Prediction of the fuel consumption of heavy goods vehicles by computer simulation.
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Prediction of the fuel consumption of heavy goods vehicles by computer simulation. by M. A. Renouf

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Published by Transport and Road Research Laboratory, Transport Systems Department, Transport Engineering Division in Crowthorne .
Written in English


Book details:

Edition Notes

SeriesSupplementary reports / Transport and Road Research Laboratory -- 453
ID Numbers
Open LibraryOL13921505M

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Fuel consumption prediction of fleet vehicles using Machine Learning: A comparative study Conference Paper April with Reads How we measure 'reads'. Fuel consumption of mining dump trucks accounts for about 30% of total energy use in surface mines. Moreover, a fleet of large dump trucks is the main source of greenhouse gas (GHG) generation. Modeling and prediction of fuel consumption per cycle is a valuable tool in assessing both energy costs and the resulting GHG by: Visit to get more information about this book, to buy it in print, or to download it as a free PDF. Liquid fuel consumption by medium- and heavy-duty vehicles (MHDVs) represents 26 percent of all U.S. liquid transportation fuels consumed and has increased more rapidly—in both. simulation is becoming an increasingly important tool for regulators. Vehicle simulation software can be used for the prediction of fuel consumption and CO 2 emissions from HDVs under various operating conditions, as long as sufficiently detailed models are provided and the necessary input data and parameters are Size: KB.

C. Predictions of Annual Fuel Consumption Using the model, equation (1), the total fuel consumed, TFCON. for each of the years of interest can be computed from the vehicle distribution matrix, VMIX^. given in Table 4, the vehicle miles traveled, MIT;. given in . The committee has sought to update and summarize key information for these vehicles. Table , “Comparison of Light-Duty Vehicles with Medium- and Heavy-Duty Vehicles,” presents the committee’s compilation of data for and It highlights weights, sales volumes and registrations, fuel economy, fuel consumption, mileage, and other information across the various vehicle classes. It can be seen in Fig. 9 that ED versus fuel consumption was predicted as a linear relationship where with a CC increase in ED, the fuel consumption rate increases with %. FE and FC are metrics used to determine the fuel efficiency of vehicles. FC is defined by the amount of fuel consumed while. traveling a certain distance and is usually expressed in gallons per miles (or liters per km) [6]. FE is the distance traveled per. Quantitative Effects of Vehicle Parameters on Fuel Consumption for Heavy-Duty.

The paper concerns field measurements for validation of the simulation method VEMOSIM developed in Finland. The field measurements and their analyses were carried out in the following order: Quantification of; - drive resistance coefficients by coasting; and - power train losses by acceleration; - Utilization of parameters quantified in computer simulation of fuel consumption; - Based on the Cited by: 1. Fuel Economy Simulation for the Vehicle Fleet Forecasting the fuel consumption of an entire vehicle fleet has become a crucial challenge for all car manufacturers. Over the past few years, simulation of the entire vehicle has become an essential means of making precise predictions of the effectiveness of fuel economy measures at BMW.   In this study, correlation between vehicle fuel efficiency and total fuel energy consumption is analyzed to support the energy consumption and greenhouse gas (GHG) emissions reduction master plan in Korea. The background and highlights of recently amended fuel economy regulations and fuel efficiency labeling standards in Korea are also introduced. 18 representative vehicle groups, classified Cited by: 4. Evaluation of Fuel Consumption Potential of Medium and Heavy Duty Vehicles through Modeling and Simulation. Report to National Academy of Sciences. Fifth Street NW. Washington DC Octo Contract Number: DEPS-BEES Prepared by. Antoine Delorme, Dominik Karbowski, Ram Vijayagopal, Phillip Sharer.