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A real-world fleet test of the effects of engine oil on Low Speed Pre-Ignition occurrence in TGDi engine
ISSN: 0148-7191, e-ISSN: 2688-3627
Published December 19, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
In the last decade, numerous studies have been conducted to investigate the mechanism of Low Speed Pre-Ignition (LSPI) in Turbocharged Gasoline Direct Injection (TGDi) engines. According to technical reports, engine oil formulations can significantly influence the occurrence of LSPI particularly when higher levels of calcium-based additives are used, increasing the tendency for LSPI events to occur. While most of the studies conducted to date utilized engine tests, this paper evaluates the effect of engine oil formulations on LSPI under real-world driving conditions, so that not only the oil is naturally aged within an oil change interval, but also the vehicle is aged through total test distance of 160,000 km. Three engine oil formulations were prepared, and each tested in three vehicles leading to an identical fleet totaling nine vehicles, all of which were equipped with the same TGDi engine. In this work an LSPI event recording system was developed and deployed on each of the nine vehicles to record the occurrence of LSPI events. In addition, a high-speed data logger was used to continuously monitor vehicle operational data including the air/fuel ratio and other key engine factors, such as engine speed and intake manifold absolute pressure. The findings from this study have shown that LSPI does happen in the field and engine oil formulations impact the number of LSPI events whilst operating in uncontrolled real-world conditions, further confirming that the right engine oil formulation is a critical component for protecting TGDi engines.
CitationZhang, R., Howard, K., and Browne, D., "A real-world fleet test of the effects of engine oil on Low Speed Pre-Ignition occurrence in TGDi engine," SAE Technical Paper 2019-01-2294, 2019, https://doi.org/10.4271/2019-01-2294.
Data Sets - Support Documents
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