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A numerical analysis was performed to study the variation of the laminar burning speed of gasoline-ethanol blend, pressure, temperature and dilution using the one-dimensional premixed flame code CHEMKIN™. A semi-detailed validated chemical kinetic model (142 species and 672 reactions) for a gasoline surrogate fuel was used. The pure components in the surrogate fuel consist of n-heptane, isooctane and toluene. The ethanol mole fraction was varied from 0 to 85 percent, initial pressure from 4 to 8 bar, initial temperature from 300 to 600K, and the EGR dilution from 0 to 32% to represent the in-cylinder conditions of a spark-ignition engine. The laminar flame speed is found to increase with ethanol concentration and temperature but decrease with pressure and dilution.

This paper evaluates the potential of adding higher alcohols to gasoline blendstock in an attempt to improve overall fuel performance. The alcohols considered include ethanol, normal- and iso-structures of propanol, butanol and pentanol as well as normal-hexanol (C2-C6). Fuel performance is quantified based on energy content, knock resistance as well as petroleum displacement and promising multi-component blends are systematically identified based on property prediction methods. These promising multi-component blends, as well as their respective reference fuels, are subsequently tested for efficiency and emissions performance utilizing a gasoline direct injection, spark ignition engine. The engine test results confirm that combustion and efficiency of tailored multi-component blends closely match those of the reference fuels. Regulated emissions stemming from combustion of these blends are equal or lower compared to the reference fuels across the tested engine speed and load regime.

A numerical simulation of autoignition of gasoline-ethanol/air mixtures has been performed using the closed homogeneous reactor model in CHEMKIN® to compute the dependence of autoignition time with ethanol concentration, pressure, temperature, dilution, and equivalence ratio. A semi-detailed validated chemical kinetic model with 142 species and 672 reactions for a gasoline surrogate fuel with ethanol has been used. The pure components in the surrogate fuel consisted of n-heptane, isooctane and toluene. The ethanol volume fraction is varied between 0 to 85%, initial pressure is varied between 20 to 60 bar, initial temperature is varied between 800 to 1200K, and the dilution is varied between 0 to 32% at equivalence ratios of 0.5, 1.0 and 1.5 to represent the in-cylinder conditions of a spark-ignition engine. The ignition time is taken to be the point where the rate of change of temperature with respect to time is the largest (temperature inflection point criteria).

This paper presents the development of a Stochastic Knock Detection (SKD) method for combustion knock detection in a spark-ignition engine using a model based design approach. The SKD set consists of a Knock Signal Simulator (KSS) as the plant model for the engine and a Knock Detection Module (KDM). The KSS as the plant model for the engine generates cycle-to-cycle accelerometer knock intensities following a stochastic approach with intensities that are generated using a Monte Carlo method from a lognormal distribution whose parameters have been predetermined from engine tests and dependent upon spark-timing, engine speed and load. The lognormal distribution has been shown to be a good approximation to the distribution of measured knock intensities over a range of engine conditions and spark-timings for multiple engines in previous studies.

Higher carbon number alcohols offer an opportunity to meet the Renewable Fuel Standard (RFS2) and improve the energy content, petroleum displacement, and/or knock resistance of gasoline-alcohol blends from traditional ethanol blends such as E10 while maintaining desired and regulated fuel properties. Part II of this paper builds upon the alcohol selection, fuel implementation scenarios, criteria target values, and property prediction methodologies detailed in Part I. For each scenario, optimization schemes include maximizing energy content, knock resistance, or petroleum displacement. Optimum blend composition is very sensitive to energy content, knock resistance, vapor pressure, and oxygen content criteria target values. Iso-propanol is favored in both scenarios' suitable blends because of its high RON value.

The U.S. Renewable Fuel Standard (RFS2) requires an increase in the use of advanced biofuels up to 36 billion gallons by 2022. Longer chain alcohols, in addition to cellulosic ethanol and synthetic biofuels, could be used to meet this demand while adhering to the RFS2 corn-based ethanol limitation. Higher carbon number alcohols can be utilized to improve the energy content, knock resistance, and/or petroleum displacement of gasoline-alcohol blends compared to traditional ethanol blends such as E10 while maintaining desired and regulated fuel properties. Part I of this paper focuses on the development of scenarios by which to compare higher alcohol fuel blends to traditional ethanol blends. It also details the implementation of fuel property prediction methods adapted from literature. Possible combinations of eight alcohols mixed with a gasoline blendstock were calculated and the properties of the theoretical fuel blends were predicted.

The ability to operate a spark-ignition (SI) engine near the knock limit provides a net reduction of engine fuel consumption. This work presents a real-time knock control system based on stochastic knock detection (SKD) algorithm. The real-time stochastic knock control (SKC) system is developed in MATLAB Simulink, and the SKC software is integrated with the production engine control strategy through ATI's No-Hooks. The SKC system collects the stochastic knock information and estimates the knock level based on the distribution of knock intensities fitting to a log-normal (LN) distribution. A desired knock level reference table is created under various engine speeds and loads, which allows the SKC to adapt to changing engine operating conditions. In SKC system, knock factor (KF) is an indicator of the knock intensity level. The KF is estimated by a weighted discrete FIR filter in real-time.