COLOMBO Deliverable 4.2: Extended Simulation Tool PHEM coupled to SUMO with User Guide

This public deliverable is an extension of the draft deliverable D4.1. The first part covers the extensions performed on PHEM’s database for modelling the vehicle fleet in the year 2020. The document extension describes the second work item which was to allow using PHEM as an emission model directly within the COLOMBO overall simulation system (COSS). Both possibilities – an off-line connection with SUMO output files fed into PHEM, and an on-line approach by embedding the derivative PHEMlight into SUMO – are presented in detail

Parameterisation of fuel consumption and CO2 emissions of passenger cars and light commercial vehicles for modelling purposes

CO2 emissions of new passenger cars (PCs) registered in Europe are monitored in order to meet the objectives of Regulation EC 443/2009. This calls for an average CO2 emission of 130 g/km for new PCs registered in Europe to be met by vehicle measures in 2015. This decreases to 95 g/km in 2020. Similar regulations are gradually promoted for other vehicle categories as well, more prominently for light commercial vehicles (LCVs). CO2 emissions of new vehicle types are determined during the vehicle type-approval by testing over the New European Driving Cycle (NEDC). Worries have been expressed that this driving cycle is not representative of real-world driving conditions. It is considered that fuel consumption, and hence CO2 emissions (and air pollutant emissions), measured over this cycle under-represent reality. This report uses real-world information to compare in-use fuel consumption of PCs with type-approval CO2. The main objective was to develop functions that may enable prediction of in-use fuel consumption values, based on vehicle specifications. The functions can then be used in inventorying tools, such as COPERT and HBEFA, to correctly allocate fuel consumption to the different PC vehicle types.JRC.F.9-Sustainable Transport (Ispra

JEC Tank-to-Wheels Report v5: Heavy duty vehicles: Well-to-Wheels analysis of future automotive fuels and powertrains in the European context

In this study typical figures for fuel consumption (FC), CO2 and CO2-equivalent emissions as well as energy consumption of current and future propulsion and fuel configurations for heavy duty vehicles (HDV) have been assessed. This report covers the Tank-to-Wheels (TTW) part of a comprehensive Well-to-Wheel (WTW) analysis. The parts of the study related to Well-to-Tank (WTT) analysis and to integrated WTW view are published in separate reports. ● The following two HDV configurations have been analysed: ● Rigid truck with 18 tons gross vehicle mass rating (GVMR) designed for use in regional delivery mission (“group 4 vehicle”) ● Tractor-semitrailer combination with 40 tons GVMR designed for use in long haul mission (“group 5 vehicle”) The analysed HDV configurations are either driven with a conventional internal combustion engine (ICE) or an electrified propulsion system (xEV). ICE only configurations include the technologies: ● Direct Injection Compression Ignition (CI) ● Port Injection Positive Ignition (PI) ● LNG High Pressure Direct Injection Compression Ignition (HPDI) For CI engines the fuels Diesel B0, B7 and B100 (FAME) as well as DME, ED95, OME and Paraffinic Diesel were considered. For PI engines CNG and LNG were analysed. The electrified propulsion systems include: ● Hybrid electric vehicle (HEV) ● Battery electric vehicle (BEV) ● Catenary electric vehicle (CEV) ● Hydrogen/Fuel cell (FCEV) All considered vehicle concepts have been analysed for the model years 2016 and 2025, whereby 2016 models are representing the state of the art on the European market for the individual application purpose. Vehicle specifications for 2025 are based on a technology assessment of future improvements. For xEV concepts the it is at the moment not possible to identify typical vehicle configurations as the these systems are currently a new technology under development for HDV. As a consequence xEV vehicle specifications and related results as elaborated in the present study shall been understood as examples for these new technologies. Simulation of vehicles which are driven by an ICE only have been performed with the software Vehicle Energy Consumption Calculation tool (VECTO), the tool which is also used for the CO2 certification of HDV in the EU. Electrified propulsion systems have been simulated with the model PHEM as these propulsion concepts are not covered in the current VECTO version. Figure 1 and Figure 2 give a summary on the results on transport specific figures (i.e. per tonne-kilometre) for energy consumption and TTW CO2-equivalent emissions. The main conclusions on the comparison of different propulsion systems drawn from these results are given in chapter 7 of this report.JRC.C.2-Energy Efficiency and Renewable

