Modeling and Parameterization Study of Fuel Consumption and Emissions for Light Commercial Vehicles

Paper #:
  • 2014-01-2020

Published:
  • 2014-03-24
DOI:
  • 10.4271/2014-01-2020
Citation:
Lee, J., Park, J., Lim, Y., Oh, Y. et al., "Modeling and Parameterization Study of Fuel Consumption and Emissions for Light Commercial Vehicles," SAE Technical Paper 2014-01-2020, 2014, doi:10.4271/2014-01-2020.
Pages:
6
Abstract:
This paper describes the effects of diverse driving modes and vehicle component characteristics impact on fuel efficiency and emissions of light commercial vehicles. The AVL's vehicle and powertrain system level simulation tool (CRUISE) was adopted in this study. The main input data such as the fuel consumption & emission map were based on the experimental value and vehicle components characteristic data (full load characteristic curves, gear shifting position curves, torque conversion curve etc.) and basic specifications (gross weight, gear ratio, tire radius etc.) were used based on the database or suggested value.The test database for two diesel vehicles adopted whether prediction accuracy of simulation data were converged in acceptable range. These data had been acquired from the portable emission measurement system, the exhaust emission and operating conditions (engine speed, vehicle speed, pedal position etc.) were acquired at each time step. The fuel consumption rate was derived from carbon balance method. The 3 types of test driving modes were selected to verify the correlations between the simulation and experiment results. These modes contain city driving and expressway driving modes, it is expected that almost all vehicle operating ranges were covered.It is revealed that most of suggested default module data offered in CRUISE did not significant impact on prediction accuracy. However, the characteristic of the torque converter data had high impact on prediction results. The predicted fuel efficiency errors were converged in 3.5 percent regardless of driving mode by changing the torque converter data whereas origin model shows the over 10 percent differences in specific driving modes. In case of vehicle1, the emission prediction simulations were also conducted based on the emission map data. The predicted total CO2 emission which is closely related to the fuel consumption rate shows the good agreement with test results. The predicted NOx emission also shows the similar trends with test results but some discrepancies were exist. Through this processes, the vehicle dynamics model adopted in this study was sufficiently shows the high prediction accuracy and it was concluded that this model useful to further parametric study which specifications shows the great influenced on the vehicle performance. Parametric study was performed by changing the parameters at specific percentages. The priority of main factor impact on fuel efficiency were slightly changed depending on the driving mode and vehicle type, it was revealed that the gross weight, rolling resistance and the drag force have a potential possibility impact on the fuel efficiency about 1 to 3 percent.
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