ABSTRACT

Fuel consumption for transport activities should be as low as possible for ecological and economic reasons. This said, there is no transparency as to which truck model behaves better for a given route and weight. Existing physical models are trained using only specific reference driving cycles. This contribution proposes to use real data from telematics systems in order to extract differences in the fuel consumption of various truck models. ML models are then developed to predict their fuel consumption. Finally, the prediction model is applied to a sample roundtrip and the predicted fuel consumption of different truck models is compared.

中文翻译：

---

中重型卡车油耗数据驱动分析与预测

摘要

出于生态和经济原因，运输活动的燃料消耗应尽可能低。这就是说，对于给定的路线和重量，哪种卡车模型表现更好并没有透明度。现有的物理模型仅使用特定的参考驾驶循环进行训练。该贡献建议使用来自远程信息处理系统的真实数据，以提取各种卡车型号的油耗差异。然后开发 ML 模型来预测它们的燃料消耗。最后，将预测模型应用到一个样本往返中，并比较不同卡车模型的预测油耗。