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The implementation of emission standards has brought significant reductions in vehicle emissions in the EU, but road transport is still a major source of air pollution. Future emission standards will aim at making road vehicles as clean as possible under a wide range of driving conditions and throughout their complete lifetime. The current paper presents the methodology followed by the Consortium for ultra LOw Vehicle Emissions (CLOVE) to support the preparation of the Euro 7 proposal. As a first step, the emission performance of the latest-technology vehicles under various driving conditions was evaluated. Towards this direction, an emissions database was developed, containing data from a wide range of tests, both within and beyond the current RDE boundaries. The results revealed that harsh accelerations, extreme ambient temperatures, very short trips (particularly at urban conditions), DPF regeneration and uphill driving, or combination of those conditions, can result to high emissions. Next, suitable technology packages to address such high emissions were defined and evaluated, using simulation models. On top of this analysis, additional elements were assessed, namely on-board emissions monitoring, additional species to be regulated and instrumentation for future on-road emission testing. The overall analysis revealed that existing state-of-the-art emission control technologies can achieve very low emission levels, but not under all driving conditions. Thus, additional improvements and potential new technologies are needed to bring ultra-low emissions. These technologies include larger exhaust aftertreatment devices, optimized engine and aftertreatment thermal management (mainly during cold-start) and further penetration of electrification. Particularly the latter is heavily enforced by the CO2-related measures and can strongly support the limitation of pollutant emissions, as well.

Real-world vehicle emissions differ from the legislative emissions limits for a number of reasons. Emissions can be substantially lower but in most cases emissions in real driving conditions are higher than the type-approval values. This is especially the case for NOx emissions from diesels. Between Euro 1 and Euro 5 standards the European NOx limit decreased by a factor of five but real-world NOx emissions have remained more or less constant. High real-world NOx emissions from light and heavy-duty diesels are the main cause of high NO2 concentrations in cities. Some Euro 6 vehicles now show real-world NOx emissions close to the limit, but many vehicle models still exceed the limit by a factor of eight in real-world driving conditions. This report outlines the main reasons for the deviations and discusses the implications for managing air pollution. The European situation is taken as example.

In-use fuel consumption data of 924 passenger cars (611 petrol, 313 diesel) were collected from various European sources and were evaluated in comparison to their corresponding type-approval values. The analysis indicated that the average in-use fuel consumption was higher than the type-approval one by 11% for petrol cars and 16% for diesel cars. Comparison of this dataset with the Travelcard database in the Netherlands showed that the deviation increased for late model years and in particular for cars with low type-approval values. The deviation was higher than 60% for vehicles registered in 2012 within the 90–100 gCO 2 /km bin. Unrealistic vehicle resistances used in type-approval were identified as one of the prime reasons of the difference. A simplified linear model developed in the study may be used to predict in-use fuel consumption based on data publicly available. The model utilizes the fuel consumption measured in type-approval, the mass, and the engine capacity to provide in-use fuel consumption. This may be either used to correct fuel consumption factors currently utilized by emission models (e.g. COPERT, HBEFA, VERSIT+, and others) or could be used independently to make projections on how fuel consumption may develop on the basis of changing future passenger cars characteristics.

Diesel NOx emissions have been at the forefront of research and regulation scrutiny as a result of failures of late vehicle technologies to deliver on-road emissions reductions. The current study aims at identifying the actual emissions levels of late light duty vehicle technologies, including Euro 5 and Euro 6 ones. Mean NOx emission factor levels used in the most popular EU vehicle emission models (COPERT, HBEFA and VERSIT+) are compared with latest emission information collected in the laboratory over real-world driving cycles and on the road using portable emissions measurement systems (PEMS). The comparison shows that Euro 5 passenger car (PC) emission factors well reflect on road levels and that recently revealed emissions control failures do not call for any significant corrections. However Euro 5 light commercial vehicles (LCVs) and Euro 6 PCs in the 2014–2016 period exhibit on road emission levels twice as high as used in current models. Moreover, measured levels vary a lot for Euro 6 vehicles. Scenarios for future evolution of Euro 6 emission factors, reflecting different degree of effectiveness of emissions control regulations, show that total NOx emissions from diesel Euro 6 PC and LCV may correspond from 49% up to 83% of total road transport emissions in 2050. Unless upcoming and long term regulations make sure that light duty diesel NOx emissions are effectively addressed, this will have significant implications in meeting future air quality and national emissions ceilings targets. © 2016 Elsevier Ltd

