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The latent heat-of-vaporization (HoV) of blends of biofuel and hydrocarbon components into gasolines has recently experienced expanded interest because of the potential for increased HoV to increase fuel knock resistance in direct-injection (DI) engines. Several studies have been conducted, with some studies identifying an additional anti-knock benefit from HoV and others failing to arrive at the same conclusion. Consideration of these studies holistically shows that they can be grouped according to the level of fuel octane sensitivity variation within their fuel matrices. When comparing fuels of different octane sensitivity significant additional anti-knock benefits associated with HoV are sometimes observed. Studies that fix the octane sensitivity find that HoV does not produce additional anti-knock benefit. New studies were performed at ORNL and NREL to further investigate the relationship between HoV and octane sensitivity.

Numerous studies have demonstrated that exhaust gas recirculation (EGR) can attenuate knock propensity in spark ignition (SI) engines at naturally aspirated or lightly boosted conditions [1]. In this study, we investigate the role of cooled EGR under higher load conditions with multiple fuel compositions, where highly retarded combustion phasing typical of modern SI engines was used. It was found that under these conditions, EGR attenuation of knock is greatly reduced, where EGR doesn’t allow significant combustion phasing advance as it does under lighter load conditions. Detailed combustion analysis shows that when EGR is added, the polytropic coefficient increases causing the compressive pressure and temperature to increase. At sufficiently highly boosted conditions, the increase in polytropic coefficient and additional trapped mass from EGR can sufficiently reduce fuel ignition delay to overcome knock attenuation effects.

Accurately predicting the fuel savings that can be achieved with the implementation of various technologies developed for fuel efficiency can be very challenging, particularly when considering combinations of technologies. Differences in the usage of highway vehicles can strongly influence the benefits realized with any given technology, which makes generalizations about fuel savings inappropriate for different vehicle applications. A model has been developed to estimate the potential for reducing fuel consumption when advanced efficiency technologies, or combinations of these technologies, are employed on highway vehicles, particularly medium- and heavy-duty trucks. The approach is based on a tractive energy analysis applied to drive cycles representative of the vehicle usage, and the analysis specifically accounts for individual energy loss factors that characterize the technologies of interest.

Gasoline direct injection (GDI) engines can offer improved fuel economy and higher performance over their port fuel-injected (PFI) counterparts, and are now appearing in increasingly more U.S. and European vehicles. Small displacement, turbocharged GDI engines are replacing large displacement engines, particularly in light-duty trucks and sport utility vehicles, in order for manufacturers to meet more stringent fuel economy standards. GDI engines typically emit the most particulate matter (PM) during periods of rich operation such as start-up and acceleration, and emissions of air toxics are also more likely during this condition. A 2.0 L GDI engine was operated at lambda of 0.91 at typical loads for acceleration (2600 rpm, 8 bar BMEP) on three different fuels; an 87 anti-knock index (AKI) gasoline (E0), 30% ethanol blended with the 87 AKI fuel (E30), and 48% isobutanol blended with the 87 AKI fuel.

Ethanol is a very attractive fuel from an end-use perspective because it has a high chemical octane number and a high latent heat of vaporization. When an engine is optimized to take advantage of these fuel properties, both efficiency and power can be increased through higher compression ratio, direct fuel injection, higher levels of boost, and a reduced need for enrichment to mitigate knock or protect the engine and aftertreatment system from overheating. The ASTM D5798 specification for high level ethanol blends, commonly called “E85,” underwent a major revision in 2011. The minimum ethanol content was revised downward from 68 vol% to 51 vol%, which combined with the use of low octane blending streams such as natural gasoline introduces the possibility of a lower octane “E85” fuel.

Today’s transportation is quickly transforming with the nascent advent of connectivity, automation, shared-mobility, and electrification. These technologies will not only affect our safety and mobility, but also our energy consumption, and environment. As a result, it is of unprecedented importance to understand the overall system impacts due to the introduction of these emerging technologies and concepts. Existing modeling tools are not able to effectively capture the implications of these technologies, not to mention accurately and reliably evaluating their effectiveness with a reasonable scope. To address these gaps, a dynamometer-in-the-loop (DiL) development and testing approach is proposed which integrates test vehicle(s), chassis dynamometer, and high fidelity traffic simulation tools, in order to achieve a balance between the model accuracy and scalability of environmental analysis for the next generation of transportation systems.

