Ecological Driving System for Connected/Automated Vehicles Using a Two-Stage Control Hierarchy

To improve a vehicle's fuel efficiency when operating on roadways, this study develops an ecological driving system under the connected and automated vehicle (CAV) environment. The system includes three critical functions, including traffic state prediction, eco-driving speed control, and powertrain control implementation. According to the real-time traffic information obtained from vehicle-to-infrastructure and vehicle-to-vehicle communications, the embedded traffic state prediction model will estimate and predict the average speeds and densities of freeway subsections. With an objective of minimizing the fuel consumption, the eco-driving speed control function follows a two-stage hierarchical framework. The first stage, which is executed at the global level, aims to optimize the travel speed profile of the CAV over a certain time period. The second stage, local speed adoption, is designed to dynamically adjust the CAV's speed and make lane-changing decisions based on the local driving condition. The resulting control parameters will then be forwarded to the powertrain control system for implementations. To evaluate the proposed system, this study performs comprehensive numerical tests by using simulation models. This results confirm the effectiveness of the proposed system in reducing fuel consumption. Further comparisons with different models highlights the need to consider traffic state information in the first-stage optimization and lane-changing decision module in the local adoption function.