Degradation prediction of PEM fuel cell using LSTM based on Gini gamma correlation coefficient and improved sand cat swarm optimization under dynamic operating conditions
Ruike Huang,
Xuexia Zhang,
Sidi Dong,
Lei Huang and
Yuan Li
Applied Energy, 2025, vol. 392, issue C, No S030626192500697X
Abstract:
Accurate longevity prediction is crucial for the optimal functioning of Proton Exchange Membrane Fuel Cells (PEMFC). This paper proposes the relative voltage loss rate (RVLR) as a novel aging indicator to overcome the limitations of traditional static indicators like voltage and power, which fall short under dynamic operating conditions. An innovative approach using an LSTM neural network model enhanced by Improved Sand Cat Swarm Optimization (ISCSO) and the Gini Gamma Correlation Coefficient method (GG) is presented for predicting PEMFC degradation across variable conditions. This method first conducts a Gini Gamma correlation analysis, then employs the LSTM network to forge a degradation prediction model for the PEMFC, optimizing the number of neurons in the hidden layer, initial weights, and iteration count through ISCSO. Comparative discussions with eight alternative methods and validations through aging experiments on PEMFC under two different conditions underscore the accuracy of this new approach in various application environments. Specifically, the ISCSO-LSTM model achieves R2 values of 0.9713 and 0.9822, MAE values of 0.0026 and 0.0019, and RMSE values of 0.0050 and 0.0032 across the two different datasets, respectively. These results demonstrate the robustness, accuracy, and reliability of the proposed method for accurate degradation prediction.
Keywords: Proton exchange membrane fuel cells; Relative voltage loss rate; Gini gamma correlation coefficient method; Improved sand cat swarm optimization algorithm; Long short-term memory; Degradation prediction (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030626192500697X
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:392:y:2025:i:c:s030626192500697x
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.2025.125967
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 ().