约束非线性车辆队列分布式多目标模型预测控制

针对具有状态和控制约束的非线性车辆队列系统多目标控制问题,提出一种分布式多目标模型预测控制(model predictive control, MPC)策略.首先,基于前车-后车单向通信拓扑,建立网联车辆队列非线性纵向巡航模型,应用字典序算法描述分布式多目标MPC问题;然后,通过设计弦稳定与收缩约束,并结合MPC三要素条件,保证车辆队列在经济性能与协同性能最优条件下的稳定性和弦稳定性结果;最后,通过典型工况的仿真结果验证所提出策略的有效性.     

基于Dymola的ECUV纯电动汽车能量流仿真

为了准确分析纯电动汽车的能耗问题,以研究纯电动汽车能量传递为目的,对纯电动汽车系统结构进行了分析。应用多物理标准化统一建模语言Modelica,采用Dymola支撑平台,建立了ECUV纯电动汽车层次化、机电控的统一模型。基于测试数据,对电机及控制系统模型进行了标定、验证。完成了整车工况仿真,针对纯电动汽车整车及子系统的能量消耗,计算得出ECUV纯电动汽车行驶能耗平衡图,提出提高纯电动汽车能量利用率的措施。     

负载影响下纯电动汽车配送路径规划及充电策略

由于纯电动汽车行驶里程的限制,在满足商用要求的前提下,纯电动汽车用于长途运输服务在短期内难以实现。不过,城市物流因其配送区域较小、货物的批量较小、批次较多的特点,可以考虑使用纯电动汽车来完成城市的配送任务。为满足车辆当天多次配送任务的要求以及考虑车辆负载对实时能耗的具体影响,建立了考虑车辆负载对实时能耗影响的配送模型,以及时满足客户的服务时间要求。并以城市A为例,设计了蚁群算法对模型进行求解,为纯电动汽车的配送任务进行合理的路径规划与充电策略的安排。最后,通过与使用燃油车辆运营相比较,分析未来纯电动汽车在城市配送物流中的可行性。     

基于Dijkstra算法的动态交通诱导技术及仿真

研究车辆行驶过程中的路径动态诱导问题,针对目前交通导航系统不能实时动态规划行驶路线的不足,结合自主研发的车载终端装置,通过对Dijkstra算法的改进及优化,提出了一个可应用于交通诱导过程的动态实时最优路径算法;基于该路径优化算法,车载终端装置可以通过接受交通控制中心的实时道路信息,不断调整车辆的行驶路线,最终实现行驶路线的全程动态优化;仿真实例证明:在实时交通信息的引导下,动态交通诱导技术保证了行驶路线的全程优化.     

基于实时交通信息的车辆路径问题研究

石油化工企业的物流配送越来越成为降低成本、提高效率的1个重要环节,而车辆路径问题是其中的基础性问题。面对各种不确定性,动态随机车辆路径问题越来越成为有价值的研究方向,其关键在于实时交通信息的利用。本文研究了基于实时交通信息的单配送中心、有时间窗口约束的车辆路径问题,建立了混合整数规划模型,提出了利用实时速度估计信息的动态调度策略,并设计了带插入规则的节约算法。通过对标准benchmark问题进行仿真,验证了策略和算法的有效性。     

增程式电动汽车控制策略的优化研究

针对增程式电动汽车恒功率控制策略中发动机工作点难以选择的问题,运用一种基于多目标遗传算法的优化方法,以百公里油耗和排放指标为优化目标,利用AVL CRUISE和Matlab/Simulink软件联合建立增程式电动汽车整车动态性能仿真分析模型,针对NEDC工况和FTP75工况进行恒功率控制策略下发动机工作点优化,仿真结果显示,优化后的发动机工作点有效改善了百公里油耗和尾气排放量。该优化方法可以减少设计者调试和选择电动汽车增程器发动机工作点的时间,具有良好的实用价值。     

