A Method for Optimizing Vehicle Energy Consumption Using Speed Guidance in A Connected Vehicle Environment
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摘要: 传统的车速引导策略考虑交通信号的信号配时（signal phases and timing，SPAT）信息和到下游交叉口的距离，来对车辆进行速度建议和引导，以提高交叉口通行效率、减少能源消耗。但由于通信设备频率的限制，实时诱导效果欠佳。随着车载设备与路侧基础设施通信技术（vehicle to infrastructure，V2I）的发展，能实时、同步地获取交通流的多维信息，研究了1种符合真实驾驶场景的实时变速引导策略。以信号相位时间和道路通行限制条件为约束，构建三阶段变速诱导模型。提出将车辆通过连续路口的车速引导问题分解为车辆通过多个相邻路口的子问题进行求解。针对任意相邻2个交叉口，求解车辆到达下游交叉口的可通行时间区域，并将到达时间区域离散化，计算车辆到达时间区域内的每1个时间节点的能耗。将连续路口车速引导问题转换为速度轨迹寻优问题进行求解，以车辆能耗为权重，采用Dijkstra算法在所有可通行速度轨迹中寻找能耗最小的速度轨迹。利用交通仿真软件SUMO搭建仿真环境，并用Python对SUMO进行二次开发，以武汉市经济开发区东风大道的3个连续路口为研究对象进行仿真验证。实验结果表明：所提车速引导方法在过饱和，饱和、欠饱和流量下，与多级最优策略相比能耗分别减少0.68%，1.64%，3.97%，与匀速策略相比能耗分别减少0.7%，2.60%，9.80%。所提变速诱导方法在不同交通流量情况下均能诱导车辆节能地驶离交叉口，在欠饱和流量下效果最佳。
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关键词:
- 交通工程 /
- 智能交通 /
- 变速诱导 /
- Dijkstra算法 /
- 车联网
Abstract: Based on the signal phases and timing of traffic lights and the distance to the downstream intersection, traditional speed guidance strategies provide advisory speed, in order to improve the efficiency of road transportation and reduce vehicle energy consumption. However, it is difficult to recommend and guide the speed of vehicles in real time due to the limitation of traditional communication methods. With the development of vehicle to infrastructure (V2I) technology, it is possible to access multi-dimension information of traffic flow instantly and simultaneously, and a real-time variable speed guidance method, which can adapt to real-world driving scenarios, is proposed. A three-stage variable speed guidance model is developed by considering signal phase time and road capacity as the constraints. Moreover, the speed guidance problem of vehicle crossing multiple intersections is decomposed into sub-problems defined by each pair of consecutive ones. Between any two adjacent intersections, the feasible time range for vehicle arriving at the downstream junction is solved first, and then it is discretized to calculate the energy consumed at each time node. In the meantime, the speed guidance problem for vehicle traveling through continuous intersections is transformed into an optimal speed control problem. Taking energy consumption of vehicles as the weight, a Dijkstra algorithm is applied to compute the desired path that generates the most efficient speed profile with the lowest energy consumption among all feasible options. The simulation is conducted to verify the proposed method using the simulation of urban mobility (SUMO) simulator, and a case study is carried out for three consecutive intersections of Dongfeng Avenue in Wuhan Economic Development District. Experimental results show that, under scenarios of oversaturated, saturated, and undersaturated traffic flow, the proposed speed guidance method can reduce energy consumption by 0.68%, 1.64%, and 3.97%, when compared with the multi-level optimal method; and by 0.7%, 2.60%, and 9.80%, when compared with the constant speed method, respectively. The proposed variable speed guidance method can provide an energy-efficient trajectory for vehicles to pass through intersections under different traffic volumes and performs best in an undersaturated traffic flow condition. -
图 2 车辆通过连续路口示意图
Figure 2. Diagram of vehicles passing through continuous intersections
图 3 不同工况的最大建议车速逻辑图
Figure 3. Logic diagram of maximum recommended speed under different working conditions
图 4 连续路口车速引导示意图
Figure 4. Diagram of vehicles passing through consecutive intersections
图 5 车辆通过连续路口可行路径示意图
Figure 5. Diagram of feasible paths for vehicles passing through consecutive intersections
图 6 车辆通过连续路口权重示意图
Figure 6. Schematic diagram of weights for vehicles passing through consecutive intersections
表 1 VT-Micro微观油耗模型拟合系数
