Journal of Shandong University(Engineering Science) ›› 2022, Vol. 52 ›› Issue (6): 23-29.doi: 10.6040/j.issn.1672-3961.0.2022.068

• Transportation Engineering—Special Issue for Intelligent Transportation • Previous Articles     Next Articles

Eco-driving model for connected and automated vehicle platoons using the traffic capacity remainder

Shaowei YU1,4(),Ruiling QIN2,Jingjing GUAN2,Can JI1,Shuo FENG3,Rui JIANG2,*(),Yingning LIU1   

  1. 1. School of Transportation Engineering, Chang′an University, Xi′an 710064, Shaanxi, China
    2. School of Information Engineering, Chang′an University, Xi′an 710064, Shaanxi, China
    3. School of Engineering Machinery, Chang′an University, Xi′an 710064, Shaanxi, China
    4. Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area, Chang′an University, Xi′an 710064, Shaanxi, China
  • Received:2022-02-19 Online:2022-12-20 Published:2022-12-23
  • Contact: Rui JIANG E-mail:swyu2020@126.com;jiangrui@bjtu.edu.cn

RichHTML

35

PDF (PC)

305

Abstract:

Aiming at the problems of capacity remainder and high fuel consumption on unsaturated urban and rural traffic roads, this paper proposed an eco-driving model for connected and automated platoons using the capacity remainder, which considered the two goals of fuel economy and traffic capacity, and obtained the optimal speed profiles by solving optimization, to guide a series of small-sized platoons smoothly through the unsaturated urban and rural traffic corridors. In order to improve the computational efficiency of the proposed eco-driving model, this paper proposed an approximate speed optimization model and used genetic algorithm. In order to verify the performance of the proposed model, three control strategies were defined to test the model. The simulation results showed that compared with Strategy 1, Strategy 2 could reduce fuel consumption by 49.4%, increased traffic capacity by 200%, and reduced remaining green time by 14.7%; Strategy 3 could reduce 59.5% of fuel consumption, increased traffic capacity by 200%, and reduced remaining green time by 23.5%. Compared with Strategy 2, Strategy 3 could reduce fuel consumption by 20% without affecting traffic capacity by reducing remaining green time by 10.3% and the average speed by 5.2%. The results showed that when there was traffic capacity remainder at the signalized intersection, the fuel economy could be significantly improved by adjusting the vehicle speed curve to make full use of the traffic capacity remainder.

Key words: intelligent transportation, eco-driving, connected and automated vehicle platoons, the traffic capacity remainder, prediction-based optimization strategy

CLC Number: 

  • U495

Fig.1

Study scenario"

Fig.2

Schematic diagram of the test road"

Table 1

Simulation parameter setting"

交叉口名称 绿灯时长/s 红灯时长/s 停车线距车辆起点距离/m
第一个交叉口 60 80 0
第二个交叉口 49 5 832
第三个交叉口 63 29 1151

Table 2

Fuel consumption under different strategies  mL"

控制方案 F1 F2 F
1 1 201.457 5 1 060.594 7 2 263.052 2
2 694.386 0 450.622 8 1 145.008 8
3 694.386 0 221.079 5 915.465 5

Table 3

Capacity under different strategies  辆"

控制方案 V1 V2 V1
1 12 4 4
2 12 12 12
3 12 12 12

Table 4

Average speed under different strategies  km/h"

控制方案 ${\bar v}$1 ${\bar v}$2 ${\bar v}$
1 53.095 0 50.745 0 51.920 0
2 62.121 4 55.879 6 59.000 5
3 62.121 4 49.693 1 55.907 3

Fig.3

Travel time-fuel consumption-average speed"

Table 5

Remaining green times  s"

