A novel method of long-term aging prediction for proton exchange membrane fuel cell under the dynamic load cycling condition

Abstract

Estimating the remaining useful life (RUL) of proton exchange membrane fuel cell (PEMFC) is beneficial for deploying control strategies. Long-term aging prediction forms the foundation for RUL estimation, but currently long-term aging prediction and RUL estimation for dynamic load cycling conditions have not been achieved, and uncertainty during the prediction and estimation processes has not been considered. Therefore, in this study, a novel hybrid prediction framework based on Bayesian gated recurrent unit, model uncertainty and state estimation (MUSE), and convolutional neural network-long short-term memory (CNN-LSTM) is proposed. First, MUSE is used to simultaneously quantify the state of health (SOH) and predict aging trend to generate a guiding sequence Next, based on the sequence and historical experimental data, CNN-LSTM is trained and multi-step prediction is performed. Subsequently, Bayesian gated recurrent unit is employed to fuse the MUSE-based prediction sequence with the CNN-LSTM-based prediction, and confidence interval is provided. This results in a novel model and data-driven long-term aging prediction structure. Finally, SOH during the prediction stage is evaluated by MUSE and RUL is estimated. The proposed method is validated using experimental data under a dynamic load cycling condition, demonstrating accurate long-term aging prediction and RUL estimation while considering uncertainty.