Optimizing Urban Traffic Efficiency via Virtual Eco-Driving Featured by a Single Automated Vehicle

In the face of growing concerns about environmental sustainability and urban congestion, the integration of eco-driving strategies has emerged as a pivotal solution in the field of the urban transportation sector. This study explores the potential benefits of a CAV functioning as a virtual eco-driving controller in an urban traffic scenario with a group of following human-driven vehicles. A computationally inexpensive and realistic powertrain model and energy management system of the Chrysler Pacifica PHEV are developed with the field experiment data and integrated into a forward-looking vehicle simulator to implement and validate an eco-driving speed planning and energy management strategy assuming longitudinal automation. The eco-driving algorithm determines the optimal vehicle speed profile and energy management strategy. Then, a microscopic traffic model that represents the driving behaviors of the human-driven vehicle queue is introduced to investigate the overall energetic impact of the eco-driving strategy on human-driven vehicles in urban routes. Two different scenarios are considered, one involving human-driven vehicles following a lead human-driven vehicle, and the other with the human-driven vehicles led by the CAV. The results reveal that CAV not only achieves high energy savings for the CAV itself but also improves the fuel economy of the following human-driven vehicles without featuring any cooperative driving. The findings highlight that even with a low penetration rate, CAVs could reduce the overall energy usage of a cohort of uncoordinated vehicles in urban traffic scenarios by as much as 7% - 27% when used as virtual eco-driving controllers.