A-EMCS for PHEV based on real-time driving cycle prediction and personalized travel characteristics

Abstract

Energy management plays an important role in improving the fuel economy of plug-in hybrid electric vehicles (PHEV). Therefore, this paper proposes an improved adaptive equivalent consumption minimization strategy (A-ECMS) based on long-term target driving cycle recognition and short-term vehicle speed prediction, and adapt it to personalized travel characteristics. Two main contributions have been made to distinguish our work from exiting research. Firstly, online long-term driving cycle recognition and short-term speed prediction are considered simultaneously to adjust the equivalent factor (EF). Secondly, the dynamic programming (DP) algorithm is applied to the offline energy optimization process of A-ECMS based on typical driving cycles constructed according to personalized travel characteristics. The improved A-ECMS can optimize EF based on mileage, SOC, long-term driving cycle and real-time vehicle speed. In the offline part, typical driving cycles of a specific driver is constructed by analyzing personalized travel characteristics in the historical driving data, and optimal SOC consumption under each typical driving cycle is optimized by DP. In the online part, the SOC reference trajectory is obtained by recognizing the target driving cycle from Intelligent Traffic System, and short-term vehicle speed is predicted by Nonlinear Auto-Regressive (NAR) neural network which both adjust EF together. Simulation results show that compared with CD-CS, the fuel consumption of A-ECMS proposed in the paper is reduced by 8.7%.

Keywords

• plug-in hybrid electric vehicle
• energy management
• driving cycle prediction
• dynamic programming
• adaptive equivalent consumption minimization strategy