PEMFC remaining useful life prediction method combined particle swarm optimization algorithm with improved echo state network

In order to improve the accuracy of degradation prediction of proton exchange membrane fuel cell(PEMFC), a PEMFC voltage prediction method based on particle swarm optimization(PSO) algorithm to optimize the revised echo state network(RESN) is proposed. The nonlinear fitting process is accelerated by improving the connection mode of each neuron in the echo state network reservoir. The PSO algorithm is used to optimize the model spectral radius, leakage rate, number of neurons, etc, to improve the prediction accuracy of the model. The SG (Savitzky-Golay) filtering algorithm is used to retain the original trend of the original data and effectively remove the peak and noise. The PSO-RESN was used to accurately predict the PEMFC voltage. Different sample data sets are used as training sets and test sets, and the proposed model is compared with extended Kalman filter and traditional echo state network under static and quasi-dynamic experimental data sets. The results show that when the proportion of the training set is 80%, for the static condition FC1, compared with ESN, the root mean square error(RMSE) and the average percentage error(MAPE) of PSO-RESN method are reduced by 17.50% and 25.53%, respectively. For the quasi-dynamic condition FC2, compared with ESN, RMSE and MAPE are reduced by 16.93% and 21.28%, respectively. The proposed method can achieve higher precision degradation trend and remaining useful life prediction of PEMFC.