Energy management strategy based on GIS information and MPC for a heavy-duty dual-mode power-split HEV

Abstract

The control performance of energy management strategy (EMS) in heavy-duty dual-mode power-split hybrid electric vehicles (PSHEV) are highly dependent on the forecasted velocity and battery state of charge (SOC) planning. In this paper, a model predictive control (MPC)-based energy management strategy is proposed, in which the predicted velocity and SOC trajectory is regarded as reference signal. The velocity predictor is designed based on radial basis function neural network (RBF-NN), and the battery SOC trajectory is planned using the road grade information from Geographic Information System (GIS). The proposed strategy is verified by a Matlab/simulink model. The results indicate that the fuel economy of PSHEV is improved by considering velocity prediction and SOC trajectory planning.