A Comparative Study of Fuel Cell Prediction Models Based on Relevance Vector Machines with Different Kernel Functions 2021-01-0728
Fuel cell reactors, as the core components of fuel cell vehicles, have a short life problem that has always limited the development of fuel cell vehicles. The life attenuation curve of fuel cell shows nonlinear characteristics, and there is no model that can accurately predict its effect. This paper is based on the experimental data of the vehicle fuel cell reactor, which is derived from the 600 h durability test run by a 4 kW fuel cell reactor. The relevance vector machine, as a Bayes processing method that supports vector machine, is a data-driven method based on kernel functions. The regression model is established by the relevance vector machine, and the super-parameters are found by genetic algorithm, because the kernel function strongly affects the nonlinearity of the curve, and the decay curve of fuel cell reactor performance is predicted according to four different kernel functions. In this paper, according to four different kernel functions: polynomial kernel function, Gaussian radial basis kernel function, Sigmoid kernel function and the mixed kernel function, the corresponding prediction effect is discussed. The comparative analysis and error analysis of the prediction results of the relevance vector machine regression model of these four kernel functions show that the regression effect and prediction effect of the mixed kernel functions are the best, have better learning ability, and combine the advantages of two different kernel functions. The results of this paper can provide reference for the prediction of the performance attenuation of fuel cell reactors.