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Diesel low temperature combustion (LTC) is an operational strategy that is effective at reducing soot and oxides of Nitrogen (NOx) emissions at low engine loads in-cylinder. A downside to LTC in diesel engines is increased hydrocarbon (HC) emissions. This study shows that semi-volatile species from LTC form the bulk of particulate matter (PM) upon dilution in the atmosphere. The nature of gas-to-particle conversion from high HC operating modes like LTC has not been well characterized. In this work, we explore engine-out PM and HC emissions from LTC and conventional diffusion combustion (CC) operation for two different engine load and speed modes using a modern light-duty diesel engine. An experimental method to investigate PM volatility was implemented. Raw exhaust was diluted under two dilution conditions. A tandem differential mobility analyzer (TDMA) was used to identify differences in volatility between particle sizes.

Diesel low temperature combustion (LTC) is an operational strategy that effectively limits soot and oxides of nitrogen (NOx) emissions in-cylinder. Unfortunately, LTC results in increased hydrocarbon emissions as compared to conventional diesel combustion (CDC). Previous work has shown that exhaust conditions resulting from LTC inhibit oxidation of HC within a diesel oxidation catalyst (DOC). Further, these elevated HC emissions result in engine-out particulate matter (PM) that primarily consists of semi-volatile organic material. The current work shows that a DOC incompletely oxidizes this PM forming material. These results investigated the effectiveness of both a DOC and a diesel particulate filter (DPF) in reducing particle emissions for LTC. In this work, engine-out, DOC-out, and DPF-out exhaust were sampled using a micro-dilution system. Particle distributions were determined with a scanning mobility particle sizer (SMPS) and engine exhaust particle sizer (EEPS).

Negative valve overlap (NVO) is a viable control strategy that enables low-temperature gasoline combustion (LTGC) at low loads. Thermal effects of NVO fueling on main combustion are well understood, but fuel reforming chemistry during NVO has not been extensively studied. The objective of this work is to analyze the impact of global equivalence ratio and available oxidizer on NVO product concentrations. Experiments were performed in a LTGC single-cylinder engine under a sweep of NVO oxygen concentration and NVO fueling rates. Gas sampling at the start and end of the NVO period was performed via a custom dump-valve apparatus with detailed sample speciation by gas chromatography. Single-zone reactor models using detailed chemistry at relevant mixing and thermodynamic conditions were used in parallel to the experiments to evaluate expected yields of partially oxidized species under representative engine time scales.

On-board diagnostics (OBD) data contain valuable information including real-world measurements of vehicle powertrain parameters. These data can be used to gain a richer data-driven understanding of complex physical phenomena like emissions formation during combustion. In this study, we develop a physics-based machine learning framework to predict and analyze trends in engine-out NOx emissions from diesel and diesel-hybrid heavy-duty vehicles. This model differs from black-box machine learning models presented in previous literature because it incorporates engine combustion parameters that allow physical interpretation of the results. Based on chemical kinetics and the characteristics of diffusive combustion, NOx emissions from compression ignition engines primarily depend non-linearly on three parameters: adiabatic flame temperature, the oxygen concentration in the cylinder when the intake valves are closed, and combustion time duration.

Optimization-based (OB) methods used in vehicle energy management strategies (EMSs) have the potential to significantly increase fuel economy and extend the electric-only range of plug-in hybrid electric vehicles (PHEVs). However, OB methods are difficult to apply to current real-world vehicles because accurate detailed and high-resolution information about the future, including second-by-second vehicle velocity trajectory data, are not currently available in the current transportation infrastructure. In this paper, a practical reinforcement learning (RL) algorithm for automatic mode-switching of a multimode PHEV is introduced. The PHEV used in the work was a 2016 Chevrolet Volt driven on a simulated commuter route. The goal is to blend the charge depleting and charge sustaining modes during the trip to reduce gasoline consumption and extend electric-only range.

A central challenge for efficient auto-ignition controlled low-temperature gasoline combustion (LTGC) engines has been achieving the combustion phasing needed to reach stable performance over a wide operating regime. The negative valve overlap (NVO) strategy has been explored as a way to improve combustion stability through a combination of charge heating and altered reactivity via a recompression stroke with a pilot fuel injection. The study objective was to analyze the thermal and chemical effects on NVO-period energy recovery. The analysis leveraged experimental gas sampling results obtained from a single-cylinder LTGC engine along with cylinder pressure measurements and custom data reduction methods used to estimate period thermodynamic properties. The engine was fueled by either iso-octane or ethanol, and operated under sweeps of NVO-period oxygen concentration, injection timing, and fueling rate.

