In the context of increasing electrification and the automation of future mobility, research and development of efficient powertrains requires enhanced test methods. One important aspect to consider is the complex interactions among a smart driving strategy, traffic flow, an individual vehicle's energy demand and emissions, and the corresponding overall values. Until now, the respective development domains of traffic flow control, powertrain control, component design, and intervehicle communication have usually been considered separately. This article presents a methodology that combines these areas and enables developers to obtain deep and highly realistic system insights, taking into account the mutual interactions among the domains. For this purpose, an X-in-the-loop validation platform is constructed that builds upon vehicular networking, road traffic, and a vehicle simulator, all coupled with two real drive-unit component test benches. We further show how this methodology can be used to investigate the effects of a novel predictive powertrain control algorithm that takes vehicle-to-everything (V2X) communication into account. Focusing on a typical urban reference route, we demonstrate that our algorithm enables performance to be maintained using electric motors with reduced specifications.

中文翻译：

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迈向智能的车辆到一切连接的动力总成：在完全交互式的虚拟智能城市中驾驶实物测试台

在电气化和未来机动性自动化的背景下，高效动力总成的研究与开发需要增强的测试方法。要考虑的一个重要方面是智能驾驶策略，交通流量，单个车辆的能源需求和排放以及相应的总体价值之间的复杂相互作用。到目前为止，通常分别考虑交通流控制，动力总成控制，组件设计和车辆间通信的各个开发领域。本文提出了一种结合了这些领域的方法，使开发人员能够在考虑领域之间的相互影响的情况下获得深刻而高度现实的系统见解。为此，我们构建了一个基于车辆联网的X-in-the-loop验证平台，道路交通，车辆模拟器以及两个真实的驾驶单元组件测试台。我们进一步展示了如何使用这种方法来研究新型预测动力总成控制算法的效果，该算法将车辆到一切（V2X）的通信考虑在内。重点关注典型的城市参考路线，我们证明了我们的算法可以使用规格降低的电动机来维持性能。