Leitfaden für Städte und Gemeinden zu Remote Sensing Messungen von Fahrzeugemissionen

LEITFADEN FÜR STÄDTE UND GEMEINDEN ZU REMOTE SENSING MESSUNGEN VON FAHRZEUGEMISSIONEN Leitfaden für Städte und Gemeinden zu Remote Sensing Messungen von Fahrzeugemissionen / Wappelhorst, Sandra (Rights reserved) ( -

Distance-based emission factors from vehicle emission remote sensing measurements

Vehicle emission remote sensing has the potential to provide detailed emissions information at a highly disaggregated level owing to the ability to measure thousands of vehicles in a single day. Fundamentally, vehicle emission remote sensing provides a direct measure of the molar volume ratio of a pollutant to carbon dioxide, from which fuel-based emissions factors can readily be calculated. However, vehicle emissions are more commonly expressed in emission per unit distance travelled e.g. grams per km or mile. To express vehicle emission remote sensing data in this way requires an estimate of the fuel consumption at the time of the emission measurement. In this paper, an approach is developed based on vehicle specific power that uses commonly measured or easily obtainable vehicle information such as vehicle speed, acceleration and mass. We test the approach against 55 independent comprehensive PEMS measurements for Euro 5 and 6 gasoline and diesel vehicles over a wide range of driving conditions and find good agreement between the method and PEMS data. The method is applied to individual vehicle model types to quantify distance-based emission factors. The method will be appropriate for application to larger vehicle emission remote sensing databases, thus extending real-world distance-based vehicle emissions information

Emission optimized control for isolated intersections

Stopping and accelerating at traffic lights is one of the main contributing factors to vehicular emissions in urban environments. The work in this paper demonstrates a generic guideline for minimizing CO2 emissions at traffic lights. This was done using an adaptive control, which uses a cost function for optimization, rather than network-specific control parameters. A new version of the emission model PHEMlight was used, which added of a fuel cut-off mode during coasting and other improvements compared to the previous version. Using this model, it could be determined that the emission optimal ratio between delay time and stops for the cost function of an adaptive control should be 1:4.8. Using this ratio a reduction of 7.6 % of CO2 emissions was achieved compared to a vehicle actuated control

Second Generation of Pollutant Emission Models for SUMO

Traffic puts a high burden on the environment in means of emitted pollutants and consumed fuel. Different attempts exist for reducing these impacts, ranging from traffic management actions to in-vehicle ITS solutions. When equipped with a model for vehicular pollutant emissions, microscopic traffic simulations are assumed to be helpful in predicting the performance of such approaches. We report about the implementation of a second generation of pollutant emission models

COLOMBO: Investigating the Potential of V2X for Traffic Management Purposes assuming low penetration Rates

Vorstellung des COLOMBO-Systems zur Reduktion der Emissionen im Straßenverkehr unter Verwendung von Fahrzeug-Fahrzeug- und Fahrzeug-Infrastruktur-Kommunikation bei geringen Ausrüstungsraten

Emission optimized control and speed limit for isolated intersections

Stopping and accelerating at traffic lights is one of the main contributing factors to vehicular emissions in urban environments. The work in this paper demonstrates a generic guideline for minimizing CO2 emissions at traffic lights. This was done using an adaptive control, which uses a cost function for optimization, rather than network-specific control parameters. A new version of the emission model PHEMlight was used, which added of a fuel cut-off mode during coasting and other improvements compared to the previous version. Using this model, it could be determined that the emission optimal ratio between delay time and stops for the cost function of an adaptive control should be 1:165 at 50km/h. When the speed limit increases the ratio also increases with 1:296 for 70km/h. However, it was also found that the optimal free flow travel speed was around 70km/h. Therefore with long distances between intersections and a low amount of average stops, the maximum speed should be higher than 50km/h for emission optimality. Application of the ratio for 50km/h to the adaptive control algorithm ImFlow resulted in a CO2 emission reduction of 6.3% compared to a vehicle actuated control