Virtual powertrain analysis is widely applied in the automotive industry to cope with the increasing complexity and variance of future vehicle propulsion technologies. Since the vehicle-usage behavior has a strong impact on component loads, realistic computation results require accurate assumptions about these boundaries. In this context, driving cycles (DCs) are used to represent the system boundaries in vehicle operation. The aim of this article is to identify multiple characteristic driving cycles (CDCs) from extensive vehicle measurement data which represent the full variety of possible real-driving scenarios. Vehicle measurements are segmented and consumption-relevant features are extracted from each segment. These features are then used to apply clustering and classification techniques to identify characteristic groups that are consequently assigned to different driving environments and driving styles. In order to obtain even more realistic driving scenarios, a data-fusion approach is used to incorporate a road slope signal from a NASA digital elevation model for each segment. Lastly, a genetic mixed-integer optimization algorithm is proposed to efficiently generate representative DCs for each characteristic group of driving segments. The main contribution of this article is a data-driven identification of the parameter space of real-driving scenarios from extensive vehicle measurements including the implementation of road slope information. The scenarios are represented via a constrained number of compact CDCs which enables comprehensive investigations of new powertrain technologies under average as well as extreme real-driving conditions to develop efficiency-robust powertrain systems.

中文翻译：

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基于 k 均值聚类和混合整数优化的特征实车循环数据驱动识别

虚拟动力系统分析广泛应用于汽车行业，以应对未来车辆推进技术日益复杂和多样化的问题。由于车辆使用行为对组件负载有很大影响，因此实际的计算结果需要对这些边界进行准确的假设。在这种情况下，驾驶循环 (DC) 用于表示车辆运行中的系统边界。本文的目的是从广泛的车辆测量数据中识别多个特征驾驶循环 (CDC)，这些数据代表了各种可能的真实驾驶场景。车辆测量被分段，并从每个分段中提取与消费相关的特征。然后将这些特征用于应用聚类和分类技术来识别因此分配给不同驾驶环境和驾驶风格的特征组。为了获得更真实的驾驶场景，使用数据融合方法将来自 NASA 数字高程模型的道路坡度信号合并到每个路段。最后，提出了一种遗传混合整数优化算法，以有效地为每个特征组的驾驶段生成有代表性的 DC。本文的主要贡献是从广泛的车辆测量（包括道路坡度信息的实施）中对真实驾驶场景的参数空间进行数据驱动识别。