Prognostic for fuel cell based on particle filter and recurrent neural network fusion structure

Abstract

Fuel cells are considered as one of the most promising candidates for future power source due to its high energy density and environmentally friendly properties, whereas the short lifespan blocks its large-scale commercialization. In order to enhance the reliability and durability of proton exchange membrane fuel cell, a fusion prognostic approach based on particle filter (model-based) and long-short term memory recurrent neural network (data-driven) is proposed in this paper. Both the remaining useful life estimation and the short-term degradation prediction can be achieved based on the prognostic method. For remaining useful life estimation, the particle filter method is used to identify the model parameters in the training phase and the long-short term memory recurrent neural network is used to update the parameters in the prediction phase. As for short-term degradation prediction, the particle filter and long-short term memory recurrent neural network are firstly trained individually in the training phase and then be fused to make predictions in the prediction phase. The proposed fusion structure is validated by the fuel cell experimental tests data, and results indicate that better prognostic performance can be obtained compared with the individual model-based or data-driven method.