An Energy-Efficient Dynamic Route Optimization Algorithm for Connected and Automated Vehicles Using Velocity-Space-Time Networks

This paper introduces three types of status information based on the current environmental perception and the information computing power of connected and automated vehicles (CAVs) and proposes an energy-efficient dynamic route planning algorithm without starting and stopping for the CAVs. The information types include real-time road traffic flow, traffic light and vehicle driving status (driving velocity and direction). The algorithm aims to minimize both time and energy consumption for the route. For this study, the feasibility of this energy-efficient route planning algorithm was first theoretically deduced. After introducing traffic light and driving energy consumption elements, a velocity-space-time three-dimensional network (VSTN) model was mathematically formulated to describe a dynamic route in addition to the design of the shortest route searching algorithm in VSTN. Two experimental road networks were then constructed to test and compare the energy efficiency. The first test scenario is a line road with two road conditions and various signal cycles and initial phases. The experiment shows that the cumulative energy consumption of the energy-efficient route under various scenarios is no greater than the normal route, ranging from 5.18% to 16.4%. The second test scenario involves a network located in the Jing’an district of Shanghai and is intended to verify the route searching function and energy efficiency. Compared to the traditional driving method, the new route reduces energy consumption by 10.36%. This study demonstrates that the energy efficiency does not correlate with the signal cycle or initial phase, and it appears to be random. The new algorithm proposed in this paper can be applied to dynamic route planning by CAVs and the goal of saving energy can be achieved in various road networks.