Abstract
The Proton Exchange Membrane Fuel Cell (PEMFC) is a fast-developing battery technology, and the key to its reliability and lifespan improvement lies in the accurate assessment of durability. However, the degradation mechanism of the PEMFC is hard to determine and its internal parameters are highly coupled. Thus, the development of a more accurate life prediction model that meets the actual scenarios is to be investigated urgently. To solve this problem, a multi-feature fusion life prediction method based on the Temporal Convolutional Network-Gated Recurrent Unit (TCN-GRU) is proposed. A TCN algorithm is used as the prediction base model, and two GRU modules are included with the model to strengthen the model's expression ability and improve its predictive accuracy. Two widely recognized datasets and two operating conditions are utilized for model training and prediction, respectively. Comparisons are made with single-feature parameter models in terms of Root Mean Square Error (RMSE) and the Determination Coefficient (R(2)). The results show that the prediction accuracy of the TCN-GRU multi-feature fusion model is higher than that of the single-feature models in terms of stability and anti-interference under both operating conditions. The accuracy of the TCN-GRU (three-feature) model is the most optimal in a steady-state condition at 80% of the training set ratio (RMSE = 3.27 × 10(-3), R(2) = 0.965). Furthermore, with the increase in the input feature parameter, the TCN-GRU model is closer to the real value, which proves once again that the proposed model can meet the accuracy requirements of the life prediction of the PEMFC.