A novel online degradation model for proton exchange membrane fuel cell based on online transfer learning

Abstract

A novel online degradation prediction model is proposed to prognosticate the future degradation trend (FDT) of proton exchange membrane fuel cell (PEMFC) stack in this paper. In order to overcome the fact that existing FDT prediction methods of PEMFC stack based on data-driven model rely on the assumption that the operating conditions of the training data and testing data need to be consistent, an end-to-end prediction algorithm based on the combination of transfer learning and transformer neural network, referred to as TLTNN, is proposed to predict the FDT of PEMFC stack. Besides, in order to demonstrate the effectiveness and superiority of the proposed method in prognostics tasks of PEMFC stack FDT, the prediction performance is validated on the PEMFC test system. The results show that the RMSE, MAE and MAPE values of the predicted degradation voltage are 0.00598 V, 0.004842 V and 0.1518%, respectively, which indicates that the proposed TLTNN strategy based on transfer online learning can be used to predict the degradation voltage of PEMFC stack and the superiority of the proposed method is better, thus solving the problem that the distribution of training and test data must be the same in traditional machine learning models.