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  • Summary & Details

A Simplified CO2 and Fuel Consumption Model for Buses Derived from VECTO Simulations

Detailed Information

Author(E)1) Stijn Broekaert, 2) Evangelos Bitsanis, 3) Georgios Fontaras
Affiliation(E)1) European Commission Joint Research, 2) European Commission Joint Research, 3) European Commission Joint Research
Abstract(E)New heavy-duty vehicles are simulated with the Vehicle Energy Consumption Calculation Tool (VECTO) to certify their fuel consumption and CO2 emissions in the European Union. The vehicle manufacturer runs the simulation tool and requires the vehicle components' characteristics to simulate the vehicle over standardized mission profiles. The detailed component characteristics required to run the tool are not always publicly available. In this work, a simplified model was developed to predict the fuel consumption and CO2 emissions of heavy buses over the VECTO mission profiles. It requires only the basic vehicle properties as input, such as the air drag, tire rolling resistance, mass, drivetrain efficiency and auxiliary power use. The model was derived from detailed VECTO simulations of numerous variants of a diesel high floor bus, a diesel low floor bus and a CNG low floor bus. The simulation results were used to analyze the energy use and CO2 emissions over the different mission profiles. If the driving cycle contains fewer high-speed sections and more start-stop driving, the vehicle's air drag and rolling resistances have a lesser contribution to the overall energy use, in favour of the auxiliary power use and energy lost by braking. The proposed model can capture the general trend of the CO2 emissions well, with an average model error of less than 0.04% and a standard deviation of less than 0.6% for each mission profile. The average model error is less than 0.01%, considering all vehicle variants and mission profiles, and the standard deviation is less than 0.4%. The model can be applied to all bus subgroups and vehicles operated with different fuels or with a different engine efficiency by appropriately scaling the model coefficients.

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