An Economical Velocity Planning Algorithm for Intelligent Connected Electric Vehicles Based on Real-Time Traffic Information

The energy efficiency of intelligent networked connected electric vehicle (EV) is directly related to its velocity. Aiming at the influence of real-time traffic flow information on road speed interval, a two-layer speed planning method is proposed. The upper layer extracts the road speed interval according to the traffic flow information, and based on cellular automata and confidence interval theory, traffic information rules are introduced, and a road speed interval extraction method considering traffic density information is established. The lower layer is used to obtain energy-optimal cruising velocity profile. Taking the road speed interval as the variable boundary constraint, a dynamic programming algorithm that changes the state quantity boundary in real time is designed, which realizes the efficient acquisition of the energy-optimized velocity trajectory. To verify the effectiveness of proposed approach, the simulation model is formulated based on using collected real traffic information. The simulation results demonstrate that, compared with the conventional constant speed cruising strategy and dynamic programming (DP) strategy based on road speed interval, the strategy proposed in this study not only improves energy efficiency and reduces computing time significantly, but also can predict the traffic conditions ahead to avoid large fluctuations in velocity. Besides, the biggest significance of this study is the designed economic velocity planning algorithm based on real-time traffic density information improves the adaptability of intelligent networked connected EV control strategy to actual traffic conditions, and extends the optimization dimension of eco-driving.