AI-enhanced RUL prediction of PEMFCs under dynamic operating conditions using XGBoost-based HI extraction and hybrid transformer-GRU model
提出一种基于XGBoost从电压数据中提取退化健康指标、结合Transformer和GRU混合模型的方法，在动态工况下准确预测质子交换膜燃料电池剩余寿命，并在工业数据集上验证了优越性。
Proton Exchange Membrane Fuel Cells (PEMFCs) are critical for zero-emission energy systems, particularly in electro-hydrogen generators (GEH2). Accurate Remaining Useful Life (RUL) prediction is crucial for ensuring operational reliability and enabling predictive maintenance. However, dynamic operating conditions present a significant challenge for existing prognostic approaches, particularly in extracting robust Health Indicators (HIs). Conventional HIs, often based on voltage or power, are highly sensitive to mission profiles and fail to generalize in real-world conditions. To address this limitation, we propose a novel data-driven approach based on XGBoost regression to extract a degradation-specific HI directly from raw voltage measurements. This method effectively filters out transient fluctuations caused by varying power demands, isolating the true degradation trend without requiring complex preprocessing or domain expertise. Leveraging the extracted HI, we introduce a hybrid deep learning model that combines Transformer networks and Gated Recurrent Units (GRUs) to capture temporal dependencies and provide accurate RUL predictions under dynamic conditions. Explainable AI techniques are integrated to interpret the model’s predictions and analyze the influence of operational variables on fuel cell degradation. The proposed framework is validated on a real-world industrial dataset from four PEMFC stacks operating in GEH2 systems. Experimental results demonstrate superior accuracy, robustness, and generalizability compared to state-of-the-art methods, highlighting the potential of this scalable and interpretable approach for predictive maintenance in complex industrial environments.