Model predictive control strategy for energy optimization of series-parallel hybrid electric vehicle

Series-parallel hybrid electric vehicle (SPHEV) is a compact and effective configuration of HEV, which has great potential to save fuel consumption. Because of multi power sources (one engine and two electric motors) and various driving conditions, it is difficult to design an optimal energy management strategy (EMS). To obtain better fuel economy, a novel particle swarm optimization based (PSO-based) nonlinear model predictive control (NMPC) strategy is proposed for EMS of SPHEV. First, a nonlinear model predictive control framework is designed. Then, a modified particle swarm optimization is used for receding horizon optimization. Next, in order to realize fast computing, a two-steps optimization method is adopted. Finally, the proposed strategy are verified by simulations based on the data of a real bus and a driving cycle. The results show that the fuel consumption of SPHEV is greatly decreased by more than 10% compared to that with CD-CS strategies.