A novel WaveNet-GRU deep learning model for PEM fuel cells degradation prediction based on transfer learning

Abstract

Precise prediction of Remaining Useful Life (RUL) within the transportation industry is essential for cost reduction and enhanced energy efficiency, focusing on extending the operational lifespan of proton exchange membrane fuel cells (PEMFCs). In pursuit of this objective, this study employs data-driven prediction methodologies centered on deep neural networks and transfer learning. The fundamental premise is that these approaches hinge on the compatibility of functional conditions across diverse datasets. Multiple strategies, amalgamating transfer learning, and deep neural networks, are introduced to forecast the PEMFC stack's behavior and its associated RUL. Network hyperparameters are optimized through Bayesian optimization, targeting root-mean-square error (RMSE) minimization in voltage predictions. The efficacy of these prediction techniques is evaluated through essential performance metrics, including the mean absolute percentage error (MAPE), RMSE, and coefficient of determination (R2), applied to both voltage predictions and RUL estimations. For the first time, a WaveNet-GRU model has been developed. Comparative assessment of models trained on 50% of the dataset underscores its supremacy. This model attains R2, RMSE, and MAPE scores of 99.1, 2.16E-4, and 0.166E-1, respectively, in predicting stack voltage. Also, RUL has increased by 21% compared to the best contemporary research. The WaveNet-GRU model demonstrates exceptional transfer learning capabilities when applied to stacks influenced by current ripples. In this context, it achieves optimal R2, RMSE, and MAPE values of 99.69, 1.37E-4, and 0.31E-1, respectively.