In this paper, a cooperative optimization strategy is proposed for velocity planning and energy management of intelligent connected plug-in hybrid electric vehicles. Based on the established vehicle model, a mathematical analytical method is investigated to convert the driving cycles from the original time based profiles to the driving distance based speed values. Then, the iterative dynamic programming is exploited to achieve the synergistic optimization in terms of speed planning and power allocation of the vehicle with the consideration of gear shifting limits and speed fluctuation. To meet the requirement of trip duration limitation which may be violated due to autonomous speed planning, the terminal driving time is constrained by adding a time adjustment factor to the cost function. The simulation results suggest that the proposed strategy attains the collaborative optimization with high efficiency in terms of speed planning and driving power distribution. In addition, the proposed strategy leads to significant reduction of the energy consumption cost under the constraints of allowed speed variation ranges.

针对智能互联插电式混合动力汽车的速度规划和能量管理提出了一种协同优化策略。基于已建立的车辆模型，研究了一种数学分析方法，以将驾驶周期从原始的基于时间的曲线转换为基于驾驶距离的速度值。然后，在考虑到换挡极限和速度波动的情况下，利用迭代动态编程来实现车辆的速度规划和功率分配方面的协同优化。为了满足可能由于自主速度计划而违反的行程持续时间限制的要求，通过在成本函数中添加时间调整因子来限制终端的驾驶时间。仿真结果表明，所提出的策略在速度规划和驱动功率分配方面实现了高效的协同优化。另外，在允许的速度变化范围的约束下，所提出的策略导致能量消耗成本的显着降低。