Pre- and post-catalyst-, fuel-, velocity- and acceleration-dependent benzene emission data of gasoline-driven EURO-2 passenger cars and light duty vehicles

A comprehensive modal emissions model for light-duty cars and trucks is being developed under the sponsorship of NCHRP Project 25-11. Model development has been described previously for vehicles operating under stoichiometric and enrichment conditions. A modal emissions model is presented for vehicles operated under enleanment conditions. Enleanment typically occurs with sharp deceleration or load reduction events, and sometimes during long deceleration. Under enleanment conditions, the air/fuel ratio is lean and incomplete combustion or misfire occurs. Preliminary research indicates that enleanment emissions (particularly for hydrocarbons) contribute significantly to a vehicle’s overall emissions. An enleanment emissions module has been developed on the basis of second-by-second emission measurements generated at the College of Engineering—Center for Environmental Research and Technology’s vehicle testing facility using the Federal Test Procedure, US06, and a specially designed modal emission cycle (MEC01). On the basis of more than 200 vehicles tested and modeled, lean-burn hydrocarbon emissions (HClean) account for 10 to 20 percent of the overall HC emissions under the various test cycles. HClean emission contributions vary greatly from vehicle to vehicle, ranging from near 0 to more than 30 percent of total HC emissions of individual vehicles. After detailed analysis of the second-by-second emission data over the modal emission cycle MECO1, it was found that enleanment hydrocarbons emissions are mostly associated with rapid load reduction events and long deceleration events. The former is most likely to cause extremely high levels of HC as short spikes, and the latter is mostly associated with longer-lasting HC puffs. A methodology has been developed to characterize and model enleanment hydrocarbons emissions associated with these two events. The model estimates are compared with measurements, with encouraging results.

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Analysis of the influence of RDE test data processing methods on the emission results of China 6 light duty vehicles

E3S Web of Conferences ◽

10.1051/e3sconf/202126801022 ◽

2021 ◽

Vol 268 ◽

pp. 01022

Author(s):

Zhihong Wang ◽

Penghui Wu ◽

Nenghui Yu ◽

Yuanjun Zhang ◽

Zhijun Wang

Keyword(s):

Data Processing ◽

Test Data ◽

Emission Factor ◽

Vehicle Emission ◽

Emission Data ◽

Processing Methods ◽

Light Duty ◽

Light Duty Vehicle ◽

Light Duty Vehicles ◽

Data Processing Methods

The CO2 moving average window(MAW) method is used to process RDE (real drive emissions) emissions data in China 6 light duty vehicle emissions regulations, while the Euro 6 light duty vehicle emission regulations allow to use both of MAW and power binning(PB) method to deal with RDE emission data. In order to study the difference between the two data processing methods and analyze the differences in the emission results, 10 different types of light duty vehicles are conducted RDE test with PEMS (portable emissions measurement system), and the test data are processed by the two methods separately. The results show that there is a little difference between MAW and PB, while both of them can satisfy the vehicle emission assessment. The PB method calculates the emission factors higher than the MAW method. After removing the cold start and idle condition data, the results of PB is similar to MAW. Besides, reducing the average speed limit of urban working conditions in PB has a greater impact on the urban driving condition emission factor, but less on the whole cycle emission factor.

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