新能源汽车的智能化和网联化是未来汽车发展的必然趋势，当前智能网联插电式混合动力汽车（PHEV）能量管理策略未能充分考虑自主车速规划与动力源转矩分配之间的耦合关系，难以解决安全性、通行性和燃油经济性多目标最优的能量管理问题。本项目以智能网联PHEV为研究对象，以解决自主驾驶过程中车速/转矩协同优化能量管理问题为目标，研究基于关键路况信息的交通环境耦合建模方法，探明各个关键路况特征参数对PHEV行驶性能的影响规律，构建适应复杂交通环境的PHEV“巡航-跟车-通行”多行驶模式耦合控制模型，建立智能网联PHEV行驶模式自主决策系统，揭示在关键路况信息下车速规划与转矩分配之间的耦合关系，探索智能网联PHEV车速/转矩协同优化能量管理方法，提出一套实现智能网联PHEV自主车速规划与实时转矩分配协同控制的多目标综合最优能量管理策略，为实现新能源汽车技术和智能网联技术深度融合提供理论和使能技术支撑。

Intellectualization and network connection of clean energy vehicles represent the inevitable trend of future development, and energy management strategies (EMSs) of intelligent connected plug-in hybrid electric vehicles (PHEVs), however, do not account for the coupling relationship between autonomous velocity planning and torque distribution of power sources, leading to difficulty of attaining multi-objective optimization of energy management including safety, trafficability and fuel economy. Motivated by this, the project will focus on the cooperative energy management including velocity and torque optimization of intelligent connected PHEVs during autonomous driving. The coupled modeling methodology of transportation and driving environment will be researched based on the key driving information, and the influence principle of all crucial driving characteristic parameters on the driving performance of PHEVs will be investigated. Next, the coupled control model based on multi driving modes, i.e., cruising, following and passing, will be constructed for adapting to complex transportation environment, and the autonomous decision-making principle of driving modes will be established for intelligent connected PHEVs. Then, the coupling relationship between the velocity planning and torque distribution will be revealed under key driving conditions, and the cooperative optimization-based EMS in terms of velocity and torque will be explored for intelligent connected PHEVs. Finally, a uniform multi-objective optimal EMS will be developed for the cooperative control of autonomous velocity planning and real-time torque distribution of intelligent connected PHEVs, thereby supplying solid theoretical foundation and technical support for in-depth integration of clean energy vehicles and intelligence networking technologies.