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  • Summary & Details

An Analysis of Modern Vehicle Road Loads for Fleetwide Energy Consumption Modelling

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Author(E)1) Dimitrios Komnos, 2) Stefanos Tsiakmakis PhD, 3) Jelica Pavlovic, 4) Andrés Laverde Marín lng, 5) Anatoli Chatzipanagi lng, 6) Georgios Fontaras
Affiliation(E)1) FINCONS group, 2) Ideas Forward PC, 3) European Commission Joint Research, 4) FINCONS group, 5) 0, 6) European Commission Joint Research
Abstract(E)Passenger and light-duty vehicles have a high, and steadily increasing, greenhouse gas emissions footprint. Industry and regulators put effort into new, efficient propulsion configurations to reduce carbon dioxide (CO2) emissions from the transport sector. Energy savings are highly impacted not only by the driving style and needs of the driver, but also by the energy mix used during a trip, making the vehicle efficiency benchmarking increasingly complex. A potential way to curb the vehicle energy demand is by minimising the losses due to factors opposing the forward movement, such as vehicle inertia, tyre deformation, drivetrain, and vehicle air-drag. These losses are included in the vehicle road loads. In the present study, we derive representative road load values by employing open access vehicle information and combining physical and statistical methods. These values are then compared to the ones declared by the manufacturer, which are derived by physical coast down tests. Since the impact of the road loads depends on the trip characteristics, the accuracy of the modelling methods is presented in a set of real-world driving conditions for both the cycle energy demand (CED) and CO2 emissions. The CED error inserted is limited, averaging at 2%. The dispersion is higher in low average speeds and lower in high speeds. This trend results from the individual parameter error diffusion across different speeds and its contribution to the CED. For an average trip speed of up to 50 km/h, tyre rolling resistance is the primary energy consumer with a share of more than 40-60%; above 50 km/h, the air drag is the dominant contributor, accounting for up to 75% of the losses. The error introduced in CO2 is limited, averaging at 1.5%.

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