Deep Echo State Network for Predicting Degradation and Remaining Useful Life in Proton Exchange Membrane Fuel Cells
Year: 2026
Type: article
Abstract: Introduction: The limited durability caused by the degradation of proton exchange membrane fuel cells (PEMFCs) hinders their large-scale application in various fields. Understanding the degradation trends and effectively predicting the remaining useful life (RUL) can significantly improve the durability of PEMFCs. Methods: This paper proposes a long-term prediction method based on the Deep Echo State Network (DESN). First, the input data are screened and smoothed using wavelet threshold denoising. Next, the Gaussian kernel function is introduced into the deep echo state network to establish a prediction model. Finally, the degradation and remaining useful life of PEMFCs are predicted under steady-state and dynamic operating conditions. Results: Under steady-state conditions, when the training duration is 450 h, degradation prediction is optimal, with RMSE and MAPE of 0.0156 and 0.0036, respectively. The prediction error of the RUL is the lowest at 19.31%. Under dynamic conditions, degradation prediction is also satisfactory, with RMSE and MAPE of 0.0281 and 0.0069, respectively, and the prediction error of RUL is 12.61%. Discussion: The DESN method can effectively predict degradation and RUL of PEMFCs. However, further validation with real-world data and automated hyperparameter tuning are required to ensure broader applicability beyond laboratory conditions. Conclusion: The proposed method, combining wavelet threshold denoising and the Gaussian kernel function, effectively extracts data features, improving the prediction accuracy of degradation trends and remaining useful life for PEMFCs under various operating conditions. This demonstrates its potential to enhance durability and support practical applications in real-world energy systems.
Authors: Lieqi Zhang, Desong Jiang, Jianfeng Wang
Institution: Jimei University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7125511903