Due to sparse charging infrastructure and short driving ranges, drivers of battery electric vehicles (BEVs) can experience range anxiety, which is the fear of stranding with an empty battery. To help eliminate range anxiety and make BEVs more attractive for customers, accurate range estimation methods need to be developed. In recent years, many publications have suggested machine learning algorithms as a fitting method to achieve accurate range estimations. However, these algorithms use a large amount of data and have high computational requirements. A traditional placement of the software within a vehicle's electronic control unit could lead to high latencies and thus detrimental to user experience. But since modern vehicles are connected to a backend, where software modules can be implemented, high latencies can be prevented with intelligent distribution of the algorithm parts. On the other hand, communication between vehicle and backend can be slow or expensive. In this article, an intelligent deployment of a range estimation software based on ML is analyzed. We model hardware and software to enable performance evaluation in early stages of the development process. Based on simulations, different system architectures and module placements are then analyzed in terms of latency, network usage, energy usage, and cost. We show that a distributed system with cloud‐based module placement reduces the end‐to‐end latency significantly, when compared with a traditional vehicle‐based placement. Furthermore, we show that network usage is significantly reduced. This intelligent system enables the application of complex, but accurate range estimation with low latencies, resulting in an improved user experience, which enhances the practicality and acceptance of BEVs.

中文翻译：

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评估用于电动汽车行驶里程估计和充电规划的系统架构

由于充电基础设施稀少且行驶里程短，纯电动汽车 (BEV) 的驾驶员可能会经历里程焦虑，即担心因电池耗尽而搁浅。为了帮助消除里程焦虑并使纯电动汽车对客户更具吸引力，需要开发准确的里程估计方法。近年来，许多出版物都建议将机器学习算法作为实现精确范围估计的拟合方法。但是，这些算法使用的数据量大，计算要求高。软件在车辆电子控制单元中的传统放置可能会导致高延迟，从而损害用户体验。但是由于现代车辆连接到后端，可以在其中实施软件模块，可以通过算法部分的智能分布来防止高延迟。另一方面，车辆和后端之间的通信可能很慢或很昂贵。在本文中，分析了基于 ML 的距离估计软件的智能部署。我们对硬件和软件进行建模，以便在开发过程的早期阶段进行性能评估。基于模拟，然后在延迟、网络使用、能源使用和成本方面分析不同的系统架构和模块放置。我们表明，与传统的基于车辆的放置相比，具有基于云的模块放置的分布式系统显着降低了端到端延迟。此外，我们表明网络使用量显着减少。这个智能系统能够应用复杂的、