Accurately predicting long-term degradation patterns in proton exchange membrane fuel cell (PEMFC) stacks 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 show that MBFNet achieves 18.6% lower prediction error and 36.8% fewer parameters than the long short-term memory (LSTM)-attention benchmark under real operating scenarios. In multi-step prediction tasks, MBFNet reduces root mean square error by an average of 24.5% relative to LSTM-attention and 55.2% relative to a one-dimensional convolutional neural network (1D-CNN) across four prediction horizons, better satisfying automotive application requirements. Moreover, MBFNet exhibits strong physical interpretability, making it efficient to implement and promising for practical deployment.
Wang et al. (Mon,) studied this question.