Nonlinear multivariable modeling of locomotive proton exchange membrane fuel cell system

•A nonlinear multivariable model of locomotive PEMFC based on an SVR is proposed.•Study operating conditions effect on dynamic behavior of locomotive PEMFC.•The EIA-PSO algorithm is utilized to tune hyper-parameters of SVR model.•Comparisons with experimental data demonstrate that the proposed model can approximate locomotive PEMFC.

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.