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Common approaches to assess the evolution of CO 2 emissions from road vehicles are usually based on (a) estimates of future fleet composition, where most approaches consider vehicles at a rather aggregated level, and (b) emission factors, which are either based on CO 2 certification data or statistically-provided functional relationships obtained from real world test data, or a combination of the two. This approach has certain limitations in capturing the effect of new technologies on CO 2 emission related policy initiatives. The present study proposes a new method for the detailed calculation of the European light duty vehicle fleet CO 2 emissions, which could help to overcome such limitations, achieve better results when making CO 2 emissions projections and better support future policies. Simulation at individual vehicle level is combined with fleet composition data, retrieved from the official European CO 2 emissions monitoring database, and publicly available data regarding individual vehicle characteristics in order to calculate vehicle CO 2 emissions and fuel consumption over different conditions and vehicle configurations. The methodology is applied to analyze and assess the impact of the introduction of the new certification procedure, the Worldwide Light duty vehicle Test Procedure (WLTP), on the European car fleet CO 2 emissions. Results show an average WLTP to NEDC CO 2 emissions ratio of approximately 1.2. The increases in CO 2 emissions are higher for cars exhibiting lower NEDC emission values (additional 29 and 25 gCO 2 /km for vehicles emitting 100 and 119 gCO 2 /km, respectively). At higher emission levels (about 250 CO 2 g/km) WLTP and NEDC present comparable results. Three possible scenarios for the translation of projected NEDC CO 2 emissions to WLTP-based ones are quantified. [ABSTRACT FROM AUTHOR]

Background: Surging acceptance of adaptive cruise control (ACC) across the globe is further escalating concerns over its energy impact. Two questions have directed much of this project: how to distinguish ACC driving behaviour from that of the human driver and how to identify the ACC energy impact. As opposed to simulations or test-track experiments as described in previous studies, this work is unique because it was performed in real-world car-following scenarios with a variety of vehicle specifications, propulsion systems, drivers, and road and traffic conditions. Methods: Tractive energy consumption serves as the energy impact indicator, ruling out the effect of the propulsion system. To further isolate the driving behaviour as the only possible contributor to tractive energy differences, two techniques are offered to normalize heterogeneous vehicle specifications and road and traffic conditions. Finally, ACC driving behaviour is compared with that of the human driver from transient and statistical perspectives. Its impact on tractive energy consumption is then evaluated from individual and platoon perspectives. Results: Our data suggest that unlike human drivers, ACC followers lead to string instability. Their inability to absorb the speed overshoots may partly be explained by their high responsiveness from a control theory perspective. Statistical results might imply the followers in the automated or mixed traffic flow generally perform worse in reproducing the driving style of the preceding vehicle. On the individual level, ACC followers have tractive energy consumption 2.7–20.5% higher than those of human counterparts. On the platoon level, the tractive energy values of ACC followers tend to consecutively increase (11.2–17.3%). Conclusions: In general, therefore, ACC impacts negatively on tractive energy efficiency. This research provides a feasible path for evaluating the energy impact of ACC in real-world applications. Moreover, the findings have significant implications for ACC safety design when handling the stability-responsiveness trade-off. [ABSTRACT FROM AUTHOR]

Background: Surging acceptance of adaptive cruise control (ACC) across the globe is further escalating concerns over its energy impact. Two questions have directed much of this project: how to distinguish ACC driving behaviour from that of the human driver and how to identify the ACC energy impact. As opposed to simulations or test-track experiments as described in previous studies, this work is unique because it was performed in real-world car-following scenarios with a variety of vehicle specifications, propulsion systems, drivers, and road and traffic conditions. Methods: Tractive energy consumption serves as the energy impact indicator, ruling out the effect of the propulsion system. To further isolate the driving behaviour as the only possible contributor to tractive energy differences, two techniques are offered to normalize heterogeneous vehicle specifications and road and traffic conditions. Finally, ACC driving behaviour is compared with that of the human driver from transient and statistical perspectives. Its impact on tractive energy consumption is then evaluated from individual and platoon perspectives. Results: Our data suggest that unlike human drivers, ACC followers lead to string instability. Their inability to absorb the speed overshoots may partly be explained by their high responsiveness from a control theory perspective. Statistical results might imply the followers in the automated or mixed traffic flow generally perform worse in reproducing the driving style of the preceding vehicle. On the individual level, ACC followers have tractive energy consumption 2.7 – 20.5 % higher than those of human counterparts. On the platoon level, the tractive energy values of ACC followers tend to consecutively increase (11.2 – 17.3 %). Conclusions: In general, therefore, ACC impacts negatively on tractive energy efficiency. This research provides a feasible path for evaluating the energy impact of ACC in real-world applications. Moreover, the findings have significant implications for ACC safety design when handling the stability-responsiveness trade-off. [ABSTRACT FROM AUTHOR]

