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A vehicle simulation model, CO 2 MPAS supports the introduction of the new WLTP-based certification system for CO 2 emissions in Europe. This paper investigates the possibility to use the underlying simulation methodology to accurately calculate CO 2 emissions over real-world trips, thus to extend the use of the methodology beyond vehicle certification. As a reference, the analysis used measurement data obtained from four vehicles over two different routes under real-world driving conditions. The CO 2 emissions were measured using portable emissions measurement systems. The formal CO 2 MPAS methodology and two modified versions of it that require a reduced number of input data were assessed about their capacity to predict the measured CO 2 emissions. The analysis focused on the accuracy and uncertainty of the three different methodology configurations. As an additional benchmark, the analysis considered the CO 2 emissions estimates obtained from the EMEP/EEA Guidebook methodology used for emissions inventorying in Europe. Results show that the basic CO 2 MPAS configuration demonstrates good performance in predicting CO 2 emissions over on-road tests, reaching a prediction accuracy over an entire test trip of −0,3% and a standard deviation of 3,1%. The modified versions showed slightly higher biases up to 3% and uncertainties (5–7%), but remaining within reasonable limits considering the reduced number of inputs used in each case. Given its ability to predict CO 2 emissions accurately on a local base, CO 2 MPAS could be used for the prediction of instantaneous CO 2 emissions in traffic micro-simulation exercises. [ABSTRACT FROM AUTHOR]

[Display omitted] • Three sampling approaches were investigated for estimating HDV fleet CO2 emissions. • Mean CO 2 emissions deviate less than 2% in the best cases and less than 5% overall. • The JDE sampling preserves well the distribution characteristics. • MEDP and MSD are suitable to investigate fleet emissions when limited data are available. • The alternative methodologies can be used to predict CO 2 emissions in future fleet scenarios. Certification and monitoring of heavy vehicle CO 2 emissions in several countries are based on individual vehicle simulation. Smaller fleet subsets can be used for accurate fleet-level results while preserving the characteristics of the underlying fleet-emissions distributions. The paper focuses on three approaches to capture fleet CO 2 emissions: a) sampling directly from the fleet-data, b) sampling from data of individual vehicle components and c) using key statistics regarding the fleet composition that are available. The first and second approach deliver marginal divergences of the mean, between 1.1 and 2.1% and below 2.7 respectively, preserving the characteristics of the distribution. The third deviated by up to 5%, but lacked the detailed characteristics of the underlying statistical distribution. All three are useful when setting up fleet-wide monitoring schemes where detailed data are not available and to investigate the potential CO 2 savings of various future fleet compositions, and scenarios regarding the diffusion of different types of technologies. [ABSTRACT FROM AUTHOR]

Abstract CO 2 emissions of light-duty vehicles are certified over standardised, laboratory-based conditions and reported to the consumers. Such tests reflect specific operating conditions that differ from what an individual driver experiences. Vehicle simulation can bridge the gap and help provide customised, vehicle and trip-specific values. This study investigates the potential of using a simulation-based approach for calculating CO 2 emissions over real-world operation, when limited information and test-data are available. The methodology introduced in the European vehicle certification regulation since 2017 is used as a basis. Seven vehicles were tested over multiple on-road trips and in some cases on a chassis dyno. First, the analysis focused on the accuracy of the simulations when only limited information for the vehicle and its components are used. Subsequently, the model was calibrated on test data. The first case presented an error between 1.0% and 4.4% depending on the test, while the standard deviation was 10.0%. When using WLTP for calibration, the average error dropped to −2.9% to −0.2%, and the standard deviation decreased to 2.0%. When calibrating over on-road tests, the average error was 0.7% for the on-road tests and 4.5% for the WLTP. Highlights • A simulation-based method for estimating lab and real world CO 2 of LDVs is investigated. • 3 different approaches were investigated based on generic, real world or lab input data. • The average error remained low (<5%) and was <2% when calibrating on tests-data. • Test-data based calibration significantly reduces the uncertainty from 10% to about 3%. • The approach could serve for extrapolating certification data to real world or vice versa. [ABSTRACT FROM AUTHOR]

The study proposes a methodology for quantifying the impact of real-world heterogeneous driving behavior on vehicle energy consumption, linking instantaneous acceleration heterogeneity and CO 2 emissions. Data recorded from 20 different drivers under real driving are benchmarked against the Worldwide Harmonized Light Vehicle Test Cycle (WLTC), first by correlating the speed cycle with individual driver behavior and then by quantifying the CO 2 emissions and consumption. The vehicle-Independent Driving Style metric (IDS) is used to quantify acceleration dynamicity, introducing driving style stochasticity by means of probability distribution functions. Results show that the WLTC cycle assumes a relatively smooth acceleration style compared to the observed ones. The method successfully associates acceleration dynamicity to CO 2 emissions. We observe a 5% difference in the CO 2 emissions between the most favourable and the least favourable case. The intra-driver variance reached 3%, while the inter-driver variance is below 2%. The approach can be used for quantifying the driving style induced emissions divergence. [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]