负载影响下纯电动汽车配送路径规划及充电策略

由于纯电动汽车行驶里程的限制，在满足商用要求的前提下，纯电动汽车用于长途运输服务在短期内难以实现。不过，城市物流因其配送区域较小、货物的批量较小、批次较多的特点，可以考虑使用纯电动汽车来完成城市的配送任务。为满足车辆当天多次配送任务的要求以及考虑车辆负载对实时能耗的具体影响，建立了考虑车辆负载对实时能耗影响的配送模型，以及时满足客户的服务时间要求。并以城市A为例，设计了蚁群算法对模型进行求解，为纯电动汽车的配送任务进行合理的路径规划与充电策略的安排。最后，通过与使用燃油车辆运营相比较，分析未来纯电动汽车在城市配送物流中的可行性。     

V2X环境下基于MPC算法的弯道区域CACC车队轨迹跟踪控制策略

针对CACC(cooperative adaptive cruise control)车队在弯道行驶的安全性和稳定性问题,提出一种V2X(vehicle to everything)环境下基于MPC(model predictive control)算法的弯道区域CACC车队行驶轨迹跟踪策略.首先,分析CACC车队在弯道区域的行驶工况以及纵向平衡问题,并基于牛顿第二定律构建车辆在弯道行驶的车辆动力学模型;其次,CACC车队基于V2X技术实现车车之间状态信息的实时交互,并以基于车辆运动学的MPC算法为基础,引入可变间距的车队安全距离控制模型,提出一种适用于弯道区域的轨迹跟踪模型;最后,通过二次规划进行模型求解.实验分析结果表明:V2X环境下的CACC车队在弯道行驶过程中面对不同的行驶工况能够不同程度地保证车车之间的安全性、稳定性以及驾乘人员的舒适性,有效验证了所提V2X环境下基于MPC算法的弯道区域CACC车队轨迹跟踪策略的可行性.     

面向自由飞行目标捕获的四旋翼最优轨迹规划

自由飞行目标物捕获作为动态任务，在其被执行的过程中，四旋翼不仅要规划出一条时间最优的追踪轨迹，而且还要根据目标物的位置反馈信息实时对轨迹进行重新规划，以实现在最短的时间内追上目标物.针对这一问题，提出了诱导时间最优MPC （model predictive control）算法用于四旋翼的轨迹规划.该算法通过宽松约束条件下时间最优轨迹的引导，利用MPC的滚动优化策略，可以在每个控制周期内用反馈信息实时求解时间最优的追踪轨迹.为了躲避追踪路径中的障碍物，本文还提出了一种用动态线性约束表示障碍物的方法，以提高障碍物约束下轨迹求解的效率.结合诱导时间最优MPC的算法，可以在线实时地求解出具有障碍物避碰能力的时间最优轨迹.仿真结果表明了本文提出算法的有效性，其高效的计算效率也能满足实际系统对算法实时性的要求.     

电动汽车机电复合制动力分配策略研究

为了实现再生制动力与机械制动力在驱动轮和从动轮之间的优化分配,在保证车辆制动安全的同时提高能量回收效率,将汽车理想制动力分配I曲线与模糊算法相结合,提出一种基于模糊控制的电动汽车机电复合制动力分配策略。设计了电动汽车再生制动力分配模糊控制器,根据车辆工况与理想制动力分配I曲线,计算前后轮上分别应加载的机电复合制动力大小。建立了电动汽车制动系统动力学仿真模型,在此基础上进行仿真分析。最后利用Advisor仿真软件对该分配策略进行回收能量效率测试。结果表明,该分配策略既能保证汽车前后轮的制动力分配按照理想制动力I曲线分布,确保汽车的制动安全;又能有效地实现再生制动能量回收,提高电动汽车的续驶里程。     

Editorial - Special issue on networked system control and connected vehicles: opportunities and challenges

