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Particle Number Emission Reduction for GDI Engines with Gasoline Particulate Filters
ISSN: 0148-7191, e-ISSN: 2688-3627
Published October 08, 2017 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
In order to meet the challenging CO2 targets beyond 2020 despite keeping high performance engines, Gasoline Direct Injection (GDI) technology usually combined with charged aspiration is expanding in the automotive industry. While providing more efficient powertrains to reduce fuel consumption one side effect of GDI is the increased particle formation during the combustion process. For the first time for GDI from September 2014 there is a Particle Number (PN) limit in EU of 6x10 sup 12 #/km, which will be further reduced by one order of magnitude to 6x10 sup 11 #/km effective from September 2017 to be the same level as applied to Diesel engines. In addition to the PN limit of the certification cycle NEDC further certification of Real Driving Emissions (RDE) including portable PN measurements are under discussion by the European Commission. RDE test procedure requires stable and low emissions in a wide range of engine operations and durable over a distance of 160 000 km. Furthermore, PN limit of 6x10 sup 11 #/km in WLTC cycle plans to be applied to Chinese legislation from 2020. To achieve such stringent targets the ceramic wall-flow Gasoline Particulate Filter (GPF) is under discussion as one potential emission control.
- Takayuki Ogata - NGK Insulators, Ltd.
- Mikio Makino - NGK Insulators, Ltd.
- Takashi Aoki - NGK Insulators, Ltd.
- Takehide Shimoda - NGK Insulators, Ltd.
- Kyohei Kato - NGK Insulators, Ltd.
- Takahiko Nakatani - NGK Insulators, Ltd.
- Koji Nagata - NGK Ceramics
- Claus Dieter Vogt - NGK Europe GmbH
- Yoshitaka Ito - NGK Europe GmbH
- Dominic Thier - NGK Europe GmbH
CitationOgata, T., Makino, M., Aoki, T., Shimoda, T. et al., "Particle Number Emission Reduction for GDI Engines with Gasoline Particulate Filters," SAE Technical Paper 2017-01-2378, 2017, https://doi.org/10.4271/2017-01-2378.
Data Sets - Support Documents
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