Vehicle Speed Planning and Control for Fuel Consumption Optimization with Traffic Light State
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摘要: 基于车联网（Vehicular ad hoc networks，VANETs）进行车辆和信号灯的协同控制是下一代智能交通系统（Intelligent transportation systems，ITSs）中非常重要的核心技术之一.本文提出了一种预测信号灯信息的车辆低油耗环保驾驶控制系统.首先，根据道路信息和牛顿第二定律建立车辆动态模型，根据系统测量的信号灯状态信息，获得车辆避免刹车情况下通过前方信号灯的参考速度.然后，结合基于油耗模型和速度跟踪的综合优化指标，运用模型预测控制（Model predictive control，MPC）方法计算车辆的最优控制输入，并利用Laguerre函数方法对MPC问题进行求解.仿真表明，该系统可减少路口不必要的停车和刹车操作，节约燃油.Abstract: Vehicle and traffic timing joint control based on vehicular ad hoc networks (VANETs) is one of the most significant core technologies in the next generation intelligent transportation systems (ITSs). This paper presents an eco-driving control system aiming at minimizing fuel consumption via traffic timing prediction. First, a vehicle dynamic model is established according to the road conditions and Newton's second law. The reference velocity for the car to pass through a signal intersection ahead without stopping is calculated by predicting the traffic light state. Then, based on a comprehensive optimization index, which is a function of fuel consumption and velocity tracking error, the optimal control input is obtained in the framework of model predictive control (MPC). The solution to the MPC problem is derived using Laguerre function method. Simulation results illustrate that this method can effectively decrease the waiting times and unnecessary brake operations at intersections, and at the same time reduce the fuel consumption.
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Key words:
- Traffic timing /
- model predictive control (MPC) /
- fuel cost /
- speed tracking
1) 本文责任编委 侯忠生 -
图 2 基于信号灯信息的车辆驾驶距离曲线
Fig. 2 The vehicle driving distance curve with\\ traffic light information
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