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SDP Policy Iteration-Based Energy Management Strategy Using Traffic Information for Commuter Hybrid Electric Vehicles

机译:基于SDP策略迭代的通勤混合动力汽车基于交通信息的能源管理策略

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This paper demonstrates an energy management method using traffic information for commuter hybrid electric vehicles. A control strategy based on stochastic dynamic programming (SDP) is developed, which minimizes on average the equivalent fuel consumption, while satisfying the battery charge-sustaining constraints and the overall vehicle power demand for drivability. First, according to the sample information of the traffic speed profiles, the regular route is divided into several segments and the statistic characteristics in the different segments are constructed from gathered data on the averaged vehicle speeds. And then, the energy management problem is formulated as a stochastic nonlinear and constrained optimal control problem and a modified policy iteration algorithm is utilized to generate a time-invariant state-dependent power split strategy. Finally, simulation results over some driving cycles are presented to demonstrate the effectiveness of the proposed energy management strategy.
机译:本文演示了一种基于交通信息的通勤混合动力汽车能源管理方法。开发了一种基于随机动态规划(SDP)的控制策略,该策略可平均降低等效燃料消耗,同时满足电池电荷维持约束和车辆整体动力需求。首先,根据交通速度曲线的样本信息,将常规路线划分为几个部分,并根据平均车速收集的数据构建不同部分中的统计特性。然后,将能量管理问题表述为随机非线性且受约束的最优控制问题,并使用改进的策略迭代算法生成时变状态相关的功率分配策略。最后,给出了一些驾驶周期的仿真结果,以证明所提出的能源管理策略的有效性。

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