G. Meccariello

An integrated knowledge base for modelling and predicting vehicle real-world emissions as a function of driving behaviour kinematics

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.

Preliminary results on emissions and driving behavior of ATENA fleet test project in Naples

One of the objectives of the Atena project [1] was the definition of a methodology for the predictive evaluation of the environmental impact of different types of vehicles used in an urban scenario. The target is to obtain a methodology that allow the decision maker to verify in simulation the effects of possible measures like low enforcement to the access restrictions or vehicle fleet composition. The main obstacle is the realisation and the managing of real driving cycles in order to that overtake the limits derived from the utilisation of typical cycles (i.e. ECE o NEDC) or the simple consideration of average speed. The starting point is a digital representation of the urban network where all the roads are represented with one or more arcs and for all this arcs is available an estimation of the traffic variables like the vehicle flow [vehicles per hour] or the average speed [kph]. Every arc is described in terms of traffic parameters like the type of road (i.e. highway, district road). The results presented in this work will be the starting point for the definition of one methodology to describe the emissions of vehicles fleets in terms of traffic parameters.

A Developed SW Tool For A LCC Model Evaluation Of A Stage Pre-IIIB To Stage IIIB NRMM-compliant Vehicle

The scope of this paper is to explain the main concepts and characteristics of the LCC (low-cost carrier) model for the new subsystem to fulfill NRMM (Non-Road Mobile Machinery) Directive Stage IIIB requirements on emissions. In particular, an after treatment device and emerging technology solutions will be considered to evaluate the delta cost of new corrective and preventive maintenance plans. The LCC model is a Delta-LCC tool, with respect to previous Stage IIIB, where all the additional costs relevant to acquiring and managing vehicles powered with NRMM-compliant engines are compared with the equivalent costs currently sustained. The Delta-LCC model of each type of vehicle concerned is a SW (software) tool, developed in the Microsoft Excel environment. It could be used to estimate and compare the LCC of different alternative solutions or to perform an impact analysis, as far as cost issues are concerned. It could also be used to support the Political/Legal, Economic, Social, Technical and Environmental impact (PESTLE) analyses. The Delta-LCC model structure will include development costs; manufacturing and installation costs; operation and maintenance costs; and decommissioning costs. The paper describes the development of such a tool and, as a case study based on literature data, focuses on its application in supporting the evaluation of cost integration of a diesel engine builder in a new rail vehicle.

Evaluation of emission factors for new technology S.I. Euro 4 cars

This research attempted to analyze the environmental impact of two available non-conventional vehicle technologies, highlighting the benefits in terms of emissions and fuel consumption reduction with respect to conventional vehicles. Moreover, tests were compared with predicted emissions of the conventional gasoline technology provided by the kinematic model developed. Results show that cold start strongly affects the emissions of CO, THC and NOX, which become very low during hot driving cycle. The existing emission model cannot be used to obtain a satisfactory prediction for hybrid car. But it could be proved that KEM emission model allows us to predict emission factor for conventional car and compressed natural gas. However, it is concluded that new experimental campaign with new technological cars might be interesting for future model developments.

Development of an “easy module” tool based on the Kinematic Emission Model

In this paper we present results from the first implementation of an “easy module” tool, through a graphical user interface (GUI), running the Kinematic Emission Model (KEM) developed at the Istituto Motori in Naples. The core of the tool is a set of regression equations applying an empirical model which calculates vehicle emissions in real-world hot driving conditions, based on a Partial Least Squares regression method. The tool is able to predict emission factors from the input of some variables. The following information is provided as input to the model: driving cycle (DC) profiles representing a traffic situation, vehicle homologation, power and displacement. These parameters are necessary to identify the appropriate coefficients model, previously stored in a data warehouse, and calculate predicted emission factors.