Recoverable fault behavior oriented diagnostic-prognostic hybrid framework for proton exchange membrane fuel cells

Proton exchange membrane fuel cell (PEMFC) is used in several fields due to its high efficiency and environmental friendliness. Durability issues remain one of the main barriers limiting its large-scale commercialization. Although prognostic techniques can optimize operation and extend fuel cell lifetime, their accuracy is often compromised by recoverable fault behaviors. Therefore, addressing this interference has become a crucial challenge in achieving reliable PEMFC prognostics. To tackle these issues, a diagnostic-prognostic hybrid framework integrating data-driven recoverable fault recognition with model-based degradation prediction is proposed. Specifically, a recoverable fault recognition model is developed to determine the operational state of the fuel cell, where data augmentation is employed to mitigate the adverse effects of data imbalance on classification performance. Using a prognostic strategy-guided particle filter (PSG-PF) model for degradation trend prediction and remaining useful life (RUL) estimation. The proposed method is evaluated by the dynamic load aging experimental data of PEMFC. The results show that the proposed method achieves over 98% accuracy in recoverable fault recognition. Meanwhile, by effectively circumventing the impact of recoverable faults, the average relative accuracy of predicted RUL reached 86%. The proposed framework can achieve reliable recognition of recoverable faults and credible RUL prediction under complex operating conditions. It enhances the robustness of PEMFC prognostics, supporting longer lifetime and more stable operation of fuel cells.