Performance degradation trend prediction of proton exchange membrane fuel cell based on GA-TCN

Abstract

To improve the prediction accuracy of the performance degradation trend of proton exchange membrane fuel cell (PEMFC), this paper proposes a temporal convolutional network (TCN) model based on genetic algorithm (GA) optimization to predict the performance degradation trend of PEMFC. Firstly, variational mode decomposition and wavelet threshold denoising algorithms are used to denoise the original data. Then the hyperparameters of the TCN model are optimized by GA, and the GA-TCN model for predicting the performance degradation trend of PEMFC is constructed. Finally, this paper uses the PEMFC stack degradation experimental dataset disclosed in the IEEE PHM 2014 Data Challenge to verify, and compares the proposed model with the backpropagation neural networks model, the long short-term memory model and the classical TCN model. The results show that the proposed method has the highest performance degradation trend prediction accuracy. In particular, when the training dataset accounts for 30%, i.e. the training samples are small, the root mean square error, mean absolute error and mean absolute percentage error of the GA-TCN model are 0.004 726, 0.003 119 and 9.62%, respectively, which are 14.48%, 20.05% and 2.42% lower than that of the classical TCN model. Consequently, this methodology can forecast the degradation trend of PEMFC with high accuracy.