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12 December 2024 Remaining useful life prediction of PEMFC based on informer model
Yihong Liu, Zhongjian Kang, Yichao Shen, Yi Shen, Chenguang Zhang
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Proceedings Volume 13419, Tenth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2024); 134193B (2024) https://doi.org/10.1117/12.3050206
Event: Tenth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2024), 2024, Lhasa, China
Abstract
The prediction of the remaining useful life (RUL) of proton exchange membrane fuel cells (PEMFC) is one of the reasons limiting their large-scale application, and is a key focus of current researchers. Time series prediction methods for PEMFCs based on sliding windows, such as CNN and LSTM, have larger errors as the longer the time series prediction. To address the issue of error accumulation in the RUL prediction of PEMFC using traditional time series methods, this paper innovatively introduces the Informer model into the RUL prediction of PEMFC. Informer builds upon the Transformer architecture by incorporating a self-attention mechanism to mitigate the accumulation of prediction errors and improve the prediction effect of the model. By comparing the predictive results of the static and semi-dynamic PEMFC datasets with those of the traditional LSTM, it is verified that the Informer model has excellent performance in PEMFC time series prediction. The predictive results indicate that the Informer can significantly improve the accuracy of the RUL prediction of PEMFC to a certain extent.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yihong Liu, Zhongjian Kang, Yichao Shen, Yi Shen, and Chenguang Zhang "Remaining useful life prediction of PEMFC based on informer model", Proc. SPIE 13419, Tenth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2024), 134193B (12 December 2024); https://doi.org/10.1117/12.3050206
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KEYWORDS
Data modeling
Data processing
Transformers
Feature extraction
Machine learning
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