Forecasting the output performance of PEMFCs via a novel deep learning framework considering varying operating conditions and time scales

Yulong Yu, Qiang Zheng, Tianyi Zhang, Zhengyan Li, Lei Chen and Wen-Quan Tao

Applied Energy, 2025, vol. 389, issue C, No S0306261925004933

Abstract: Proton exchange membrane fuel cell (PEMFC) represents a significant technology for hydrogen energy conversion and are widely utilized in renewable energy systems. However, their performance tends to degrade over time during operation. Accurate prediction of PEMFCs performance is critical for optimizing hydrogen energy efficiency and ensuring the reliability of renewable energy systems. Meanwhile, the monitoring data collected from PEMFCs exhibit characteristics of diverse types, varying time resolutions, and distinct operating conditions, which complicate accurate predictions. To address this challenge, the feature-fusion and feature-attention blocks are developed to amalgamate interactive information and emphasize key features across various monitoring datasets. Based on the blocks, the feature-fusion and feature-attention deep learning (FFA-DL) framework that incorporates convolutional long short-term memory (ConvLSTM) networks is proposed. To validate the proposed framework, real-world data from two operation conditions, FC1 and FC2, are employed. The results demonstrate that the FFA-DL framework effectively extracts valuable information from complex monitoring data, thereby enhancing the accuracy of PEMFCs performance prediction. FFA-DL significantly enhanced prediction performance of the embedding models for both FC1 and FC2, and the FFA-enhanced ConvLSTM (FFA-ConvLSTM) outperformed other models with R2 of 0.9631 and 0.9946 for FC1 and FC2, respectively. Additionally, the FFA-ConvLSTM exhibited excellent robustness and accuracy for data under varying time resolutions, with R2 exceeding 0.9200 and 0.9800 for FC1 and FC2, respectively.

Keywords: PEMFC; Performance prediction model; Different operation conditions; Time scales; Deep learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261925004933
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:appene:v:389:y:2025:i:c:s0306261925004933

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2025.125763

Access Statistics for this article

Applied Energy is currently edited by J. Yan

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