Abstract: This paper takes a multi-mode hybrid electric vehicle (HEV) in an intelligent connected environment as the research object and researches economic driving for urban road traffic intersections. Firstly, the phase and timing data of the signal lights are obtained based on the information from the Internet to Vehicles, and a road network model of multiple signal intersections is built according to the constraints such as the position of the signal lights and the speed limit. Meanwhile, a control-oriented multi-mode hybrid powertrain component model and an energy consumption model for assembled cars are established. Secondly, economical driving of a single vehicle is solved based on the Pontryagin minimum principle (PMP). The scenario analysis of the traffic light intersections is performed, the effective speed range of the vehicle without stopping is planned, and the economical speed optimization of continuous signal intersections is analyzed. Finally, the optimal energy management strategy (EMS) for the multi-mode hybrid electric vehicle is solved based on model predictive control (MPC), and the penalty term about mode switching is introduced to improve the smoothness of the multi-mode hybrid power system. Compared with the dynamic programming-based energy management which belongs to the global optimum, the proposed control strategy achieves a near-optimal fuel economy of 3.767 L/100 km.