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山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (6): 23-29.doi: 10.6040/j.issn.1672-3961.0.2022.068

• 交通工程—智慧交通专题 • 上一篇    下一篇

利用通行能力余量的智能网联车队生态驾驶模型

于少伟1,4(),秦瑞伶2,关京京2,吉灿1,封硕3,姜锐2,*(),刘英宁1   

  1. 1. 长安大学运输工程学院, 陕西 西安 710064
    2. 长安大学信息工程学院, 陕西 西安 710064
    3. 长安大学工程机械学院, 陕西 西安 710064
    4. 生态安全屏障区交通网设施管控及循环修复技术交通运输行业重点实验室(长安大学), 陕西 西安 710064
  • 收稿日期:2022-02-19 出版日期:2022-12-20 发布日期:2022-12-23
  • 通讯作者: 姜锐 E-mail:swyu2020@126.com;jiangrui@bjtu.edu.cn
  • 作者简介:于少伟，1981年4月生，山东威海人，工学博士，教授，博士生导师。中国公路学会自动驾驶工作委员会委员，管理科学与工程学会理事，第六届中国交通运输协会青年科技工作者工作委员会委员。研究方向为：智慧公交运行优化及控制、车路协同控制技术。在TRB、TRR等期刊上共发表学术论文50余篇。主持国家自然科学基金面上项目等10余项。授权国家发明专利6项。
    于少伟(1981—)，男，山东威海人，教授，工学博士(后)，博导，主要研究方向为车路协同控制技术、智慧公交运行优化及控制。E-mail: swyu2020@126.com
  • 基金资助:
    国家自然科学基金项目(71871028);中央高校基本科研业务费专项资金项目(300102240104);中央高校基本科研业务费专项资金项目(300102342501);光电技术与智能控制教育部重点实验室开放课题(KFKT2020-04)

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

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摘要：

针对非饱和城乡交通干道上存在通行能力余量和高燃油消耗的问题, 提出一种利用通行能力余量的智能网联车队生态驾驶模型, 该模型兼顾燃油经济性和通行能力两个目标, 通过优化求解获取最优速度曲线, 引导一系列小型车队平滑地通过非饱和城乡交通干道。提出近似的速度优化模型并采用遗传算法对其求解。定义3种控制方案对模型进行测试, 仿真结果表明: 与方案1相比, 方案2燃油消耗量减少49.4%, 通行能力增加200%, 绿灯剩余时间减少14.7%;方案3燃油消耗量减少59.5%, 通行能力增加200%, 绿灯剩余时间减少23.5%。与方案2相比, 方案3可以在不影响通行能力的前提下, 通过绿灯剩余时间缩短10.3%和平均速度降低5.2%, 燃油消耗量可以减少20%。结果表明, 当信号交叉口存在通行能力余量时, 可以通过调整车辆的行驶速度曲线以充分利用通行能力余量, 明显改善燃油经济性。

关键词: 智能交通, 生态驾驶, 智能网联车队, 通行能力余量, 预测优化策略

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

中图分类号: 

  • U495

图1

研究场景"

图2

测试路段示意图"

表1

仿真参数设置"

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

表2

不同控制方案下的燃油消耗量"

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

表3

不同控制方案下的通行能力"

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

表4

不同控制方案的平均速度"

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

图3

车队行驶时间-油耗-平均速度关系"

表5

绿灯剩余时长"

控制方案 t2 t3
1 0 34
2 0 29
3 0 26
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