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Particle Number Emissions Evaluation for Conventional SI, Low-Pressure Loop EGR, and D-EGR Combustion Strategies
Technical Paper
2021-01-0485
ISSN: 0148-7191, e-ISSN: 2688-3627
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SAE WCX Digital Summit
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English
Abstract
The size and distribution of a vehicle’s tailpipe particulate emissions can have a strong impact on human health, especially if the particles are small enough to enter the human respiratory system. Gasoline direct injection (GDI) has been adopted widely to meet stringent fuel economy and CO2 regulations across the globe for recent engine architectures. However, the introduction of GDI has led to challenges concerning the particulate matter (PM) and particle number (PN) emissions from such engines.
This study aimed to compare the particulate emissions of three SI combustion strategies: conventional SI, conventional stoichiometric low-pressure exhaust gas recirculation (LP-EGR), and Dedicated-EGR (D-EGR) at four specific test conditions. It was shown that the engine-out PM/PN for both the EGR strategies was lower than the conventional SI combustion under normal operating conditions. The test conditions were chosen to represent the WLTC test conditions. It was also observed that the particle number measurements were not repeatable due to their high sensitivity to engine inlet conditions. However, the PN measurements were comparable to the engine-out AVL micro soot sensor measurements, confirming that the variation observed in the particle number was a strong function of minor changes in engine operating conditions. Data trends were compared for the three strategies with all strategies being run successively with no engine idle time in between. Furthermore, the impact of injection strategy, the start of injection, and the injection pressure on engine-out PM/PN emissions were also evaluated.
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Kalaskar, V., Gukelberger, R., Conway, G., Premnath, V. et al., "Particle Number Emissions Evaluation for Conventional SI, Low-Pressure Loop EGR, and D-EGR Combustion Strategies," SAE Technical Paper 2021-01-0485, 2021, https://doi.org/10.4271/2021-01-0485.Data Sets - Support Documents
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