控制方案 t2 t3
1 0 34
2 0 29
3 0 26
1 ALVARO G C , ANDRES M , CRISTINA V , et al. Modeling different penetration rates of eco-driving in urban areas: impacts on traffic flow and emissions[J]. International Journal of Sustainable Transportation, 2016, 11 (4): 282- 294.
2 MARIA A C , MICHAEL S , PAUL S , et al. Off-design optimisation of organic Rankine cycle (ORC) engines with piston expanders for medium-scale combined heat and power applications[J]. Applied Energy, 2019, 238, 1211- 1236.
doi: 10.1016/j.apenergy.2018.12.086
3 ZHANG Lei , HU Xiaosong , WANG Zhenpo , et al. A review of supercapacitor modeling, estimation, and applications: a control/management perspective[J]. Renewable and Sustainable Energy Reviews, 2018, 81 (2): 1868- 1878.
4 HUANG Yuhan , GUANG Hong , HUANG Ronghua . Investigation to charge cooling effect and combustion characteristics of ethanol direct injection in a gasoline port injection engine[J]. Applied Energy, 2015, 160, 244- 254.
doi: 10.1016/j.apenergy.2015.09.059
5 ZHEN Xudong , WANG Yang . An overview of methanol as an internal combustion engine fuel[J]. Renewable and Sustainable Energy Reviews, 2015, 52, 477- 493.
doi: 10.1016/j.rser.2015.07.083
6 GUO Qiangqiang , OHAY A , LIU Zhijun , et al. Hybrid deep reinforcement learning based eco-driving for low-level connected and automated vehicles along signalized corridors[J]. Transportation Research Part C, 2021, 124, 102980.
doi: 10.1016/j.trc.2021.102980
7 MARIUS W , LUCAS K , MARKUS E , et al. Automated eco-driving in urban scenarios using deep reinforcement learning[J]. Transportation Research Part C, 2021, 126, 102967.
doi: 10.1016/j.trc.2021.102967
8 ZHAO Shuaidong , ZHANG Kuilin . Online predictive connected and automated eco-driving on signalized arterials considering traffic control devices and road geometry constraints under uncertain traffic conditions[J]. Transpor-tation Research Part B, 2021, 145, 80- 117.
doi: 10.1016/j.trb.2020.12.009
9 MICHAEL S , BRANDON S . Eco-driving: strategic, tactical, and operational decisions of the driver that influence vehicle fuel economy[J]. Transport Policy, 2012, 22, 96- 99.
doi: 10.1016/j.tranpol.2012.05.010
10 HUANG Yuhan , ELVIN C , JOHN L Z , et al. Eco-driving technology for sustainable road transport a: review[J]. Renewable and Sustainable Energy Reviews, 2018, 93, 596- 609.
doi: 10.1016/j.rser.2018.05.030
11 廖若桦. 车路协同环境下信号交叉口车队生态驾驶研究[D]. 北京: 北京交通大学, 2018.
LIAO Ruoye. Research on eco-driving of signalized intersection fleet in vehicle-road cooperative environment[D]. Beijing: Beijing Jiaotong University, 2018.
12 LI Yongfu , TANG Chuancong , SRINIVAS P , et al. Nonlinear consensus based connected vehicle platoon control incorporating car-following interactions and heterogeneous time delays[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20 (6): 2209- 2219.
doi: 10.1109/TITS.2018.2865546
13 NIU Dening , SUN Jian . Eco-driving versus green wave speed guidance for signalized highway traffic: a multi-vehicle driving simulator study[J]. Procedia-Social and Behavioral Sciences, 2013, 96, 1079- 1090.
doi: 10.1016/j.sbspro.2013.08.124
14 STEBBINS S , MARK H , KIM J , et al. Characterising green light optimal speed advisory trajectories for platoon-based optimization[J]. Transportation Research Part C, 2017, 82, 43- 62.
doi: 10.1016/j.trc.2017.06.014
15 LIU Meiqi , WANG Meng , SERGE H . Optimal platoon trajectory planning approach at arterials[J]. Transpor-tation Research Record, 2019, 2673 (9): 214- 226.
doi: 10.1177/0361198119847474
16 LU Yingrong , XU Xiaotong , DING Chuan , et al. A speed control method at successive signalized intersections under connected vehicles environment[J]. IEEE Intelligent Transportation Systems Magazine, 2019, 11 (3): 117- 128.
doi: 10.1109/MITS.2019.2919638
17 HE Xiaozheng , LIU Henry X , LIU Xiaobo . Optimal vehicle speed trajectory on a signalized arterial with consideration of queue[J]. Transportation Research Part C, 2015, 61, 106- 120.
doi: 10.1016/j.trc.2015.11.001
18 MA Fangwu , YANG Yu , WANG Jiawei , et al. Eco-driving-based cooperative adaptive cruise control of connected vehicles platoon at signalized intersections[J]. Transportation Research Part D, 2021, 92, 102746.
