A trip distance adaptive real-time optimal energy management strategy for a plug-in hybrid vehicle integrated driving condition prediction

Abstract

To achieve better fuel economy for plug-in hybrid electric vehicles (PHEVs), this paper proposes a novel improved adaptive equivalent consumption minimization strategy (A-ECMS) integrated driving condition prediction using artificial neural network (ANN) combined the least-squares with forgetting factors. Firstly, the ANN method and the least-square with forgetting factors are used to predict the velocity of the vehicle and the slope of the road. Then a trip adaptive ECMS is proposed which the equivalent factor (EF) is adjusted in real-time according to the remaining distance. Furthermore, the driving condition prediction technology is integrated into A-ECMS to decrease fuel consumption further. Besides, the impact of different preview horizon lengths on fuel consumption is analyzed. Finally, a simulation study is conducted for applying the proposed strategy to a practical trip path in the Fuzhou road network. Simulation results show that, compared with CD-CS, the A-ECMS combined with driving condition prediction can achieve better fuel economy with a fuel consumption reduction by 12.1%, which effectively improves the fuel economy of the PHEV.