Lifespan Prediction for Proton Exchange Membrane Fuel Cells Based on Wavelet Transform and Echo State Network

Limited durability is one of the major issues that hinder the large-scale commercialization of the proton exchange membrane fuel cells system. Based on the prognostic technique, predicting the remaining useful life (RUL) efficiently and accurately can help prolong its residual life, especially on the long-term horizon and under different mission profiles. Thus, a data-driven approach of discrete wavelet transform-echo state network-genetic algorithm (DWT-ESN-GA) is proposed to improve the RUL prediction performance. First, the historical datasets are compressed by the DWT. Second, the approximation components of the original data are predicted in the compressed space by ESN. Rather than predicting the degradation data themselves, their shortened coefficients are evaluated to decrease the prediction data points, i.e., from 2016 data points to 253 data points. Besides, a GA is used to optimize the key parameters of ESN, and it can further increase the prediction accuracy. Finally, the inverse DWT is utilized to reconstruct the coming data based on the estimated approximation components. The performance of the proposed approach is evaluated by three different experimental tests under steady-state, quasi-dynamic, and full dynamic operating conditions separately.

Automated summary

In this study, a data-driven approach is proposed to improve the remaining useful life (RUL) prediction performance of proton exchange membrane fuel cells. The proposed approach uses discrete wavelet transform (DWT) to compress historical data and predicts the approximation components using echo state network (ESN) in the compressed space. The key parameters of ESN are optimized using genetic algorithm (GA). Finally, the inverse DWT is used to reconstruct the coming data, and the performance of the proposed approach is evaluated through experimental tests.