Integrating traffic velocity data into predictive energy management of plug-in hybrid electric vehicles

Abstract

Recent advances in the traffic monitoring systems have made traffic velocity information accessible in real time. This paper proposes a supervised predictive energy management framework aiming to improve the fuel economy of a power-split plug-in hybrid electric vehicle (PHEV) by incorporating dynamic traffic feedback data. Compared with conventional model predictive control (MPC), an additional supervisory state of charge (SOC) planning level is constructed in this framework. A power balance PHEV model is developed for this upper level to rapidly generate optimal battery SOC trajectories, which are utilized as final state constraints in the MPC level. The proposed PHEV energy management framework is evaluated under three different scenarios: (i) without traffic information, (ii) with static traffic information, and (iii) with dynamic traffic information. Simulation results show that the proposed control strategy successfully integrates dynamic traffic velocity into the PHEV energy management, and achieves 5% better fuel economy compared with when no traffic information is utilized.