Many of the existing studies on vehicular fuel consumption estimation are criticized in aspects such as ignoring real-world training data, low diversity of test fleet, impracticality of models in real-world applications (i.e. instrument-independent eco-driving), or their prediction power in the non-linear multi-dimensional space of fuel consumption estimation. In this paper, we proposed a machine learning modeling method using large on-road data collected from a fleet of 27 vehicles. The usability of models in absence of specialized instruments was in focus. We tried to improve the accuracy of our base models by introducing engine-speed estimates through a cascaded modeling procedure. As a result, the accuracy of models reached 83%, while improvements as high as 37% were achieved depending on the technique (support vector regression or artificial neural networks) and vehicle class. Finally, we took the first step from vehicle-specific models towards category-specific modeling by a categorical analysis over fleet attributes.

现有的许多有关汽车燃油消耗估算的研究都受到批评，例如忽略了现实世界的训练数据，测试车队的多样性低，模型在实际应用中的不切实际性（即与仪表无关的生态驾驶）或其预测。非线性多维空间中的油耗估算功率。在本文中，我们提出了一种使用从27辆车的车队中收集的大量公路数据的机器学习建模方法。在没有专门工具的情况下模型的可用性是重点。我们尝试通过级联建模过程引入发动机转速估算值，以提高基本模型的准确性。结果，模型的准确性达到83％，而根据技术（支持向量回归或人工神经网络）和车辆类别的不同，可实现高达37％的改进。最后，通过对车队属性进行分类分析，我们从车辆专用模型迈向了类别专用模型的第一步。