Degradation prediction of proton exchange membrane fuel cell based on grey neural network model and particle swarm optimization

The degradation prediction is an effective tool for the long-lasting operation of the proton exchange membrane fuel cells (PEMFC). In this paper, a novel grey neural network model (GNNM) method combined with the particle swarm optimization (PSO) and the moving window method is presented to forecast the degradation of PEMFC under different operating conditions. The proposed method considers the influence of load current, inlet temperature, inlet hydrogen pressure, and inlet relative humidity. The degradation prediction model of PEMFC is established by a grey neural network. The initial weight and threshold of established GNNM are optimized by the PSO. The optimized PSO-GNNM is iteratively trained based on the moving window method through several newly measured data. The influence of different moving window sizes on the degradation prediction of the PEMFC under the static load current is investigated. Then, a comparison between the proposed method and the adaptive neuro-fuzzy inference system method is discussed. Moreover, the influence of the load current on the degradation prediction of PEMFCs in postal fuel cell electric vehicle operating under real conditions is analyzed. Finally, the proposed method is validated using 3 PEMFC aging experiments under different conditions. The results show that the proposed method can precisely forecast the degradation for PEMFC on different applications.