CASE (Connected, Automated, Sharing, and Electrifying) is a global trend in the automotive industry due to the big potential in improving energy efficiency and reducing the air pollution from automobile exhaust. Indeed, the connectivity, connecting the vehicles with the internet, is firstly implemented in the automotive industry in the sense of large scale connection of vehicles. The connected environment has been two decades in the automotive industry which enables us to provide a much comfortable and smart telemetric service. However, the attention has not been focused on the control technology with the connectivity for efficiency and emission improvement. From the view of system control, the connected vehicles are large-scaled, multi-agent or high dimension systems that coupled and interacted but centralized control is not reasonable. How to formulate the optimization or control problem for the connected vehicles and how to solve the problem with system control theory are significant challenging issues. This special issue collected seven papers that addressed these control problems from the view of networked system and optimal control theory. The collection can be divided into three groups. The first group includes three papers that focused on vehicle control with the use of V2V and V2I information. The article by Qiuyi Guo et al., demonstrated the possibility of improving the fuel economy of fuel cell trucks using the traffic light signal. It is shown that with the V2I information, the model predictive control technology can save more than 7.43\% hydrogen consumption in a case study driving cycle. The model predictive control technology is also applied to car-following control on an urban road network by using V2V and V2I information. The paper by A. S. M. Bakibillah et al., investigated this issue and it is shown that the control with V2Vand V2I can improve traffic flow and fuel economy. The paper by Bo Zhang et al., proposed a two-stage optimization approach for speed planning and energy management of hybrid electric vehicles, where the control policy of MPC is fully applied in the two stages of design and a typical scenario of merging is targeted. The second group collected two papers that focused on automated driving. For automated vehicles, control of vehicle dynamics is the main subject, but it is an important elemental subject for driving vehicles under connected environment. Controlling an individual vehicle in the scene of parking is addressed in the paper by Dequan Zen et al. which also demonstrated real test results. The issue of driving-by-wire full is investigated in the paper by Ping Wang et al., where again MPC is exploited for developing the real-time control law. Finally, two articles are collected that discussed active fault tolerant control for connected mobile robots by M. Hussein et al., and powertrain control for electric vehicles with robust control theory by J. Buerger ad J. Anderson, respectively.     

Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

In this paper, we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles (HEVs) on a road with slope. Moreover, it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything (V2X), including geographic information, vehicle-toinfrastructure (V2I) information and vehicle-to-vehicle (V2V) information. The provided simulator consists of an industriallevel HEV model and a traffic scenario database obtained through a commercial traffic simulator, where the running route is generated based on real-world data with slope and intersection position. The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time. To show the HEV powertrain characteristics, a case study is given with the speed planning and energy management strategy.     

信息融合架构下的新型再生制动控制策略

再生制动能够实现能量的回收利用,是电动汽车重要的工作模式之一.现有的制动力分配方案对蓄电池和电机的限制因素考虑不够充分,能量回收效率和制动效能较低.对此,提出一种基于信息融合架构下的新型再生制动控制策略.在蓄电池和电机限制因素的基础上,综合考虑增加电动车的续驶里程和制动时的舒适性、安全性等因素,对于电动汽车的不同行驶工况具有自适应性,能够实现能量高效回收;对车辆行驶速度和制动强度进行特征提取和制动模式分类,从而根据特征匹配结果切换制动模式.最后,通过搭建Matlab/Simulink整车动力学仿真模型,验证所提出控制策略的有效性和先进性.     

Energy Control of Plug-In Hybrid Electric Vehicles Using Model Predictive Control With Route Preview

The paper proposes an adoption of slope, elevation, speed and route distance preview to achieve optimal energy management of plug-in hybrid electric vehicles (PHEVs). The approach is to identify route features from historical and real-time traffic data, in which information fusion model and traffic prediction model are used to improve the information accuracy. Then, dynamic programming combined with equivalent consumption minimization strategy is used to compute an optimal solution for real-time energy management. The solution is the reference for PHEV energy management control along the route. To improve the system's ability of handling changing situation, the study further explores predictive control model in the real-time control of the energy. A simulation is performed to model PHEV under above energy control strategy with route preview. The results show that the average fuel consumption of PHEV along the previewed route with model predictive control (MPC) strategy can be reduced compared with optimal strategy and base control strategy.      

Real-time energy-efficient anticipative driving control of connected and automated hybrid electric vehicles

In this paper, we propose a real-time energy-efficient anticipative driving control strategy for connected and automated hybrid electric vehicles (HEVs). Considering the inherent complexities brought about by the velocity profile optimization and energy management control, a hierarchical control architecture in the model predictive control (MPC) framework is developed for real-time implementation. In the higher level controller, a novel velocity optimization problem is proposed to realize safe and energy-efficient anticipative driving. The real-time control actions are derived through a computationally efficient algorithm. In the lower level controller, an explicit solution of the optimal torque split ratio and gear shift schedule is introduced for following the optimal velocity profile obtained from the higher level controller. The comparative simulation results demonstrate that the proposed strategy can achieve approximately 13% fuel consumption saving compared with a benchmark strategy.     

