Nonlinear multivariable modeling of locomotive proton exchange membrane fuel cell system

A nonlinear multivariable model of a locomotive proton exchange membrane fuel cell (PEMFC) system based on a support vector regression (SVR) is proposed to study the effect of different operating conditions on dynamic behavior of a locomotive PEMFC power unit. Furthermore, an effective informed adaptive particle swarm optimization (EIA-PSO) algorithm which is an adaptive swarm intelligence optimization with preferable search ability and search rate is utilized to tune the hyper-parameters of the SVR model for the improvement of model performance. The comparisons with the experimental data demonstrate that the SVR model based on EIA-PSO can efficiently approximate the dynamic behaviors of locomotive PEMFC power unit and is capable of predicting dynamic performance in terms of the output voltage and power with a high accuracy.