能源危机与环境污染已成为制约我国汽车产业发展的两座大山，为应对此问题，我国正在加快推进新型汽车节能技术的发展。然而，动力总成优化等传统节能技术的“边际效应”日益凸显，已难以取得显著的技术突破，因此亟待探索能够有效降低车辆能耗的新途径。随着智能交通系统与汽车智能化、网联化技术的发展，通过网联通信系统获取智能交通系统中路网、道路、交通流等信息，使汽车能够更准确地掌握实时动态交通态势，有助于深入挖掘汽车在行驶过程中的节能潜力，提升汽车综合性能。但是，智能交通系统信息的引入将形成宏观交通-微观车辆-网联通信共融型高维复杂非线性系统，导致车辆节能与动态交通环境耦合制约。如何利用智能交通系统信息实现汽车节能行驶已成为研究热点。本文将研究智能网联环境下汽车节能行驶的基本方法和关键技术，具体开展如下工作：首先，构建了汽车行驶节能系统分层递阶式总体架构：基于智能交通系统信息，建立融合宏观交通信息的全局节能优化层，确定汽车最优节能行驶路线；在行驶过程中，结合实时动态交通信息及路段参考节能速度，实时优化汽车动态行驶节能控制策略，在微观层面实现汽车的节能优化控制。其次，研究基于智能交通大数据的汽车节能行驶路线规划方法，利用智能交通大数据系统提供的路网模型，包括交通流、红绿灯位置和控制时序等信息，构建了融合路网系统、宏观交通系统与车辆系统的广域多系统动态匹配模型，建立综合交通约束的汽车行驶能耗优化目标函数，并提出一种基于混沌优化的改进粒子群算法进行在线求解，得到宏观节能行驶路线。再次，针对交通环境动态时变、前车运动状态难以预测的难题，充分发挥网联系统优势，提出基于时序递进式贝叶斯网络的前车运动状态预测方法；同时，利用最小二乘法对汽车瞬态能耗模型参数进行辨识，并构建汽车智能节能控制器，通过伪谱法进行转换求解，求取车辆最优节能速度和加速度，实现汽车行驶中节能策略自适应动态调控。最后，通过仿真与实验对所提方法进行了验证。仿真结果表明：所提出的节能行驶路线规划方法、前车运动状态预测方法及实时节能控制方法均具有良好的效果。在此基础上，搭建了基于动态驾驶模拟器的汽车智能节能控制实验平台，并选择油耗测试循环工况对所提出的智能节能控制方法进行实验验证。实验结果表明：在动态交通环境中，相比传统ACC，所提出的多约束智能节能控制方法在保证车辆行驶安全性的同时，有效降低了车辆行驶过程中的能耗。
Energy crisis and environmental pollution have become two prominent problems restricting the development of China's automotive industry. In response to the issues, China is accelerating the development of new energy-saving technologies for automotive industry. However, traditional energy-saving technologies such as powertrain optimization have been difficult to achieve significant breakthroughs, so it is urgent to explore new ways to effectively reduce vehicle energy consumption. With the development of intelligent transportation systems(ITS), intelligent vehicle and intelligent connected technology, the information such as road network, roads and traffic flow in the intelligent transportation systems can be obtained, so the vehicle can more accurately grasp the real-time dynamic traffic situation, which is helpful to deeply tap the energy-saving potential of the automotive in the driving process and improve the comprehensive performance of the automotive industry. However, due to the diversity of information in the intelligent transportation systems, an integrated high-dimensional complex nonlinear system will be formed, resulting in coupling constraints between vehicle energy conservation and dynamic traffic environment. How to use the information of intelligent transportation systems to realize energy-saving driving has become a research hotspot. This paper will study the basic methods and key technologies for automotive energy-saving driving on the intelligent connected condition. The following work will be carried out specifically:Firstly, a hierarchical technical framework of automotive driving energy-saving system is constructed: based on intelligent traffic system information, a global energy-saving optimization layer integrating macro traffic information is established to determine the optimal energy-saving driving route of the vehicle; In the process of global energy-saving route following, combining real-time dynamic traffic information and road section energy-saving speed reference, real-time optimization of vehicle dynamic driving energy-saving control strategy is investigated, and vehicle energy-saving optimization control is realized at the microscopic level.Secondly, the planning method of vehicle energy-saving driving route based on intelligent transportation big data is studied. Using the information provided by intelligent transportation big data system, such as road network model, traffic flow, traffic light position and control timing, a comprehensive traffic model integrating basic road network, dynamic traffic information and vehicle energy consumption is constructed, and an optimization objective function of vehicle driving energy consumption with comprehensive traffic constraints is established. An improved-particle-swarm-optimization-algorithm based on chaos optimization is proposed for online solution to obtain a macro energy-saving driving route.Thirdly, aiming at the difficult problem that the traffic environment is dynamic and time-varying, and the motion state of the front vehicle is difficult to predict, a prediction method of vehicle motion based on the sequential progressive Bayesian network is proposed by using the information of the intelligent connected system. At the same time, the parameters of the vehicle transient energy consumption model are identified by using the least square method, and the intelligent energy-saving controller of the vehicle is established under the constraints of predicting the motion state of the front vehicle, the longitudinal dynamics model of the vehicle and the driving performance of the vehicle, etc. The Pseudospectral method is used for high-precision calculation to obtain the optimal energy-saving speed and acceleration of the vehicle, so as to realize real-time energy-saving control of the vehicle.Finally, the proposed methods are verified by simulation and experiments. The simulation results show that the proposed energy-saving driving route planning method, the preceding vehicle motion state prediction method and the energy-saving control method have good effects. On this basis, a vehicle intelligent energy-saving control experimental platform based on dynamic driving simulator is built, and the proposed real-time intelligent energy-saving control method is verified experimentally according to standard driving cycle conditions. The experimental results show that in the dynamic traffic environment, the proposed multi-constraint intelligent energy-saving control method not only ensures the safety of the vehicle, but also effectively reduces the energy consumption.
