Proton exchange membrane fuel cells (PEMFCs) are considered promising solutions to address global energy and environmental challenges. This is largely due to their high efficiency in energy transformation, low emission of pollutants, quick responsiveness, and suitable operating conditions. However, their widespread application is limited by high cost, limited durability and system complexity. To maintain system reliability and optimize cost-effectiveness, it is essential to predict the remaining operational lifespan of PEMFC systems with precision. This study introduces a prediction framework integrating a dual-layer bidirectional LSTM architecture enhanced by an attention mechanism for accurately predicting the RUL of PEMFCs. Raw data is preprocessed, and important features are selected by the smoothing technique and random forest method to reduce manual intervention. To enhance model adaptability and predictive accuracy, the Optuna optimization framework is employed to automatically fine-tune hyperparameters. The proposed prediction model is benchmarked against several existing approaches using aging datasets from two separate PEMFC stacks. Experimental findings indicate that the proposed two-layer BiLSTM with attention mechanism surpasses other baseline models in performance. Notably, the designed prediction model demonstrates strong performance on both benchmark datasets and real-world data acquired through a custom-built experimental fuel cell platform. This research offers meaningful guidance for prolonging the service life of PEMFCs and enhancing the efficiency of maintenance planning.
Niu et al. (Thu,) studied this question.