Prediction of Degradation Behavior in Proton Exchange Membrane Fuel Cells Based on Bi-Long Short-Term Memory and Attention Mechanism
Yang Duo1, Lü Haoran1, Li Mince2, Tian Jiaqiang2, Liao Yuefeng1
1. School of Electrical and Information Engineering Zhengzhou University Zhengzhou 450000 China; 2. School of Electrical Engineering and Automation Anhui University Hefei 230601 China
Abstract：The lifetime and performance degradation of proton exchange membrane fuel cells (PEMFCs) are critical factors limiting their widespread application. At the same time, this issue represents one of the core research topics in the field of fuel cell technology. Accurately predicting the voltage degradation behavior of fuel cells not only aids in a deeper understanding of their degradation mechanisms but also facilitates the prediction and analysis of their operational states, thereby significantly enhancing system reliability and performance stability. An analysis of PEMFC aging data reveals that the aging process exhibits high nonlinearity and time dependency, making it challenging to comprehensively characterize the degradation process using traditional modeling methods. To address the difficulty of predicting PEMFC voltage degradation, this paper proposes a prediction model for fuel cell degradation behavior that integrates a bidirectional long short-term memory network (Bi-LSTM) with the attention mechanism. The proposed model first selects features that reflect the degradation behavior of fuel cells as inputs. It utilizes the Bi-LSTM to extract long-term dependencies from the data and predict temporal sequence information, fully capturing the dynamic characteristics of the degradation behavior. Subsequently, the attention mechanism enhances the weight allocation of key features, ultimately yielding PEMFC degradation prediction results. During model training, to improve training efficiency, optimize prediction performance, and effectively avoid overfitting and convergence issues caused by improper learning rate settings, the paper employs techniques such as early stopping, optimal model checkpointing, and adaptive learning rate adjustment. This study uses the PEMFC aging dataset from Tongji University, with the first 60% of the data serving as the training set and the remaining 40% as the test set, to conduct model training and testing. Systematic comparative analyses with other classical neural network models (e.g., convolutional neural networks) are also performed. Experimental results demonstrate that the proposed Bi-LSTM-Attention (Bi-LSTM-At) model exhibits outstanding prediction performance, achieving a root mean square error (RMSE) of only 0.006 1 V. The prediction accuracy improves by 23% compared to traditional neural network models, enabling precise prediction of PEMFC degradation behavior. Through multi-step prediction experiments, the proposed model has been verified to capture the overall voltage degradation trend even over extended periods, demonstrating strong long-term predictive capability. This indicates that the model achieves excellent forecasting performance and accuracy in long-term predictions. Moreover, the proposed model can accurately predict degradation behavior in both the early and late stages of operation, reflecting robust performance. This robust predictive ability provides effective technical support for fuel cell health monitoring and remaining useful life assessment. Based on the experimental results and comparisons with existing traditional algorithms, the following conclusions are drawn: (1) The proposed method converges rapidly and exhibits excellent fitting performance. (2) Compared with conventional Transformer and LSTM models, this method shows significant advantages in terms of prediction accuracy and overall model performance. (3) The introduction of the attention mechanism significantly enhances the prediction performance and accuracy of most models; compared to the Bi-LSTM model, incorporating the attention mechanism substantially reduces training time. (4) The method features a simple structure and is easy to implement, with strong real-time performance and stability, thereby achieving superior prediction accuracy for time-dependent data.
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2026, Vol. 41 
