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The blend of dimethyl ether (DME, CH3OCH3) and propane (C3H8) is a potentially renewable fuel mixture that has the potential to replace diesel in compression ignition engines. The combination can potentially reduce particulate and greenhouse gas emissions compared to a conventional diesel engine operating under similar conditions. However, detailed conceptual and simulation studies must be conducted before adopting a new fuel on a compression ignition engine. For these simulations, accurate chemical kinetic models are necessary. However, the validity of chemical kinetic mechanisms in the literature is unknown for mixing controlled compression ignition (MCCI) engine operating conditions. Hence, in this work, we studied the ignition of dimethyl ether (DME) and propane blends in a shock tube at MCCI engine conditions. Ignition delay time (IDT) data was collected behind the reflected shock for DME-propane mixtures for heavy-duty compression ignition (CI) engine parameters.

Connected and Autonomous Vehicles (CAV) rely on information obtained from sensors and communication to make decisions. In a Cooperative Vehicle Safety (CVS) system, information from remote vehicles (RV) is available at the host vehicle (HV) through the wireless network. Safety applications such as crash warning algorithms use this information to estimate the RV and HV states. However, this information is uncertain and sparse due to communication losses, limitations of communication protocols in high congestion scenarios, and perception errors caused by sensor limitations. In this paper we present a novel approach to improve the robustness of the CVS systems, by proposing an architecture that divide application and information/perception subsystems and a novel prediction method based on non-parametric Bayesian inference to mitigate the detrimental effect of data loss on the performance of safety applications.

The battery is a central part of the vehicle’s electrical system and has to undergo cycling in a wide variety of conditions while providing an acceptable service life. Within a typical distribution chain, automotive lead-acid batteries can sit in storage for months before delivery to the consumer. During storage, batteries are subjected to a wide variety of temperature profiles depending on facility-specific characteristics. Additionally, batteries typically do not receive any type of maintenance charge before delivery. Effects of storage time, temperature, and maintenance charging are explored. Flooded lead-acid batteries were examined immediately after storage and after installation in vehicles subjected to normal drive patterns. While phase composition is a major consideration, additional differences in positive active material (PAM) were observed with respect to storage parameters.

This paper reviews the literature which identifies the causes, consequences and cures for engine knock as it affects high performance engines. The physical events of normal and abnormal combustion are described. The observed variations in combustion phenomenon are explained through chemical kinetics. A mathematical model of combustion which can predict knock in an engine cylinder is summarized. Several mechanisms of knock induced damage are outlined. Design and operating considerations which affect an engine's propensity to knock are discussed. Terms that have become associated with combustion in general and the knocking phenomenon in particular are collected and examined

Co-Optimization of Fuels and Engines initiative (Co-Optima) of the U.S Department of Energy started investigations on several candidates of biofuels and blends for internal combustion engines. At this stage, only a few biomass-derived fuel blendstocks (including ethanol) for advanced spark-ignition engines have been selected using enhanced screening criteria, which included boiling point, toxicity, research octane number, octane sensitivity, and economical distribution system, etc. Ethanol, of which this paper is focused on, is also an important fuel because of its high-octane number which in turn promotes advance ignition timing and higher thermal efficiencies in reciprocating engines. Measurements of laminar burning velocity (LBV) is a key metric to understand fuel performance and applicability in engines. Furthermore, in order to quantify more complicated, and practical, burning regimes such as turbulent combustion much of the underlying theory requires knowledge of LBV.

Laminar burning velocity (LBV) measurements are reported for promising high-performance fuels selected as drop-in transportation fuels to automotive grade gasoline as part of the United States Department of Energy’s Co-Optimization of Fuels and Engines Initiative (Co-Optima). LBV measurements were conducted for ethanol, methyl acetate, and 2-methylfuran with synthetic air (79.0 % N2 and 21.0 % O2 by volume) within a constant-volume spherical combustion rig. Mixture initial temperature was fixed at 428±4 K, with the corresponding initial pressure of 1.00±0.02 atm. Current LBV of ethanol is in good agreement with literature data. LBV of ethanol and 2-methylfuran showed similar values over the range of equivalence ratios, while methyl acetate exhibited an LBV significantly lower over the range of tested equivalence ratios. The maximum laminar burning velocity occurred at slightly richer equivalence ratio from the stoichiometric value for all fuels tested.

The biofuel and engine co-development framework was initiated at Sandia National Labs. Here, the synthetic biologists develop and engineer a new platform for drop-in fuel production from lignocellulosic biomass, using several endophytic fungi. Hence this process has the potential advantage that expensive pretreatment and fuel refining stages can be optimized thereby allowing scalability and cost reduction; two major considerations for widespread biofuel utilization. Large concentrations of ketones along with other volatile organic compounds were produced by fungi grown over switchgrass media. The combustion and emission properties of these new large ketones are poorly known.

