Data-driven Prognostics for PEMFC Systems by Different Echo State Network Prediction Structures

Abstract

Limited durability, high cost, and low reliability are the key barriers to large-scale commercial applications of Proton Exchange Membrane Fuel Cell (PEMFC) systems. The discipline of Prognostic and Health Management (PHM) provides an efficient solution to improve the system durability and extend its lifespan. As a promising data-driven method of prognostic, the computational efficiency of Echo State Network (ESN) is much improved compared with traditional Recurrent Neural Network (RNN). The ESN has been used in the literature to realize the degradation prediction of PEMFC systems. Nevertheless, the prediction accuracy and the practical application need to be further stressed. Compared with the fixed output weight matrix structure of ESN, the advanced structure of the moving weight matrix is used to improve the prediction accuracy. In addition, the iterative structure with predicted data is used to improve the practical application. The prediction performance of these prediction structures of ESN is compared and verified based on the data of the 2014 IEEE PHM Data Challenge.