网联混合动力汽车能量优化控制

能量管理策略是混合动力汽车的核心技术之一，决定了车辆的燃油经济性和排放性能。针对现有混合动力汽车的能量管理都是基于固定工况开发而没有考虑实际道路工况的问题，基于智能交通系统（ITS）和专用短程通信技术（DSRC）获取的道路交通信息和周边车辆信息，提出了一种网联混合动力汽车分层能量控制方法。其中，上层控制器利用道路交通信息和模型预测控制算法预测车辆的最优目标速度并计算出需求转矩；下层控制器利用上层控制器获得的目标车速信息，实现最优车速跟随，并使用模糊神经网络控制算法优化发动机和电动机之间的转矩分配以降低燃油消耗。仿真结果表明:与传统的能量管理策略相比，所提方法可以有效避免车辆在红灯时停车，车辆的燃油消耗率降低了34.88%，HC、CO和NO_x的排放分别降低10.59%、66.19%和1.05%，提升了混合动力汽车的燃油经济性和排放性能。 

Energy optimal control of hybrid electric vehicles in connected environment

Energy management strategy is one of the core technologies of hybrid electric vehicles, which determines the fuel economy and emission performance of the vehicle. Aiming at the problem that the existing energy management strategies of hybrid electric vehicles are all developed based on the fixed operating conditions without considering the actual road driving conditions, proposes a hierarchical energy control method for hybrid electric vehicles in the connected environment based on the road trcoffic in formation and surrounding vehicle in formation obtained by intelligent transportation system (ITS) and dedicated short range communication (DSRC) technology. Road traffic information and model predictive control algorithm are utilized to predict the optimal velocity of vehicle in the upper controller. The lower controller is designed to follow the optimal velocity by using target vehicle velocity information obtained in the upper controller, and uses the fuzzy neural network control algorithm to optimize the torque distribution between the engine and the motor to reduce fuel consumption. The simulation results show that, compared with the traditional energy management strategy, the proposed method can avoid the vehicle stopping at the red light effectively, so that the fuel consumption rate of the vehicle is reduced by 34.88%, and the emission of HC, CO, and NO_x are reduced by 10.59%, 66.19%, and 1.05%, respectively, which improves the fuel economy and emission performance of hybrid electric vehicles.