Prediction of PEMFC life based on IGJO-TCN-BiGRU-Attention

To accurately predict the life of proton exchange membrane fuel cells (PEMFC), the IGJO-TCN-BiGRU-Attention algorithm is proposed, which integrates an intelligent optimization algorithm into a deep learning framework. Temporal convolutional network (TCN) decodes spatial correlations in voltage-temperature parameters. Bidirectional gated recurrent units (BiGRU) models performance degradation dynamics. Attention mechanism makes the model focus on the part that contributes the most to the predicted outcome. The improved golden jackal optimization (IGJO) algorithm proposed in this work is employed to optimize the learning rate, L₂ regularization parameters, number of BiGRU neurons, and key-value pairs of the attention mechanism in the TCN-BiGRU-Attention hybrid model. Subsequently, the optimized hybrid model is utilized to predict the life of PEMFC. Superior convergence speed and optimization accuracy of the proposed IGJO algorithm are validated through comparative simulations with five intelligent algorithms. Further validation under static and dynamic operating conditions with varying training durations exhibits significantly enhanced prediction accuracy of the IGJO-TCN-BiGRU-Attention algorithm. Under static operating conditions at 577 hours, a 67.78% reduction in root mean square error (RMSE) is achieved compared to TCN. Similarly, under dynamic conditions at 510 hours, a 22.5% RMSE reduction is observed versus TCN.