Research on multi-lane energy-saving driving strategy of connected electric vehicle based on vehicle speed prediction

Abstract

In order to enhance the energy-saving potential of electric vehicles, a lane change decision method based on vehicle-to-everything (V2X) is designed to further improve the economics of intelligent connected electric vehicles. Firstly, the traversal test of electric vehicles is conducted at different speeds and accelerations to construct an energy consumption cloud model that reflects the mapping relationship between electric vehicle speed, acceleration and power. Next, the traffic flow information from V2X is used to train the long short-term memory neural network model optimized by particle swarm optimization (PSO-LSTM) for the prediction of the future speed of the vehicle in front of each lane. Then, according to the established energy consumption cloud model, the power performance corresponding to the predicted vehicle speed is obtained. Finally, a lane change decision method based on analytic hierarchy process (AHP) is established, and it is applied in four typical parallel scenarios to verify the robustness and effectiveness of the decision method. Simulation tests were conducted in a simulated urban traffic environment, involving both single-lane change scenarios and continuous lane change scenarios. The results show that this method can accurately and effectively select the lane with the best economic performance. Compared with the driving strategy of selecting a fixed lane, the energy consumption can be improved by up to 27.2% in the continuous lane change scenario.

Keywords

• Electric vehicle
• Energy-saving
• Lane selection
• V2X
• Neural networks