As one of the most important parts of intelligent transportation system(ITS), vehicles are developing in the direction of intellectualization and electrification. At present, the research on connected and automated vehicles focuses on its safety and trafficability, and lacks the exploration of its economy. However, energy conservation is also one of the important goals of ITS. In this study, an energy management method of connected and automated hybrid electric vehicles is proposed, which takes into account the trafficability and safety as well as the economy. Based on Dijkstra algorithm, the length of the road is weighted according to the real-time traffic data to ensure the trafficability of the planning path.In addition, according to the obtained path parameters and vehicle dynamics model constraints, the safety velocity of connected and automated hybrid electric vehicles is planned. Besides, based on the planning velocity, the method of reinforcement learning is used to control the working mode and energy output of the engine and motors to achieve energy saving. The results show that using the proposed energy management method in this study, the connected and automated hybrid electric vehicles not only can improve the traffic efficiency, but also achieve the effect of energy saving.

CICTP