
A Review and Perspective on Particulate Matter Indices Linking Fuel Composition to Particulate Emissions from Gasoline Engines
- Felix Leach - University of Oxford, UK ,
- Elana Chapman - General Motors LLC, USA ,
- Jeff J. Jetter - Honda R&D Americas LLC, USA ,
- Lauretta Rubino - Opel Automobile GmbH, Germany ,
- Earl D. Christensen - National Renewable Energy Laboratory, USA ,
- Peter C. St. John - National Renewable Energy Laboratory, USA ,
- Gina M. Fioroni - National Renewable Energy Laboratory, USA ,
- Robert L. McCormick - National Renewable Energy Laboratory, USA
Journal Article
04-15-01-0001
ISSN: 1946-3952, e-ISSN: 1946-3960
Sector:
Citation:
Leach, F., Chapman, E., Jetter, J., Rubino, L. et al., "A Review and Perspective on Particulate Matter Indices Linking Fuel Composition to Particulate Emissions from Gasoline Engines," SAE Int. J. Fuels Lubr. 15(1):3-28, 2022, https://doi.org/10.4271/04-15-01-0001.
Language:
English
Abstract:
Particulate matter (PM) indices—those linking PM emissions from gasoline engines
to the composition and properties of the fuel—have been a topic of significant
study over the last decade. It has long been known that fuel composition has a
significant impact on particulate emissions from gasoline engines. Since
gasoline direct injection (GDI) engines have become the market-leading
technology, this has become more significant because the evaporative behavior of
fuel increases in importance. Several PM indices have been developed to provide
metrics describing this behavior and correlating PM emissions. In this article,
16 different PM indices are identified and collected—to the authors’ knowledge,
all of the indices are available at the time of writing. The indices are
reviewed and discussed in the context of the information required to calculate
them, as well as their utility. The authors believe that there is a need for
indices that provide both a detailed and robust correlation, as well as those
that are less sophisticated yet sufficient for specific use cases. Future
research is suggested to guide the technical community toward improvements in
the indices’ methods and equations for both high and low fidelity and high and
low time investment.