信号交叉口智能电动汽车转弯车速引导策略  

Intelligent Electric Vehicle Turning Speed Guidance Strategy at Signalized Intersections

作　　者：张乐 刘显贵 ZHANG Le;LIU Xiangui(School of Mechanical&Automotive Engineering,Xiamen University of Technology,Xiamen,361024,China)

机构地区：[1]厦门理工学院机械与汽车工程学院,福建厦门361024

出　　处：《厦门理工学院学报》2023年第3期7-16,共10页Journal of Xiamen University of Technology

摘　　要：为实现智能电动汽车在信号交叉口节能、高效地转弯通行,基于目标引导车速相关联的电动车能耗模型,提出信号交叉口智能电动汽车转弯车速引导策略。针对需要加/减速引导通行的场景,借助车联网系统获取前方车辆和对向来车的运动信息及信号灯状态信息,结合自车运动信息综合分析确定出可使车辆不停车通过交叉口的速度引导范围,进而通过建立综合考虑车辆能耗和通行时间的多目标优化函数,运用NSGA-II算法在引导范围内寻求最优引导车速。以典型交叉口左转通行为例开展Matlab仿真实验,结果表明,采用加速引导策略时车辆能耗降低6.59%,通行时间减少71.11%;采用减速引导策略最高可使能耗降低6.07%,通行时间减少23.01%。实车实验结果表明,采用加速引导策略最高可降低能耗5.32%,通行时间减少68.12%;减速引导策略最高可使能耗降低5.14%,通行时间减少17.72%。To achieve energy-saving and efficient turning of intelligent electric vehicles at signalized intersections,a turning speed guidance strategy for intelligent electric vehicles at signalized intersections is proposed based on an electric vehicle energy consumption model associated with the target speed.For scenarios where acceleration/deceleration guidance is required,the Internet of Vehicles system is used to obtain motion information and signal status information of vehicles ahead and oncoming vehicles.Combining the information with self vehicle motion information,a speed guidance range is determined that allows vehicles to pass the intersection without stopping.Furthermore,a multi-objective optimization function is established that comprehensively considers vehicle energy consumption and travel time,and non dominated sorting genetic algorithm used to seek the optimal guiding speed within the guiding range.Matlab simulation experiments are then conducted taking the case of a typical left turn traffic at an intersection.The results show the vehicle’s energy consumption is reduced by 6.59%and travel time by 71.11%using the acceleration guidance strategy,while the vehicle’s energy consumption is reduced by up to 6.07%and travel time by 23.01%using a deceleration guidance strategy.The results of real vehicle experiments show that the vehicle’s energy consumption is reduced by a maximum 5.32%and travel time by 68.12%,while the vehicle’s energy consumption is reduced by a maximum 5.14%and travel time by 17.72%using a deceleration guidance strategy.

关 键 词：智能电动汽车 信号交叉口 车速引导 NSGA-II算法 

分 类 号：U491.25[交通运输工程—交通运输规划与管理]