Engine dynamometer testing of homogeneous charge, spark ignition lean burn engines fueled by natural gas, hydrogen/natural gas blends and neat hydrogen was conducted to determine if NOx emissions from blended fuel operation can be reduced below those generated from natural gas operation, approaching those due to a 100% hydrogen fueled engine. The preliminary tests were conducted at the University of Central Florida/Florida Solar Energy Center on an eight cylinder automotive engine. The results indicate that the hydrogen/natural gas fuel has the potential of meeting highly restrictive NOx levels. Sandia National Laboratories conducted follow-on, comparative tests using a single cylinder research engine. The Sandia results indicate that the proposed CARB EZEV standard for NOx can be met without exhaust gas aftertreatment using a 30% hydrogen (by volume) / 70% natural gas blend fuel in a constant speed/power, hybrid vehicle application which achieves 60 MPG gasoline equivalent efficiency.

Calibrating the engine control unit to satisfy pollutant and performance objectives can be a challenging task. Due to the large number of variables and their interactive complexities, many firms apply design of experiment methods and modeling techniques to the acquired test data. This establishes a “black box” or “gray box” simulation model that predicts power and emissions as a function of the engine parameters. An offline optimization procedure on the fitted model(s) will identify the engine control strategy that best satisfies pollutant and performance objectives. A review of the literature reveals that the General Linear Modeling method and Neural Network modeling architectures are widely used in the development of “black box” or “gray box” simulation models. While Neural Network methods are “assumption free”, the General Linear Model method is limited to those problems in which the errors, ε, are normally distributed and have constant variance, σ2.

Optimizing IC engine performance currently requires an exhaustive experimental search to determine the combination of internal components that maximizes torque or power. An alternate and more structured approach using Response Surface Methods will lead the experimenter to the optimum combination with the least number of trials. Using simulation software to evaluate IC engine configurations, this method improved the estimated power from 439 to 516 KW. Results of the study indicate that Response Surface Methods are a viable and robust method of converging to an IC engine configuration which achieves optimum performance.

The application of Statistical Process Control and Design of Experiment methods in the research laboratory can lead to significant gains in the Powertrain development process. Empirical methods such as Design of Experiments, Regression, and Neural Network techniques can be applied to help researchers gain better understanding of the cause and effect relationships of emission, alternative fuel source, performance, fuel economy, and engine management system - calibration studies. The use of these empirical modeling techniques along with model based Genetic Algorithm, Gradient, or Constraint based solution search methods will help identify the “process settings” that improve fuel economy, improve performance, and reduce pollutants. Since empirical methods are fundamentally based on the acquired test data, it is vitally important that the laboratory measurements are repeatable, consistent, and void of sources of variance that have a significant effect on the acquired test data.

The 2,4-dimethyl-3-pentanone (DIPK) is a promising biofuel candidate for automotive applications that is produced by the endophytic fungal conversion process which can be optimized for widespread utilization. There are some studies in the literature on combustion properties of DIPK, such as ignition delay times and laminar burning velocity (LBV) measurements. However, most studies are conducted one atmospheric (atm) pressure which are far away from the high-pressure conditions present inside reciprocating engines. Therefore, we present LBV measurements at high pressures up to 10 atm for this fuel using a spherical flame speed facility. It is known that the flame in a constant volume chamber develops cellular structure (hydrodynamic instability) as the initial pressure increases because of the reduction in flame thickness. In addition, the diffusional-thermal instability prevents experiments for rich mixtures because of the reduction of Lewis number (Le).

A gasoline engine with an electronically controlled fuel injection system has substantially better fuel economy and lower emissions than a carburetted engine. In general, the stability of engine operation is improved with fuel injector, but the stability of engine operation at idle is not improved compared with a carburetted gasoline engine. In addition, the increase in time that an engine is at idle due to traffic congestion has an effect on the engine stability and vehicle reliability. Therefore, in this research, we will study the influence of fuel injection timing, spark timing, dwell angle, and air-fuel ratio on engine stability at idle.

The efficiency in energy consumption of an electric vehicle (EV) has significant value to both vehicle manufacturers and vehicle owners. Such efficiency will directly impact the cost of energy and vehicle range while relieving the stringent requirements on the DC motor and battery specs. Nowadays, with the development of advanced driver assistance systems (ADAS), such as adaptive cruise control (ACC) or cooperative adaptive cruise control (CACC), drivers enjoy a much safer driving experience. ADAS capabilities in sensory, computing and communication can be leveraged in EVs for the purpose of optimizing energy consumption. This paper introduces an energy-optimized ACC platform, which utilizes a forecast of the speed profile of the host vehicle in a short (few seconds) horizon. Such speed information can be available through ADAS or similar systems. This paper focuses on optimization in longitudinal tracks.