European light duty vehicle emission legislation is gradually shifting the focus from test procedures with merely static test cycles, towards procedures including Real Driving Emissions (RDE), as they are a mean to achieve the European (NOx) emission reduction target. Hence a RDE trip must represent European driving behavior, such that «real world driving» emissions can be assessed. However, the usage of portable emission measurement systems has technical limitations. An alternative is testing on the chassis dynamometer in the laboratory. In order to cover a wide range of driving behavior to evaluate the real world driving emissions, a Random Cycle Generation methodology based on two Markov chains and real world measured data is developed. This methodology is used in order to develop the WLTP Random Cycle Generator Tool. cop. 2015 IEEE.

A consistent approach to Hamiltonian SU (N) lattice gauge field theory is developed using the maximal-tree gauge and an appropriately chosen set of angular variables. The various constraints are carefully discussed, as is a practical means for their implementation. A complete set of variables for the colourless sector is thereby determined. We show that the one-plaquette problem in SU (N) gauge theory can be mapped onto a problem of N fermions on a torus, which is solved numerically for the low-lying energy spectra for N ≤ 5. We end with a brief discussion of how to extend the approach to include the spatial (inter-plaquette) correlations of the full theory, by using a coupled-cluster method parametrisation of the full wave functional.

This paper presents a methodology and tool that stochastically generates drive cycles based on measured data, with the purpose of testing and benchmarking light duty vehicles in a simulation environment or on a test-bench. The WLTP database, containing real world driving measurements, was used as input data. Consequently cycles that contain typical accelerations per velocity and road types are generated, such that these cycles are representative to real driving behavior.The stochastic drive cycle generator is developed in Matlab and is based on Markov processes. Two separate stochastic generators are used: one for generating the road type and one for generating the vehicle acceleration. First, a random road type profile is generated from the four different road types that are considered in the WLTP database: urban, rural, motorway and high-motorway, each of them with sub-road types based on different velocity bins. For each sub-road type, speed data and acceleration data will be used to partition the data into classes and states. The second random generator function is used to generate the vehicle acceleration corresponding to the already generated road type. Then, the speed profile is derived from the random generated acceleration profile. Copyright 2015 SAE International.

In the Netherlands a dense gas distribution network delivers gas to the majority of households. Traditionally only a few big suppliers deliver gas to the network, but currently there is a growing number of new small green gas suppliers in an increasingly flexible and competitive gas transport market. The gas transport providers, responsible for delivering gas to their customers, are confronted with a much more challenging task in operating the network, especially when confronted with fast changes in supply and demand and during maintenance and incidents. This complexity asks for operator support operating the network. In this paper we show, based on physical models of a typical network, involving hundreds of kilometers of pipelines and thousands of nodes and branches, how a fast and versatile simulation model, can predict the pressures and flows in the network. This gas transport simulation: predicting pressures, flows and mixtures at every location in the network, is the basis of controlling a less robust gas network, through periods of high and low demand. We will simulate a real network with the physical relations between flow, pressure, pressure drop, time-delay and mixing. Due to the nonlinear relations between pressure and flow, the standard techniques of dynamical (electrical) network simulation cannot be applied. They lead to instabilities, numerical inaccuracies and slow convergence. We will show how we solved these problems. We further show how this prediction model fits in an operator support environment that helps network operators in real-time management of operations.

The Dutch national transport CO2 emissions are determined by summing individual cases: a particular vehicle, on a particular road and traffic situation. In this paper the different aspects and the relations among them, as used in emission predictions, are outlined. In particular the central role that the CO2 type-approval value (from the NEDC test) plays in the real-world CO2 emissions since 2000 is clarified. © The author(s) and/or their employer(s), 2012.