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Real-time degradation modeling for automotive PEMFC stacks: a multi-scale fusion network validated on an industrial 215-channel system
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Zifei WANG1,2, Xiangxian ZHU3, Congxin LI4, Daidai CHEN3, Zhitao LIU2, Longhua MA1, Jili TAO1, Hongye SU2. Real-time degradation modeling for automotive PEMFC stacks: a multi-scale fusion network validated on an industrial 215-channel system[J]. Journal of Zhejiang University Science A, 1998, -1(-1): .
@article{title="Real-time degradation modeling for automotive PEMFC stacks: a multi-scale fusion network validated on an industrial 215-channel system",
author="Zifei WANG1,2, Xiangxian ZHU3, Congxin LI4, Daidai CHEN3, Zhitao LIU2, Longhua MA1, Jili TAO1, Hongye SU2",
journal="Journal of Zhejiang University Science A",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2500337"
}
%0 Journal Article
%T Real-time degradation modeling for automotive PEMFC stacks: a multi-scale fusion network validated on an industrial 215-channel system
%A Zifei WANG1
%A 2
%A Xiangxian ZHU3
%A Congxin LI4
%A Daidai CHEN3
%A Zhitao LIU2
%A Longhua MA1
%A Jili TAO1
%A Hongye SU2
%J Journal of Zhejiang University SCIENCE A
%V -1
%N -1
%P
%@ 1673-565X
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2500337
TY - JOUR
T1 - Real-time degradation modeling for automotive PEMFC stacks: a multi-scale fusion network validated on an industrial 215-channel system
A1 - Zifei WANG1
A1 - 2
A1 - Xiangxian ZHU3
A1 - Congxin LI4
A1 - Daidai CHEN3
A1 - Zhitao LIU2
A1 - Longhua MA1
A1 - Jili TAO1
A1 - Hongye SU2
J0 - Journal of Zhejiang University Science A
VL - -1
IS - -1
SP -
EP -
%@ 1673-565X
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2500337
Abstract: Accurately predicting long-term degradation patterns in proton exchange membrane fuel cell stacks (PEMFCs) under automotive operating conditions remains challenging. Prediction methods are largely constrained by laboratory-scale experiments and limited stack sizes, resulting in insufficient accuracy and generalization capability. To address these limitations, in this paper we propose a multi-scale bidirectional fusion network (MBFNet) tailored for an industrial 215-channel PEMFC stack, enabling accurate degradation prediction under accelerated real-world dynamic conditions using gas-heat-electricity (GHE) co-simulation data. A channel-joint adaptive noise correlation threshold (NCT) algorithm is introduced to account for variable correlations across sensors and operating conditions without relying on prior physical modeling. A multi-scale decomposition module captures degradation dynamics at different temporal scales, while a bidirectional fusion module integrates global trends and local details into the final prediction. Experimental results showed that MBFNet achieved 18.5% lower prediction error and 36.8% fewer parameters than the LSTM-Attention benchmark under real operating scenarios. In multi-step prediction tasks, MBFNet reduced error by 9% relative to LSTM-Attention and by 40.5% relative to 1D-CNN, better satisfying automotive application requirements. Moreover, MBFNet exhibits strong physical interpretability, making it efficient to implement and promising for practical deployment.
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