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Stochastic Preignition (SPI) is an abnormal combustion phenomenon for internal combustion engines (ICE), which has been a significant impact to automotive companies developing high efficiency, turbocharged, direct fuel injection, spark ignited engines. It is becoming clearer what fuel properties are related to the cause of SPI, whether directly with fuel preparation in the cylinder, or mechanisms related to the deposit build-up which contributes to initial and follow-on SPI events. The purpose of this paper is to provide a summary of global market gasoline fuel properties with special attention given to properties and specific compounds from the fuel and fuel additives that can contribute to SPI and the deposit build-up in engines. Based on a review of the global fuel quality, it appears that the fuel quality has not caught up to meet the technology requirements for fuel economy from modern technology engines.

With the implementation of the China VI gasoline standards, the use of ethanol is expanding and is intended to further reduce vehicle criteria gaseous and particulate emissions. However, due to the constraints of biomass ethanol production, petroleum derived Methyl tert-Butyl Ether (MTBE) is being used in addition to ethanol to improve fuel octane and maintain oxygen content. The impact of these mixed oxygenates on vehicle emissions are studied in this paper. The correlation of fuel characteristics to vehicle particulate emissions and their predictive indices has been investigated. The results from this study suggest some alternatives to existing fuel indices due to oxygenate contribution. Additionally, this paper studies, the emissions of three direct-injection turbocharged vehicle models focusing on particulate emissions from the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). The test fuels were China VIb gasoline, blended with different ratios of ethanol and MTBE.

A gasoline’s chemical composition impacts a vehicle’s sooting tendency and therefore has been the subject of numerous emissions studies. From these studies, several mathematical correlation equations have been developed to predict a gasoline’s sooting tendency in modern spark-ignited internal combustion engine vehicles. This paper reviews the recently developed predictive tool methods and summarizes five years of global market fuel survey data to characterize gasoline sooting tendency trends around the world. Additionally, the paper will evaluate and suggest changes to the predictive methods to improve emissions correlations.

A gasoline’s distillation profile is directly related to its hydrocarbon composition and the volatility (boiling points) of those hydrocarbons. Generally, the volatility profiles of U.S. market fuels are characterized using a very simple, low theoretical plate distillation separation, detailed in the ASTM D86 test method. Because of the physical chemistry properties of some compounds in gasoline, this simple still or retort distillation has some limitations: separating azeotropes, isomers, and heavier hydrocarbons. Chemists generally rely on chromatographic separations when more detailed and precise results are needed. High-boiling aromatic compounds are the primary source of particulate emissions from spark ignited (SI), internal combustion engines (ICE), hence a detailed understanding and high-resolution separation of these heavy compounds is needed.

There have been tremendous improvements in China fuel quality in recent years in conjunction with newly implemented vehicle emissions standards to combat air pollution. The focus of concern is particulate emissions from gasoline engines especially from high volume gasoline direct inject (GDI) engines, therefore the China 6 (GB 18351.6-2016) emission standard introduces strict PM/PN requirements. Because the fuel and vehicle are an integrated system, the composition of gasoline is one of the factors affecting PM/PN emissions. Ethanol and aromatics are widely used as octane boosters, changing the composition of China’s gasoline pool. In this study, two gasoline oxygenates, ethanol and methyl tert-butyl ether (MTBE), and heavy aromatic hydrocarbons were studied in vehicles with a GDI engines. Vehicle tests were performed on the Worldwide Harmonized Light Vehicles Test Cycle (WLTC).

The Particulate Matter Index (PMI) is a helpful tool which provides an indication of a fuel’s sooting tendency. Currently, the index is being used by various laboratories and OEMs as a metric to understand the gasoline fuels impact on both sooting found on engine hardware and vehicle out emissions. This paper will explore a new method that could be used to give indication of the sooting tendency of the gasoline range fuels, called the Particulate Evaluation Index (PEI), and provide the detailed equation in its initial form. In addition, the PEI will be shown to have a good correlation agreement to PMI. The paper will then give a detailed explanation of the data used to develop it. Initial vehicle PM/PN data will also be presented that shows correlations of the indices to the vehicle response.

Several predictive equations based on the chemical composition of gasoline have been shown to estimate the particulate emissions of light-duty, internal combustion engine (ICE) powered vehicles and are reviewed in this paper. Improvements to one of them, the PEISimDis equation are detailed herein. The PEISimDis predictive equation was developed by General Motor’s researchers in 2022 based on two laboratory gas chromatography (GC) analyses; Simulated Distillation (SimDis), ASTM D7096 and Detailed Hydrocarbon Analysis (DHA), ASTM D6730. The DHA method is a gas chromatography mass spectroscopy (GC/MS) methodology and provides the detailed speciation of the hundreds of hydrocarbon species within gasoline. A DHA’s aromatic species from carbon group seven through ten plus (C7 – C10+) can be used to calculate a Particulate Evaluation Index (PEI) of a gasoline, however this technique takes many hours to derive because of its long chromatography analysis time.

With increasing use of ethanol in automotive fuel in recent years, which can be made from renewable feedstocks, the chemical composition of gasoline is changed. The compositional change results in many changes in fuel properties. One key property is the octane rating of gasoline. Market data has shown the shifts of octane rating (antiknock index or AKI) upward due to more penetration of E10 gasoline in the US market. However, the increase in research octane is more pronounced as compared to motor octane, therefore the increase in octane sensitivity in gasoline. Refineries have used the change in octane due to ethanol contribution by sending so called sub-grade gasoline to terminals expecting the final blend after mixing with ethanol to meet the market requirement in octane. Thus the octane rating of the final blend will largely depend on the sub-grade gasoline composition and ethanol.

The impact of gasoline composition on vehicle particulate emissions response has been widely investigated and documented. Correlation equations between fuel composition and particulate emissions have also been documented, e.g. Particulate Matter Index (PMI) and Particulate Evaluation Index (PEI). Vehicle PM/PN emissions correlate very well with these indices. In a previous paper, global assessment with PEI on fuel sooting tendency was presented [1]. This paper will continue the previous theme by the authors, and cover China gasoline in more detail. With air pollution an increasing concern, along with more stringent emission requirements in China, both OEMs and oil industries are facing new challenges. Emissions controls require a systematic approach on both fuels and vehicles. Chinese production vehicle particulate emissions for a range of PEI fuels are also presented.

Fuel chemistry plays a crucial role in the continued reduction of particulate emissions (PE) and cleaner air quality from vehicles and equipment powered by internal combustion engines (ICE). Over the past ten years, there have been great improvements in predictive particulate emissions indices (correlative mathematical models) based on the fuel’s composition. Examples of these particulate indices (PI) are the Honda Particulate Matter Index (PMI) and the General Motors Particulate Evaluation Index (PEI). However, the analytical chemistry lab methods used to generate data for these two PI indices are very time-consuming. Because gasoline can be mixtures of hundreds of hydrocarbon compounds, these lab methods typically include the use of the high resolution chromatographic separation techniques such as detailed hydrocarbon analysis (DHA), with 100m chromatography columns and long (3 - 4 hours) analysis times per sample.