@inproceedings{02c8913fc18d4d24a2f9ec6f6c93279c,
title = "Augmenting RC-GRU with ARIMA-Generated Trends: A Data-Efficient Framework for PEMFC Degradation Prediction",
abstract = "This paper proposes a novel data-driven method for long-term health prediction of proton exchange membrane fuel cell (PEMFC). In specific, the autoregressive integrated moving average (ARIMA) method is exploited to learn the long-term linear trend of degradation. Relying on the learned trend, the residual convolutional gated recurrent unit (RC-GRU) network is introduced, for the first time, to learn the local nonlinearity of degradation with short-segment degradation data. By combining the merits of two methods, the proposed ARIMA-RC-GRU approach can predict the future degradation of PEMFC accurately with only small sampling data.",
keywords = "Proton exchange membrane fuel cell, data-driven method, degradation features, lifespan prediction",
author = "Yuqian Hu and Zhongbao Wei and Hasanien, \{Hany M.\} and Hongwen He",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Symposium on the Application of Artificial Intelligence in Electrical Engineering, AAIEE 2025 ; Conference date: 25-04-2025 Through 28-04-2025",
year = "2025",
doi = "10.1109/AAIEE64965.2025.11100956",
language = "English",
series = "2025 IEEE International Symposium on the Application of Artificial Intelligence in Electrical Engineering, AAIEE 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "372--376",
booktitle = "2025 IEEE International Symposium on the Application of Artificial Intelligence in Electrical Engineering, AAIEE 2025",
address = "United States",
}