As of July 2017, the emissions type-approval of light-duty vehicles in Europe is based on the Worldwide Harmonized Light-duty vehicles Test Procedure (WLTP), introduced to replace the old and outdated New European Driving Cycle (NEDC) test procedure. Since some elements of the European Legislation are still based on the NEDC (2020 CO 2 emission targets, vehicle labelling, national vehicle taxation policies, etc.) in order to allow sufficient lead time to vehicle manufacturers and national authorities to adapt to the new procedure, a simulation-based approach was chosen to calculate CO 2 emissions and fuel consumption according to the NEDC regime in the period 2017–2020. To achieve this objective without significantly increasing the cost and duration of the certification procedure, existing regulation foresees that vehicles are tested over the WLTP for CO 2 emissions, the test results are used as input in a simulation model that then calculates the corresponding CO 2 according to the NEDC test protocol. A dedicated vehicle simulation model (CO 2 MPAS) was developed for the purpose and is currently used for the type-approval of new vehicles in Europe. The development specifications of CO 2 MPAS were challenging, as it had to be highly accurate, exhibit fast operation, and function with a limited number of input data. This paper presents the development principles and process followed, details of the physical models employed in CO 2 MPAS, and provides information regarding its accuracy, validity and in use operation. CO 2 MPAS achieves low errors in the prediction of the NEDC cycle that in the controlled sample used for its development are of the order of 1% with a standard deviation of 3%, while the respective in-use numbers are of the order of 1.5% and 5%. In parallel, random sampling and testing of a 10% of the type-approved vehicles also occurs in order to guarantee the quality of the CO 2 MPAS results and the validity of the process. It is concluded that CO 2 MPAS can be used to accurately estimate emissions of conventional vehicles within a ±4% accuracy range, even when limited input data are available. In addition, the in-use data analyzed suggest that the use of the tool enables the certification of about 2/3 of the new vehicle models without the need of additional experimental tests. This is an important achievement as it reduces the costs and time necessary to certify light-duty vehicle CO 2 emissions during the transitional period. Finally, it can be concluded that the use of CO 2 MPAS does not affect the declared CO 2 emissions of vehicles over NEDC conditions. [ABSTRACT FROM AUTHOR]

The interest on the impact of vehicle automation and connectivity in the future road transport networks is very high, both from a research and a policy perspective. Results in the literature show that many of the anticipated advantages of connected and automated vehicles or automated vehicles without connectivity (CAVs and AVs respectively) on congestion and energy consumption are questionable. Some studies provide quantitative answers to the above questions through microsimulation but they systematically ignore the realistic simulation of vehicle dynamics, driver behaviour or instantaneous emissions estimates, mostly due to the overall increased complexity of the transport systems and the need for low computational demand on large-scale simulations. However, recent studies question the capability of common car-following models to produce realistic vehicle dynamics or driving behaviour, which directly impacts emissions estimations as well. This work presents a microsimulation study that contributes on the topic, using a scenario-based approach to give insights regarding the impact of CAVs and AVs on the evolution of emissions over a highway network. The motivation here is to answer whether the different driving behaviours produce significant differences in emissions during rush hours, and how significant is the impact of detailed vehicle dynamics simulation and instantaneous emissions in the outcome. The status of the network is assessed in terms of flow and speed. Furthermore, emissions are estimated using both the average-speed EMEP/EEA fuel consumption factors and a generic version of the European Commission's CO 2 MPAS model that provides instantaneous fuel consumption estimates. The simulation results of this work show that AVs can deteriorate the status of the network, and that connectivity is the key for improved traffic flow. Emissions-wise, the AVs have the highest fuel consumption per km travelled among other types, while CAVs only marginally lower the overall consumption of human-driven vehicles. For the same traffic demand, the total emissions for different vehicle types remain at comparable levels. • Traditional car-following models provide overestimated instantaneous acceleration values. • AVs do not facilitate Traffic Flow, and generate the highest emissions values per kilometer. • CAVs increase the capacity of the network and therefore during peak hours, they generate more emissions in absolute values. • In total, the differences in emissions per kilometre driven between CVs, AVs, CAVs do not exceed 6%. • Results show that the use of vehicle dynamics-based modelling in microsimulation (i.e. MFC free-flow model) is in cases, essential. [ABSTRACT FROM AUTHOR]