Table 1. Coefficient of VT-Micro fuel consumption model
拟合系数 $v^{0}$ $v^{1}$ $v^{2}$ $v^{3}$ $a \geqslant 0$ $a^{1}$ $2.2946 \times 10^{-1}$ $6.8 \times 10^{-3}$ $-4.402 \times 10^{-5}$ $4.80 \times 10^{-8}$ $a^{2}$ $-5.61 \times 10^{-3}$ $-7.7221 \times 10^{-4}$ $7.90 \times 10^{-7}$ $3.27 \times 10^{-8}$ $a^{3}$ $9.77 \times 10^{-5}$ $8.38 \times 10^{-6}$ $8.17 \times 10^{-7}$ $-7.79 \times 10^{-9}$ $a<0$ $a^{0}$ -7.73452 $2.804 \times 10^{-2}$ $-2.1988 \times 10^{-4}$ $1.08 \times 10^{-6}$ $a^{1}$ $-1.799 \times 10^{-2}$ $7.72 \times 10^{-3}$ $-5.219 \times 10^{-5}$ $2.47 \times 10^{-7}$ $a^{2}$ $-4.27 \times 10^{-3}$ $7.72 \times 10^{-3}$ $-5.219 \times 10^{-4}$ $4.87 \times 10^{-8}$ $a^{3}$ $1.8829 \times 10^{-4}$ $-3.387 \times 10^{-5}$ $2.77 \times 10^{-7}$ $3.79 \times 10^{-10}$ 
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表 2 符号说明
Table 2. Symbol description
符号 含义 单位 g1k 第1个路口的第k个周期的绿灯开始时间 s r1k 第1个路口的第k个周期的红灯开始时间 s g1k+1 第1个路口的第k + 1个周期的绿灯开始时间 s s 路段长度 m veco 经济车速 m/s vlimit 道路限速 km/h vmax 最大建议速度 m/s ttime(v) 最大建议速度为v的行程时间 s MOEe(v) 最大建议速度为v的油耗 mL
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表 3 道路交叉口参数
Table 3. Road intersection parameters
路口编号 绿灯时间/s 红灯时间/s 交叉口长度/m 过饱和流量/（ pcu/h） 饱和流量/（ pcu/h） 欠饱和流量/（ pcu/h） 1 10 10 600 2 651 2 125 1 500 2 20 20 700 2 651 2 125 1 500 3 25 25 600 2 651 2 125 1 500
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表 4 同速度引导策略指标对比
Table 4. Comparison of indicators among different speed guidance strategies
驾驶策略 过饱和 饱和 欠饱和 能源消耗/mL 行程时间/s 能源消耗/mL 行程时间/s 能源消耗/mL 行程时间/s 1匀速运动 153.33 100.00 153.33 100.00 153.33 100.00 2多级最优 153.15 130.59 151.83 120.14 145.35 106.52 3变速策略 152.11 121.00 149.34 114.00 139.57 105.00
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[1] 李冰, 成卫, 晏永廷, 等. 基于MP与MPC相结合的分布式交通信号控制研究[J]. 交通运输系统工程与信息, 2019, 19 (5): 86-93. 网联环境下基于速度引导的车辆能耗优化方法——施丘岭邱志军何书贤145交通信息与安全2023年3期第41卷总244期 https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201905012.htmLI B, CHENG W, YAN Y T et al. Research on distributed traffic signal control based on combination of MP and MPC[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(5): 86-93. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201905012.htm [2] 王鹏, 李艳雯, 杨迪, 等. 基于层级控制的宏观基本图交通信号控制模型[J]. 计算机应用, 2021, 41(2): 571-576. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY202102040.htmWANG P, LI Y X, YANG D, et al. Macro basic graph traffic signal control model based on hierarchical control[J]. Journal of Computer Applications, 2021, 41(2): 571-576. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY202102040.htm [3] 王润民, 张心睿, 赵祥模, 等. 混行环境下网联信号交叉口车路协同控制方法[J]. 交通运输工程学报, 2022, 22(3): 139-151. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202203011.htmWANG R M, ZHANG X R, ZHAO X M, et al. Vehicle-infrastructure control method under mixed connected and human-driving traffic environment[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 139-151. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202203011.htm [4] 俞灏, 刘攀, 柏璐, 等. 考虑交通事件影响的动态交通信号控制策略[J]. 交通运输工程学报, 2019, 19(6): 182-190. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201906019.htmYU H, LIU P, BO L, et al. Dynamic traffic signal control strategy considering the impact of traffic events[J]. Journal of Traffic and Transportation Engineering, 2019, 19(6): 182-190. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201906019.htm [5] WANG Z, WANG S, LIAN H. A route-planning method for long-distance commuter express bus service based on OD estimation from mobile phone location data: the case of the Changping Corridor in Beijing[J]. Public Transport, 2021, 13 (1): 101-125. doi: 10.1007/s12469-020-00254-w [6] ASADI B, VAHIDI A. Predictive cruise control: Utilizing upcoming traffic signal information for improving fuel economy and reducing trip time[J]. IEEE Transactions on Control Systems Technology, 2010, 19(3): 707-714. [7] DE N G, DE W C, MOULIN P, et al. Eco-driving in urban traffic networks using traffic signals information[J]. International Journal of Robust and Nonlinear Control, 2016, 26(6): 1307-1324. doi: 10.1002/rnc.3469 [8] 胡云峰, 刘迪, 赵靖华, 等. 