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Title: Filter-based control of particulate matter from a lean gasoline direct injection engine

Abstract

New regulations requiring increases in vehicle fuel economy are challenging automotive manufacturers to identify fuel-efficient engines for future vehicles. Lean gasoline direct injection (GDI) engines offer significant increases in fuel efficiency over the more common stoichiometric GDI engines already in the marketplace. However, particulate matter (PM) emissions from lean GDI engines, particularly during stratified combustion modes, are problematic for lean GDI technology to meet U.S. Environmental Protection Agency Tier 3 and other future emission regulations. As such, the control of lean GDI PM with wall-flow filters, referred to as gasoline particulate filter (GPF) technology, is of interest. Since lean GDI PM chemistry and morphology differ from diesel PM (where more filtration experience exists), the functionality of GPFs needs to be studied to determine the operating conditions suitable for efficient PM removal. In addition, lean GDI engine exhaust temperatures are generally higher than diesel engines which results in more continuous regeneration of the GPF and less presence of the soot cake layer common to diesel particulate filters. Since the soot layer improves filtration efficiency, this distinction is important to consider. Research on the emission control of PM from a lean GDI engine with a GPF was conducted on an engine dynamometer.more » PM, after dilution, was characterized with membrane filters, organic vs. elemental carbon characterization, and size distribution techniques at various steady state engine speed and load points. The engine was operated in three primary combustion modes: stoichiometric, lean homogeneous, and lean stratified. In addition, rich combustion was utilized to simulate PM from engine operation during active regeneration of lean NOx control technologies. High (>95%) PM filtration efficiencies were observed over a wide range of conditions; however, some PM was observed to slip through the GPF at high speed and load conditions. The PM characterization at various engine speeds and loads will help enable optimized GPF design and control to achieve more fuel efficient lean GDI vehicles with low PM emissions.« less

Authors:
 [1];  [1];  [1];  [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Fuels, Engines and Emissions Research Center (FEERC); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). National Transportation Research Center (NTRC)
Sponsoring Org.:
Work for Others (WFO)
OSTI Identifier:
1248777
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: SAE Congress 2016, Detroit, MI, USA, 20160412, 20160414
Country of Publication:
United States
Language:
English

Citation Formats

Parks, II, James E, Lewis, Sr, Samuel Arthur, DeBusk, Melanie Moses, Prikhodko, Vitaly Y, and Storey, John Morse. Filter-based control of particulate matter from a lean gasoline direct injection engine. United States: N. p., 2016. Web.
Parks, II, James E, Lewis, Sr, Samuel Arthur, DeBusk, Melanie Moses, Prikhodko, Vitaly Y, & Storey, John Morse. Filter-based control of particulate matter from a lean gasoline direct injection engine. United States.
Parks, II, James E, Lewis, Sr, Samuel Arthur, DeBusk, Melanie Moses, Prikhodko, Vitaly Y, and Storey, John Morse. 2016. "Filter-based control of particulate matter from a lean gasoline direct injection engine". United States.
@article{osti_1248777,
title = {Filter-based control of particulate matter from a lean gasoline direct injection engine},
author = {Parks, II, James E and Lewis, Sr, Samuel Arthur and DeBusk, Melanie Moses and Prikhodko, Vitaly Y and Storey, John Morse},
abstractNote = {New regulations requiring increases in vehicle fuel economy are challenging automotive manufacturers to identify fuel-efficient engines for future vehicles. Lean gasoline direct injection (GDI) engines offer significant increases in fuel efficiency over the more common stoichiometric GDI engines already in the marketplace. However, particulate matter (PM) emissions from lean GDI engines, particularly during stratified combustion modes, are problematic for lean GDI technology to meet U.S. Environmental Protection Agency Tier 3 and other future emission regulations. As such, the control of lean GDI PM with wall-flow filters, referred to as gasoline particulate filter (GPF) technology, is of interest. Since lean GDI PM chemistry and morphology differ from diesel PM (where more filtration experience exists), the functionality of GPFs needs to be studied to determine the operating conditions suitable for efficient PM removal. In addition, lean GDI engine exhaust temperatures are generally higher than diesel engines which results in more continuous regeneration of the GPF and less presence of the soot cake layer common to diesel particulate filters. Since the soot layer improves filtration efficiency, this distinction is important to consider. Research on the emission control of PM from a lean GDI engine with a GPF was conducted on an engine dynamometer. PM, after dilution, was characterized with membrane filters, organic vs. elemental carbon characterization, and size distribution techniques at various steady state engine speed and load points. The engine was operated in three primary combustion modes: stoichiometric, lean homogeneous, and lean stratified. In addition, rich combustion was utilized to simulate PM from engine operation during active regeneration of lean NOx control technologies. High (>95%) PM filtration efficiencies were observed over a wide range of conditions; however, some PM was observed to slip through the GPF at high speed and load conditions. The PM characterization at various engine speeds and loads will help enable optimized GPF design and control to achieve more fuel efficient lean GDI vehicles with low PM emissions.},
doi = {},
url = {https://www.osti.gov/biblio/1248777}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Jan 01 00:00:00 EST 2016},
month = {Fri Jan 01 00:00:00 EST 2016}
}

Conference:
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