Energy and mobility impacts of connected autonomous vehicles with co-optimization of speed and powertrain on mixed vehicle platoons

Journal Article · Mon Jul 04 00:00:00 EDT 2022 · Transportation Research Part C: Emerging Technologies

DOI:https://doi.org/10.1016/j.trc.2022.103764· OSTI ID:1875329

Sun, Wenbo ^[1]; Wang, Shian ^[2]; Shao, Yunli ^[3]; Sun, Zongxuan ^[2]; Levin, Michael W. ^[2]

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1. Univ. of Minnesota, Minneapolis, MN (United States); Univ. of Hong Kong (China)
2. Univ. of Minnesota, Minneapolis, MN (United States)
3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Intersections are known to be traffic bottlenecks where a significant amount of energy consumption could be caused due to deceleration/acceleration in the presence of red signals. With an increased level of connectivity and automation of intelligent transportation systems, connected autonomous vehicles (CAVs) are expected to be able to proactively adjust their driving strategies subject to constraints imposed by the predicted future traffic. As a result, many potential benefits can be achieved, such as improved energy efficiency, enhanced traffic safety, among many others. Notably, the way CAVs are controlled affects the following legacy vehicles (LVs) due to complex traffic dynamics. Here, we are particularly interested in studying the energy and mobility impact of CAVs with an improved traffic prediction method on mixed vehicle platoons at various market penetration rates. Leveraging traffic prediction, CAVs are controlled with co-optimization of their speed and gear position. Specifically, a traffic prediction framework in a rolling horizon fashion is employed based upon a modified Payne–Whitham (PW) model capable of handling mixed traffic consisting of CAVs and LVs. The prediction error of the modified PW model is reduced by 53.62% compared to that of the standard PW model under test scenarios. According to the predicted traffic conditions, speed and gear position of CAVs are co-optimized with the primary goal of minimizing energy consumption when driving on a signalized arterial. The energy benefits achieved by CAVs and the impact of CAVs on LVs behind are studied comprehensively for mixed vehicle platoons. The lead LV follows a real-world speed profile collected on TH-55 in Minnesota. Numerical results show that energy benefits achieved by the vehicle platoon range from 2% to 16%, and a 1% to 5% reduction in travel time for LVs behind CAVs is also observed, at different penetration rates of CAVs in various traffic scenarios. Furthermore, it is observed that CAVs using the proposed eco-driving approach appear to have a positive impact on the LVs behind in terms of energy consumption, regardless of the driving styles of the LVs ahead.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1875329
• Journal Information: Transportation Research Part C: Emerging Technologies, Journal Name: Transportation Research Part C: Emerging Technologies Vol. 142; ISSN 0968-090X
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

33 ADVANCED PROPULSION SYSTEMS
Connected autonomous vehicles
Gear position optimization
Intelligent transportation systems
Mixed vehicle platoon
Speed optimization
Traffic prediction