智能网联环境下车辆能耗与排放优化控制的研究现状与展望[J]. 中国公路学报, 2022, 35 (3): 1-14. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202203001.htmHU Y F, LIU D, ZHAO J H, et al. Research status and prospect of vehicle energy consumption and emission optimization control in intelligent network environment[J]. China Highway Journal, 2022, 35(3): 1-14. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202203001.htm [9] SUZUKI H, MARUMO Y. Safety evaluation of green light optimal speed advisory(GLOSA)system in real-world signalized intersection[J]. Journal of Robotics and Mechatronics, 2020, 32(3): 598-604. [10] NJOBELO G, SANDO T, SAJJADI S, et al. Enhancing the green light optimized speed advisory system to incorporate queue formation[R]. Washington D. C. : Transportation Research Board, 2018. [11] BUTAKOV V A, IOANNOU P. Personalized driver assistance for signalized intersections using V2I communication[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(7): 1910-1919. [12] KATSAROS K, KERNCHEN R, DIANATI M, et al. Application of vehicular communications for improving the efficiency of traffic in urban areas[J]. Wireless Communications and Mobile Computing, 2011, 11(12): 1657-1667. [13] SIMCHON L, RABINOVICI R. Real-time implementation of green light optimal speed advisory for electric vehicles[J]. Vehicles, 2020, 2(1): 35-54. [14] ASADI B, VAHIDI A. Predictive cruise control: Utilizing upcoming traffic signal information for improving fuel economy and reducing trip time[J]. IEEE Transactions on Control Systems Technology, 2010, 19(3): 707-714. [15] MANDAVA S, BORIBOONSOMSIN K, BARTH M. Arterial velocity planning based on traffic signal information under light traffic conditions[C]. 12th International IEEE Conference, Missouri, USA: IEEE, 2009. [16] ZHANG R, YAO E. Eco-driving at signalized intersections for electric vehicles[J]. IET Intelligent Transport Systems, 2015, 9(5): 488-497. [17] XIANG X, ZHOU K, ZHANG W B, et al. A closed-loop speed advisory model with driver's behavior adaptability for eco-driving[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(5): 3313-3324. [18] WU X, HE X, YU G, et al. Energy-optimal speed control for electric vehicles on signalized arterials[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(5): 2786-2796. [19] 孟竹, 邱志军. 节能导向的信号交叉口生态驾驶策略研究[J]. 交通信息与安全, 2018, 36(2): 76-84. doi: 10.3963/j.issn.1674-4861.2018.02.011MENG Z, QIU Z J. Research on eco-driving strategy of energy saving oriented signalized intersection[J]. Journal of Transport Information and Safety, 2018, 36(2): 76-84. (in Chinese) doi: 10.3963/j.issn.1674-4861.2018.02.011 [20] ZHANG Z, ZOU Y, ZHANG X, et al. Green light optimal speed advisory system designed for electric vehicles considering queuing effect and driver's speed tracking error[J]. IEEE Access, 2020, 8(8): 208796-208808. [21] GUAN T, FREY C W. Predictive fuel efficiency optimization using traffic light timings and fuel consumption model[C]. 16th International IEEE Conference, Hague, Netherlands: IEEE, 2013. [22] CHEN H, RAKHA H A. Developing and field testing a green light optimal speed advisory system for buses[J]. Energies, 2022, 15(4): 1491-1505. [23] ALSABAAN M, NAIK K, KHALIFA T, et al. Applying vehicular networks for reduced vehicle fuel consumption and CO2 emissions[M]. Rijeka: INTECH OpenAccess Publisher, 2012. [24] ZHAO X, WU X, XIN Q, et al. Dynamic eco-driving on signalized arterial corridors during the green phase for the connected vehicles[J]. Journal of Advanced Transportation, 2020, 57(3): 1-11. -
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