay data. With complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the raw voltage data are first decomposed into sequences of multiple time scales. Then, the linear and nonlinear components in the decomposed sequences are predicted by autoregressive integrated moving average (ARIMA) and the attention-based gated recurrent unit (GRU), respectively. Comparative studies show that the proposed method can improve the prediction performance by 42.6%–84.2% on FC1 and 35.0%–90.6% on FC2, compared to state-of-the-art algorithms on the basis of an open-source dataset of PEMFCs.

Keywords: Fuel cell; Degradation prediction; Decomposition; Hybrid framework; Attention mechanism (search for similar items in EconPapers)
Date: 2023
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/S0960148123008133
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:renene:v:215:y:2023:i:c:s0960148123008133

DOI: 10.1016/j.renene.2023.118913

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

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