A Generic Approach to Eco-Driving of Connected Automated Vehicles in Mixed Urban Traffic and Heterogeneous Power Conditions

被引:15
作者
Hu, Yonghui [1 ]
Yang, Peng [1 ]
Zhao, Mingming [2 ,3 ]
Li, Daofei [4 ]
Zhang, Lihui [1 ]
Hu, Simon [5 ]
Hua, Wei [6 ]
Ji, Wei [6 ]
Wang, Yibing [1 ]
Guo, Jingqiu [7 ]
机构
[1] Zhejiang Univ, Inst Intelligent Transportat Syst, Coll Civil Engn & Architecture, Hangzhou 310058, Peoples R China
[2] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China
[3] Xidian Univ, Hangzhou Inst Technol, Hangzhou 311200, Peoples R China
[4] Zhejiang Univ, Inst Power Machinery & Vehicular Engn, Hangzhou 310027, Peoples R China
[5] Zhejiang Univ, ZJU UIUC Inst, Hangzhou 314400, Peoples R China
[6] Res Ctr Smart Transportat, Zhejiang Lab, Hangzhou 311121, Peoples R China
[7] Tongji Univ, Minist Educ, Key Lab Rd & Traff Engn, Shanghai 201804, Peoples R China
基金
中国国家自然科学基金;
关键词
Connected automated vehicles; eco-driving; optimal control; rolling-horizon; mixed traffic and heterogeneous power conditions; vehicle queues at intersections; ROLLING HORIZON CONTROL; FUEL CONSUMPTION; SIGNALIZED INTERSECTION; ELECTRIC VEHICLES; MODEL; FRAMEWORK; ARTERIAL; PLATOON;
D O I
10.1109/TITS.2023.3286441
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The connected automated vehicles (CAVs) are envisioned to be implemented most likely on electric vehicles, while traditional fuel-powered manually-driven vehicles (MVs) would probably still dominate the automobile market in the next decade. In this context, this paper addresses urban eco-driving of CAVs in mixed traffic and heterogeneous power conditions. The paper aims to develop a practical and deployable eco-driving strategy for CAVs in mixed traffic flow of CAVs and MVs under realistic and complex traffic conditions. Several typical eco-driving scenarios were studied in detail. In a nutshell, the eco-driving strategy for each CAV was determined by solving a typical two-point boundary value problem with minimum electric energy consumption in urban traffic conditions with small market penetration rates (MPRs) of CAVs. A rolling-horizon scheme was applied to implement the eco-driving strategy to handle uncertain/unpredictable disturbances of preceding MVs and the interference of junction queues to the eco-driving maneuvers of CAVs. The paper also studied how eco-driving for electrified CAVs would affect MVs' fuel consumptions. Simulation studies were carried out on urban arterial roads of multiple signalized intersections in various scenarios of demand and MPR to verify the energy savings effect of the proposed eco-driving strategy. The results showed that via eco-driving electrified CAVs each had a potential of reducing energy consumption by 40%-61%, meanwhile leading to 5%-34% fuel savings on average for each following MV. Further issues concerning the energy saving mechanism of electrified CAVs, impacts of MVs cut-in from adjacent lanes, and passenger comfort were also examined.
引用
收藏
页码:11963 / 11980
页数:18
相关论文
共 66 条
  • [1] GlidePath: Eco-Friendly Automated Approach and Departure at Signalized Intersections
    Altan, Osman D.
    Wu, Guoyuan
    Barth, Matthew J.
    Boriboonsomsin, Kanok
    Stark, John A.
    [J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2017, 2 (04): : 266 - 277
  • [2] autocaat, 2018, CTR ADV AUT TECHN CO
  • [3] Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections
    Bai, Zhengwei
    Hao, Peng
    Shangguan, Wei
    Cai, Baigen
    Barth, Matthew J.
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (09) : 15850 - 15863
  • [4] Eco-driving: An overlooked climate change initiative
    Barkenbus, Jack N.
    [J]. ENERGY POLICY, 2010, 38 (02) : 762 - 769
  • [5] Becerra V. M., 2010, 2010 IEEE International Symposium on Computer-Aided Control System Design (CACSD) part of the IEEE Multi-Conference on Systems & Control (MSC 2010), P1391, DOI 10.1109/CACSD.2010.5612676
  • [6] Biggs D., 1986, Traffic engineering control, V27, P320
  • [7] Mixed platoon control of automated and human-driven vehicles at a signalized intersection: Dynamical analysis and optimal control
    Chen, Chaoyi
    Wang, Jiawei
    Xu, Qing
    Wang, Jianqiang
    Li, Keqiang
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2021, 127
  • [8] CHEN H, 2019, IEEE T INTELL TRANSP, V20, P2858, DOI DOI 10.1109/TITS.2018.2868518
  • [9] Framework for Connected and Automated Bus Rapid Transit with Sectionalized Speed Guidance based on deep reinforcement learning: Field test in Sejong City
    Choi, Seongjin
    Lee, Donghoun
    Kim, Sari
    Tak, Sehyun
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2023, 148
  • [10] Eco-Driving Control Architecture for Platoons of Uncertain Heterogeneous Nonlinear Connected Autonomous Electric Vehicles
    Coppola, Angelo
    Lui, Dario Giuseppe
    Petrillo, Alberto
    Santini, Stefania
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (12) : 24220 - 24234