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Optimal Charging and Driving Strategies for Battery Electric Vehicles on Long Distance Trips: a Dynamic Programming Approach

机译:电动汽车长途旅行的最佳充电和驾驶策略:一种动态规划方法

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The ongoing electrification of powertrains requires innovative solutions that allow a broad application of battery electric vehicles (BEVs) with respect to different driving tasks. Especially long distance journeys for fully electric vehicles are a major obstacle due to range anxiety, the need to recharge and the lack of precise information on the driving and charging scenarios needed. An optimal strategy that consists of velocity as well as charging suggestions enables a seamless use of electric vehicles on long distance journeys. Through a dynamic programming (DP) approach, a global time optimality of driving and charging tasks for two use cases is derived and presented. The existing control levers of vehicle speed and charger choice as well as the amount of charged energy are varied in their discretization. This is done under the aspects of overall travel time and a final state constraint. With regard to its computing time, the parameters' discretization is discussed. The applicability of the problem specific method is shown, optimal strategies are calculated. Also, it can be shown that the course of the state variable, i.e. the vehicles state of charge (SOC) dominates sensitivities in time and state deviation.
机译:正在进行的动力总成电气化需要创新的解决方案,以允许在不同的驾驶任务中广泛应用电池电动汽车(BEV)。由于范围焦虑,需要充电以及缺乏所需的驾驶和充电场景的精确信息,对于纯电动汽车而言,特别是长途旅行是一个主要障碍。由速度和充电建议组成的最佳策略可以在长途旅行中无缝使用电动汽车。通过动态编程(DP)方法,得出并给出了两个用例的驾驶和充电任务的全局时间最优性。现有的车速和充电器选择控制杆以及充电能量的数量在离散化方面有所不同。这是在总行驶时间和最终状态约束的情况下完成的。关于其计算时间,讨论了参数的离散化。显示了针对特定问题的方法的适用性,并计算了最佳策略。而且,可以表明状态变量的过程,即车辆充电状态(SOC)在时间和状态偏差方面占主导地位。

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