基于动态规划不同优化目标的HEV转矩分配策略对比研究

双动力源的结构模式使得混合动力汽车相对于传统汽车拥有更高的燃油经济性,同时也给混合动力汽车整车控制器的设计提出了更高的要求。本文采用动态规划算法,分别以油耗最低同时电池SOC波动尽可能小、以及整车效率最高为目标,对混合动力汽车在NEDC循环工况下的最优转矩分配进行求解。并将两种转矩分配结果进行对比分析,得出选择不同优化目标对控制效果的影响以及SOC参数选择的标准,为制定更加高效的控制规则提供了理论依据。     

基于模糊逻辑算法的纯电动客车起步加速能力优化与再生制动控制

日益严重的环境问题促使城市交通向着清洁、高效和可持续的方向发展,同时也促进了新能源交通技术的推广和应用。随着电池和电机驱动技术的发展,纯电动客车也受到越来越多的关注。起步加速能力和可再生制动是纯电动公交车区别于传统内燃机车的两个方面。由于加速踏板信号响应与驱动电机响应较快,理论上纯电动客车的加速性能要优于传统内燃机车。再生制动是一种降低能耗、提高续驶里程的重要技术手段。文章基于模糊逻辑算法,设计了驱动扭矩控制策略对驱动工况下的纯电动客车起步加速能力进行优化。同时,针对纯电动客车制动工况设计了能量回收策略。结果表明,驱动扭矩控制策略可使纯电动客车起步加速时间从19.7 s减小至19.25 s,制动能量回收策略在中国典型城市公交路况下使能量消耗减少11%。     

Predictive car-following scheme for improving traffic flows on urban road networks

Driving behavior is one of the main reasons that causes bottleneck on the freeway or restricts the capacity of signalized intersections. This paper proposes a car-following scheme in a model predictive control (MPC) framework to improve the traffic flow behavior, particularly in stopping and speeding up of individual vehicles in dense urban traffic under a connected vehicle (CV) environment. Using information received through vehicle-to-vehicle (V2V) communication, the scheme predicts the future states of the preceding vehicle and computes the control input by solving a constrained optimization problem considering a finite future horizon. The objective function is to minimize the weighted costs due to speed deviation, control input, and unsafe gaps. The scheme shares the planned driving information with the following vehicles so that they can make better cooperative driving decision. The proposed car-following scheme is simulated in a typical driving scenario with multiple vehicles in dense traffic that has to stop at red signals in multiple intersections. The speeding up or queue clearing and stopping characteristics of the traffic using the proposed scheme is compared with the existing car-following scheme through numerical simulation.     

Optimization management of hybrid energy source of fuel cell truck based on model predictive control using traffic light information

Energy optimization management can make fuel cell truck (FCT) power system more efficient, so as to improve vehicle fuel economy. When the structure of power source system and the torque distribution strategy are determined, the essence is to find the reasonable distribution of electric power between the fuel cell and other energy sources. The paper simulates the assistance of the intelligent transport system (ITS) and carries out the eco-velocity planning using the traffic signal light. On this basis, in order to further improve the energy efficiency of FCT, a model predictive control (MPC)-based energy source optimization management strategy is innovatively developed, which uses Dijkstra algorithm to achieve the minimization of equivalent hydrogen consumption. Under the scenarios of signalized intersections, based on the planned eco-velocity, the off-line simulation results show that the proposed MPC-based energy source management strategy (ESMS) can reduce hydrogen consumption of fuel cell up to 7\% compared with the existing rule-based ESMS. Finally, the Hardware-in-the-Loop (HiL) simulation test is carried out to verify the effectiveness and real-time performance of the proposed MPC-based energy source optimization management strategy for the FCT based on eco-velocity planning with the assistance of traffic light information.