doi: 10.1016/j.trd.2021.102746
19 WU Xia , ZHAO Xiangmo , XIN Qi , et al. Dynamic cooperative speed optimization at signalized arterials with various platoons[J]. Transportation Research Record, 2019, 2673 (5): 528- 537.
doi: 10.1177/0361198119839964
20 MA Xiaolong , MA Dongfang , YUAN Jinyu , et al. Bandwidth optimisation and parameter analysis at two adjacent intersections based on set operations[J]. IET Intelligent Transport Systems, 2020, 14 (7): 684- 692.
doi: 10.1049/iet-its.2019.0561
21 LI Ming , WU Xinkai , HE Xiaopeng , et al. An eco-driving system for electric vehicles with signal control under V2X environment[J]. Transportation Research Part C, 2018, 93, 335- 350.
doi: 10.1016/j.trc.2018.06.002
22 孙杨. 组合近似模型预测设计方法及应用研究[D]. 大连: 大连理工大学, 2016.
SUN Yang. Research on prediction design method and application of combined approximate model[D]. Dalian: Dalian University of Technology, 2016.
23 SANGJUN P , HESHAM A R , AHN K , et al. Virginia tech comprehensive power-based fuel consumption model (VT-CPFM): model validation and calibration considerations[J]. Transportation Science and Technology, 2013, 2 (4): 317- 336.
doi: 10.1260/2046-0430.2.4.317
[1] Haigen MIN,Yukun FANG,Xia WU,Wuqi WANG. Fault diagnosis of vehicle-to-vehicle communication in networked traffic environment [J]. Journal of Shandong University(Engineering Science), 2021, 51(6): 84-92.
[2] MA Wenjing, WU Dongya, TANG Kai, WANG Dongzhu. A design method for object resolution system applied in transport field [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2015, 45(4): 10-18.
[3] WANG Qing-yan, ZHU Wen-xing*, SHI Xiao-wei, LI Ning. Modeling for traffic overflow control and its simulation on urban arterial roads [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2013, 43(4): 57-61.
[4] LIN Jie,YANG Li-cai,WU Xiao-qing,YE Yang . Artificial immune optimization method for solving the K shortest paths search in dynamic route guidance system [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(2): 103-108 .
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] WANG Su-yu,<\sup>,AI Xing<\sup>,ZHAO Jun<\sup>,LI Zuo-li<\sup>,LIU Zeng-wen<\sup> . Milling force prediction model for highspeed end milling 3Cr2Mo steel[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(1): 1 -5 .
[2] LI Kan . Empolder and implement of the embedded weld control system[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(4): 37 -41 .
[3] KONG Xiang-zhen,LIU Yan-jun,WANG Yong,ZHAO Xiu-hua . Compensation and simulation for the deadband of the pneumatic proportional valve[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(1): 99 -102 .
[4] LAI Xiang . The global domain of attraction for a kind of MKdV equations[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(1): 87 -92 .
[5] YU Jia yuan1, TIAN Jin ting1, ZHU Qiang zhong2. Computational intelligence and its application in psychology[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 1 -5 .
[6] CHEN Rui, LI Hongwei, TIAN Jing. The relationship between the number of magnetic poles and the bearing capacity of radial magnetic bearing[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(2): 81 -85 .
[7] WANG Bo,WANG Ning-sheng . Automatic generation and combinatory optimization of disassembly sequence for mechanical-electric assembly[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 52 -57 .
[8] LI Ke,LIU Chang-chun,LI Tong-lei . Medical registration approach using improved maximization of mutual information[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 107 -110 .
[9] JI Tao,GAO Xu/sup>,SUN Tong-jing,XUE Yong-duan/sup>,XU Bing-yin/sup> . Characteristic analysis of fault generated traveling waves in 10 Kv automatic blocking and continuous power transmission lines[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 111 -116 .
[10] . [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 27 -32 .
Copyright 2018 © Editorial office of Journal of Shandong University (Engineering Science)
Supported by Beijing Magtech Co.Ltd