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An Integrated Knowledge Base for Modelling and Predicting Vehicle Real-world Emissions as a Function of Driving Behaviour Kinematics

机译:基于驾驶行为运动学对车辆实际排放进行建模和预测的综合知识库

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A multivariate modelling approach was developed by Istituto Motori to model and predict vehicle real-world emissions. Complex driving kinematics is represented by two blocks of variables, which require the development of a hierarchical multiblock emission model, where the two blocks of variables represent overall and instantaneous features of each driving cycle associated to a trip. The multiblock model was applied to analyze and model emissions of the large database built in the ARTEMIS project. In this database we collected emission measurements performed in European laboratories relative to real driving cycles which are statistically representative of many European traffic/road conditions from congested to rush-hour traffic in urban, rural and highway roads. Data concern a varied fleet of vehicles differing in technology and class. To develop a tool useful for mobility analysts for traffic environmental impact assessment, a knowledge base was envisaged to integrate the data warehouse and the model base to build a user interface for driving cycle kinematics and emission analysis. In this paper the modelling approach is presented together with overall emission and driving kinematics characterization based on experimental results, as well as functional analysis of the knowledge base structure and the information tool.
机译:Istituto Motori开发了一种多变量建模方法来建模和预测车辆实际排放。复杂的驾驶运动学由两个变量块表示,这需要开发分层的多块排放模型,其中两个变量块代表与行程相关的每个驾驶循环的整体和瞬时特征。多块模型用于分析和建模ARTEMIS项目中建立的大型数据库的排放。在该数据库中,我们收集了相对于实际驾驶周期在欧洲实验室进行的排放测量,这些测量在统计上代表了许多欧洲交通/道路状况,从城市,农村和公路的拥挤到高峰时间。数据涉及技术和类别不同的​​各种车辆。为了开发一种对交通分析员的交通环境影响评估有用的工具,设想了一个知识库来集成数据仓库和模型库,以建立用于驾驶运动学和排放分析的用户界面。在本文中,基于实验结果以及对知识库结构和信息工具的功能分析,提出了建模方法以及总体排放和驱动运动学表征。

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