Analysis of the Impact of Speed Trajectory Optimization on Energy Consumption During Highway Merging

Abstract

Traffic congestion often occurs at intersections or ramp merges, where energy-inefficient driving occurs because of the need to frequently adjust the vehicle speed. The emergence of connected and automated vehicle (CAV) technology has created an opportunity to reduce energy consumption by optimizing the speed trajectory and merging sequences of vehicles. In this study, we address the problem of integrating the vehicle powertrain characteristics and speed optimization of merging control. A battery electric vehicle is the target vehicle, and the energy-optimal speed trajectory was generated based on dynamic programming in a merging scenario. The evaluation results show that the proposed method achieves 30.1 % energy savings compared with traditional adaptive cruise control under specific merging conditions. Powertrain loss is reduced by 52.4 % and is the largest factor affecting overall energy consumption. To expend under different traffic demands, energy-optimal speed trajectories were obtained for different merging times. The analysis indicates that the powertrain loss significantly affects the changing trend of overall energy consumption at different merging times. The demand for speed variation is critical for energy-efficient merging control because it changes dramatically with the merging time and generates powertrain loss. The proposed method demonstrates significant energy-saving potential of individual vehicle in merging control strategies and can serve as a theoretical reference for optimizing the total energy consumption in multi-CAV merging scenarios.

Keywords

• Connected and automated vehicle
• energy consumption
• on-ramp merging
• optimal control
• speed trajectory optimization