Ethanol offers significant potential for increasing the compression ratio of SI engines resulting from its high octane number and high latent heat of vaporization. A study was conducted to determine the knock-limited compression ratio of ethanol-gasoline blends to identify the potential for improved operating efficiency. To operate an SI engine in a flex fuel vehicle requires operating strategies that allow operation on a broad range of fuels from gasoline to E85. Since gasoline or low ethanol blend operation is inherently limited by knock at high loads, strategies must be identified which allow operation on these fuels with minimal fuel economy or power density tradeoffs. A single-cylinder direct-injection spark-ignited engine with fully variable hydraulic valve actuation (HVA) is operated at WOT and other high-load conditions to determine the knock-limited compression ratio (CR) of ethanol fuel blends. The geometric CR is varied by changing pistons, producing CR from 9.2 to 12.87.

Fuel cell-powered electric vehicles (FCPEV) require an energy storage device to start up the fuel cells and to store the energy captured during regenerative braking. Low-voltage (12 V) batteries are preferred as the storage device to maintain compatibility with the majority of today's automobile loads. A dc/dc converter is therefore needed to interface the low-voltage batteries with the fuel cell-powered higher-voltage dc bus system (255 V ∼ 425 V), transferring energy in either direction as required. This paper presents a soft-switched, isolated bi-directional dc/dc converter developed at Oak Ridge National Laboratory for FCPEV applications. The converter employs dual half-bridges interconnected with an isolation transformer to minimize the number of switching devices and their associated gate drive requirements. Snubber capacitors including the parasitic capacitance of the switching devices and the transformer leakage inductance are utilized to achieve zero-voltage switching (ZVS).

Downsized turbocharged engines have been increasingly popular in modern light-duty vehicles due to their fuel efficiency benefits. However, high power density in such engines is achieved thanks to high in-cylinder pressure and temperature conditions that increase knock propensity. Next-cycle control has been studied as a method to reduce the damaging effects of knock by operating the engine in a low knock probability condition. This exploratory study looks at the feasibility of in-cycle knock prediction as a tool for advanced knock control algorithms. A methodology is proposed to 1) choose in-cycle features of the pressure trace that highly correlate with knock events and 2) train artificial neural networks to predict in-cycle knock events before knock onset. The methodology was validated at different operating conditions and different levels of generalization. Precision and recall were used as metrics to evaluate the binary classifier.

Low-temperature heat release (LTHR) in spark-ignited internal combustion engines is a critical step toward the occurrence of auto-ignition, which can lead to an undesirable phenomenon known as engine knock. Hence, correct predictions of LTHR are of utmost importance to improve the understanding of knock and enable techniques aimed at controlling it. While LTHR is typically obscured by the deflagration following the spark ignition, extremely late ignition timings can lead to LTHR occurrence prior to the spark, i.e., pre-spark heat release (PSHR). In this research, PSHR in a boosted direct-injection SI engine was numerically investigated using three-dimensional computational fluid dynamics (CFD). A hybrid approach was used, based on the G-equation model for representing the turbulent flame front and the multi-zone well-stirred reactor model for tracking the chemical reactions within the unburnt region.

Driver is a key component in vehicle simulation. An ideal driver model simulates driving patterns a human driver may perform to negotiate road profiles. There are simulation packages having the capability to simulate driver behavior. However, it is rarely documented how they work with road profiles. This paper proposes a new truck driver model for vehicle simulation to imitate actual driving behavior in negotiating road grade and curvature. The proposed model is developed based upon Gipps' car-following model. Road grade and curvature were not considered in the original Gipps' model although it is based directly on driver behavior and expectancy for vehicles in a stream of traffic. New parameters are introduced to capture drivers' choice of desired speeds that they intend to use in order to negotiating road grade and curvature simultaneously. With the new parameters, the proposed model can emulate behaviors like uphill preparation for different truck drivers.

This work explores the impact of the interaction of lubricant and fuel properties on the propensity for stochastic pre-ignition (SPI). Findings are based on statistically significant changes in SPI tendency and magnitude, as determined by measurements of cylinder pressure. Specifically, lubricant detergents, lubricant volatility, fuel volatility, fuel chemical composition, fuel-wall impingement, and engine load were varied to study the physical and chemical effects of fuel-lubricant interactions on SPI tendency. The work illustrates that at low loads, with fuels susceptible to SPI events, lubricant detergent package effects on SPI were non-significant. However, with changes to fuel distillation, fuel-wall impingement, and most importantly engine load, lubricant detergent effects could be observed even at reduced loads This suggests that there is a thermal effect associated with the higher load operation.

Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency.

The widespread adoption of boosted, downsized SI engines has brought pre-ignition phenomena into greater focus, as the knock events resulting from pre-ignitions can cause significant hardware damage. Much attention has been given to understanding the causes of pre-ignition and identify lubricant or fuel properties and engine design and calibration considerations that impact its frequency. This helps to shift the pre-ignition limit to higher specific loads and allow further downsizing but does not fundamentally eliminate the problem. Real-time detection and mitigation of pre-ignition would thus be desirable to allow safe engine operation in pre-ignition-prone conditions. This study focuses on advancing the time of detection of pre-ignition in an engine cycle where it occurs.

Prior research studies have investigated a wide variety of gasoline compression ignition (GCI) injection strategies and the resulting fuel stratification levels to maintain control over the combustion phasing, duration, and heat release rate. Previous GCI research at the US Department of Energy’s Oak Ridge National Laboratory has shown that for a combustion mode with a low degree of fuel stratification, called “partial fuel stratification” (PFS), gasoline range fuels with anti-knock index values in the range of regular-grade gasoline (~87 anti-knock index or higher) provides very little controllability over the timing of combustion without significant boost pressures. On the contrary, heavy fuel stratification (HFS) provides control over combustion phasing but has challenges achieving low temperature combustion operation, which has the benefits of low NOX and soot emissions, because of the air handling burdens associated with the required high exhaust gas recirculation rates.

In prior work, the EGR loop catalytic reforming strategy developed by ORNL has been shown to provide a relative brake engine efficiency increase of more than 6% by minimizing the thermodynamic expense of the reforming processes, and in some cases achieving thermochemical recuperation (TCR), a form of waste heat recovery where waste heat is converted to usable chemical energy. In doing so, the EGR dilution limit was extended beyond 35% under stoichiometric conditions. In this investigation, a Microlith®-based metal-supported reforming catalyst (developed by Precision Combustion, Inc. (PCI)) was used to reform the parent fuel in a thermodynamically efficient manner into products rich in H2 and CO. We were able to expand the speed and load ranges relative to previous investigations: from 1,500 to 2,500 rpm, and from 2 to 14 bar break mean effective pressure (BMEP).

1Increasing adoption of downsized, boosted, spark-ignition engines has improved vehicle fuel economy, and continued improvement is desirable to reduce carbon emissions in the near-term. However, this strategy is limited by damaging preignition events which can cause hardware failure. Research to date has shed light on various contributing factors related to fuel and lubricant properties as well as calibration strategies, but the causal factors behind an individual preignition cycle remain elusive. If actionable precursors could be identified, mitigation through active control strategies would be possible. This paper uses artificial neural networks to search for identifiable precursors in the cylinder pressure data from a large real-world data set containing many preignition cycles. It is found that while follow-up preignition cycles in clusters can be readily predicted, the initial preignition cycle is not predictable based on features of the cylinder pressure.

This work explores the dependence of fuel ignition delay on stochastic pre-ignition (SPI). Findings are based on bulk gas thermodynamic state, where the effects of kinetically controlled bulk gas pre-spark heat release (PSHR) are correlated to SPI tendency and magnitude. Specifically, residual gas and low temperature PSHR chemistry effects and observations are explored, which are found to be indicative of bulk gas conditions required for strong SPI events. Analyzed events range from non-knocking SPI to knocking SPI and even detonation SPI events in excess of 325 bar peak cylinder pressure. The work illustrates that singular SPI event count and magnitude are found to be proportional to PSHR of the bulk gas mixture and residual gas fraction. Cycle-to-cycle variability in trapped residual mass and temperature are found to impose variability in singular SPI event count and magnitude.

Gasoline knock resistance is characterized by the Research and Motor Octane Number (RON and MON), which are rated on the CFR octane rating engine at naturally aspirated conditions. However, modern automotive downsized boosted spark ignition (SI) engines generally operate at higher cylinder pressures and lower temperatures relative to the RON and MON tests. Using the naturally aspirated RON and MON ratings, the octane index (OI) characterizes the knock resistance of gasolines under boosted operation by linearly extrapolating into boosted “beyond RON” conditions via RON, MON, and a linear regression K factor. Using OI solely based on naturally aspirated RON and MON tests to extrapolate into boosted conditions can lead to significant errors in predicting boosted knock resistance between gasolines due to non-linear changes in autoignition and knocking characteristics with increasing pressure conditions.