Recently, individual electric mobility gains significance due to legislation and social discussion. Customers demand longer battery ranges. Advanced planning is a different and more sustainable approach. Potentially, they assist drivers in exploiting the installed range on long journeys. Earlier research of the authors showed that an optimal combination of speed, charging choice and amount potentially reduces overall traveling time on long trips. In this work, a dynamic programming algorithm controls this strategy set time-optimally on an all-electric route from Munich to Verona. For this, location-specific fleet-based data of over 600 000 km are used to improve the reliability of the strategy set in two ways. Firstly, the data provide more realistic location- and time-specific velocity bounds for speed control. Secondly, they provide fleet-sourced dynamics to a traceable analytical consumption model. These additional dynamics lead to 1.8 - 2.3 $ more energy demand in the strategy planning compared to a less accurate consumption map-based approach. Here, the incorporation of dynamics increases the optimizations’ reliability. Also, the time-dependent fleet-data allows finding an optimal departure time for the given route. In total, the incorporation of fleet information enhances the robustness of the optimization. This enables a more seamless experience of electric mobility on long trips.

中文翻译：

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全电动高山穿越：基于舰队车辆数据的时间最优策略计算

最近，由于立法和社会讨论，个人电动出行变得越来越重要。客户要求更长的电池续航时间。高级计划是一种不同且更可持续的方法。它们有可能帮助驾驶员在长途旅行中利用已安装的范围。作者的早期研究表明，速度，充电选择和电量的最佳组合有可能减少长途旅行的总体旅行时间。在这项工作中，动态编程算法在从慕尼黑到维罗纳的全电动路线上，以最佳时间控制该策略。为此，使用了超过60万公里的基于位置的特定车队数据，以两种方式提高了该策略集的可靠性。首先，数据为速度控制提供了更实际的位置和时间特定的速度范围。其次，他们将车队来源的动力学提供给可追溯的分析消耗模型。与不那么精确的基于消耗图的方法相比，这些额外的动力导致战略规划中的能源需求增加了1.8-2.3美元。在这里，动力学的结合提高了优化的可靠性。同样，随时间变化的车队数据允许找到给定路线的最佳出发时间。总的来说，车队信息的合并增强了优化的稳定性。这使长途旅行中的电动出行更加顺畅。同样，随时间变化的车队数据允许找到给定路线的最佳出发时间。总的来说，车队信息的合并增强了优化的稳定性。这使长途旅行中的电动出行更加顺畅。同样，随时间变化的车队数据允许找到给定路线的最佳出发时间。总的来说，车队信息的合并增强了优化的稳定性。这使长途旅行中的电动出行更加顺畅。