Reversible degradation detection-identification-avoidance and deep-learning co-driven fuel cell remaining useful life prediction
Résumé
A major obstacle to achieving large-scale applications of fuel cells in smart cities is the lack of durability of the stack. Accurate prediction of the Remaining Useful Life (RUL) of a fuel cell becomes a key prerequisite for enhancing its durability. However, reversible degradation behavior in fuel cell operation not only challenges the reliability of degradation trend prediction, but also affects the accurate estimation of RUL. To address this issue, this work proposes a reversible degradation detection-identification-avoidance and deep learning codriven prognostic strategy. Diagnostic techniques are utilised to detect and isolate reversible degradation. The interference of data imbalance on model training is improved by a data augmentation approach incorporating physical knowledge. The prognostic strategy proposed in this paper is validated on a dynamic operating conditions dataset, and the results show an average improvement of 22% to 62% in the prognostic horizon matching rate.
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