智能网联环境下先进的传感通讯设备、互联互通数据和远程计算能力，使得汽车更安全节能的自动驾驶成为可能。然而，自动驾驶等级的提升很大程度受制于未来驾驶条件的预测，同时驾驶预测信息与汽车动力总成控制和运动控制有效结合也是一个有待深入研究的问题。因此，本项目以智能网联插电混合动力汽车(PHEV)为研究对象，研究驾驶条件预测及基于预测信息的PHEV能量管理和自适应巡航控制的关键问题：1)研究引入在线学习机制的未来驾驶条件预测问题，主要研究如何利用人工智能学习技术有效提高驾驶条件预测模型的预测精度；2)研究结合驾驶条件预测信息的基于自适应学习的能量管理优化控制，重点研究具有分层预测协调功能的自适应实时能量管理控制策略；3)研究结合驾驶条件预测信息的协同互联自适应巡航控制策略，主要研究考虑安全性、鲁棒性和经济性多目标动态优化的协同互联自适应巡航控制。本项目的研究为PHEV进一步智能化提供一定的理论支持。

Due to advanced sensing and communication equipment, connectivity data and remote computing conditions under intelligent and connected environment, it is more possible for vehicles to operate in autonomous driving style with safer and more energy efficient performance. However, higher level of the autonomous driving is largely subject to the prediction of future driving conditions. How to effectively integrate the predicted information into powertrain control and motion control is still a bottleneck problem to be further studied. Consequently, taking the intelligent and connected plug-in hybrid electric vehicles (PHEV) as the research object, we will study the prediction method to driving conditions and how to combine the predicted driving information with the corresponding PHEV energy management and adaptive cruise control. 1) Study on prediction methods with online learning mechanism for future driving conditions. The main research is how to use artificial intelligent learning technique to improve the prediction accuracy of the prediction model. 2) Study on adaptive learning-based energy management optimization control combined with the prediction information of driving conditions. The research is focused on adaptive real-time energy management control with hierarchical prediction coordination technique. 3) Study on cooperative connected adaptive cruise control combined with the prediction information of driving conditions. The main research is the cooperative connected adaptive cruise control based on multi-objective dynamic optimization for safety, robustness and economy. The research results of this project will provide a certain theoretical support for further intelligence of PHEV.