Degradation prediction of proton exchange membrane fuel cell based on mixed gated units under multiple operating conditions

Abstract

The aging process of proton exchange membrane fuel cell (PEMFC) is affected by different operating factors, and its practical application scenarios involve multiple operation conditions. To improve the accuracy and controllability of aging prediction, a mixed recurrent neural network (RNN) model based on Gated Recurrent Unit (GRU) and Minimal Gated Unit (MGU) is proposed. The model dynamically adjusts the mixed weights of GRU and MGU throughout the entire training process to obtain the optimal prediction network structure throughout the entire lifecycle. Furthermore, an attention mechanism is incorporated into the mixed gated unit (MIXGU) model. The effectiveness of the MIXGU model is evaluated by utilizing experimental data from static condition, quasi-dynamic condition, and dynamic load cycling conditions. The predictive performance of MGU, GRU, MIXGU, and MIXGU model with attention mechanism (AT-MIXGU) are compared under different operating conditions. The validation results indicate that MIXGU model exhibits superior predictive performance compared to single gated unit, the attention mechanism enhances prediction accuracy. And the AT-MIXGU model demonstrates strong generalization capabilities, and well-suited for accurately predicting PEMFC aging under diverse operating conditions.