Negative Valve Overlap (NVO) is a potential control strategy for enabling Low-Temperature Gasoline Combustion (LTGC) at low loads. While the thermal effects of NVO fueling on main combustion are well-understood, the chemical effects of NVO in-cylinder fuel reforming have not been extensively studied. The objective of this work is to examine the effects of fuel molecular structure on NVO fuel reforming using gas sampling and detailed speciation by gas chromatography. Engine gas samples were collected from a single-cylinder research engine at the end of the NVO period using a custom dump-valve apparatus. Six fuel components were studied at two injection timings: (1) iso-octane, (2) n-heptane, (3) ethanol, (4) 1-hexene, (5) cyclohexane, and (6) toluene. All fuel components were studied neat except for toluene - toluene was blended with 18.9% nheptane by liquid volume to increase the fuel reactivity.

To ensure reliable starting under cold weather conditions (< 0 oC ambient), gasoline engines use fuel enrichment, leading to higher soot formation and greater tailpipe particle number (PN) emissions. In gasoline direct injection (GDI) engines, PN emissions are higher due to liquid fuel impingement on cold surfaces of the combustion chamber and piston. This study characterizes solid (mostly elemental carbon) and semi-volatile (organic) particle number, mass, and size distributions during cold-cold engine start-up from light duty vehicles. Particle emissions were sampled from vehicles upon engine start-up after an overnight soak, with an average ambient temperature of -8 ± 7 oC. The average PN emitted during 180 seconds by GDI and PFI vehicles were 3.09E+13 and 2.12E+13 particles respectively.

The effects of combining premixed, low temperature combustion (LTC) with biodiesel are relatively unknown to this point. This mode allows simultaneously low soot and NOx emissions by using high rates of EGR and increasing ignition delay. This paper compares engine performance and emissions of neat, soy-based methyl ester biodiesel (B100), B20, B50, pure ultra low sulfur diesel (ULSD) and a Swedish, low aromatic diesel in a multi-cylinder diesel engine operating in a late-injection premixed LTC mode. Using heat release analysis, the progression of LTC combustion was explored by comparing fuel mass fraction burned. B100 had a comparatively long ignition delay compared with Swedish diesel when measured by start of ignition (SOI) to 10% fuel mass fraction burned (CA10). Differences were not as apparent when measured by SOI to start of combustion (SOC) even though their cetane numbers are comparable.

Premixed low temperature combustion (LTC) in diesel engines simultaneously reduces soot and NOx at the expense of increased hydrocarbon (HC) and CO emissions. The use of biodiesel in the LTC regime has been shown to produce lower HC emissions than petroleum diesel; however, unburned methyl esters from biodiesel are more susceptible to particulate matter (PM) formation following atmospheric dilution due to their low volatility. In this study, the efficacy of a production-type diesel oxidation catalyst (DOC) for the conversion of light hydrocarbons species and heavier, semi-volatile species like those in unburned fuel is examined. Experimental data were taken from a high speed direct-injection diesel engine operating in a mid-load, late injection partially premixed LTC mode on ultra-low sulfur diesel (ULSD) and neat soy-based biodiesel (B100). Gaseous emissions were recorded using a conventional suite of analyzers and individual light HCs were measured using an FT-IR analyzer.

Cost and robustness are key factors in the design of diesel engines for low power density applications. Although compression ignition engines can produce very high power density output with turbocharging, naturally aspirated (NA) engines have advantages in terms of reduced cost and avoidance of system complexity. This work explores the use of direct injection (DI) and exhaust gas recirculation (EGR) in NA engines using experimental data from a single-cylinder research diesel engine. The engine was operated with a fixed atmospheric intake manifold pressure over a map of speed, air-to-fuel ratio, EGR, fuel injection pressure and injection timing. Conventional gaseous engine-out emissions were measured along with high speed cylinder pressure data to show the load limits and resulting emissions of the NA-DI engine studied. Well known reductions in NOX with increasing levels of EGR were confirmed with a corresponding loss in peak power output.

Extended range electric vehicles (EREVs) are a potential solution for fossil fuel usage mitigation and on-road emissions reduction. The use of EREVs can be shown to yield significant fuel economy improvements when proper energy management strategies (EMSs) are employed. However, many in-use EREVs achieve only moderate fuel reduction compared to conventional vehicles due to the fact that their EMS is far from optimal. This paper focuses on in-use rule-based EMSs to improve the fuel efficiency of EREV last-mile delivery vehicles equipped with two-way Vehicle-to-Could (V2C) connectivity. The method uses previous vehicle data collected on actual delivery routes and machine learning methods to improve the fuel economy of future routes. The paper first introduces the main challenges of the project, such as inherent uncertainty in human driver behavior and in the roadway environment. Then, the framework of our practical physics-model guided data-driven approach is introduced.

Fuel reforming during a Negative Valve Overlap (NVO) period is an effective approach to control Low Temperature Gasoline Combustion (LTGC) ignition. Previous work has shown through experiments that primary reference fuels reform easily and produce several species that drastically affect ignition characteristics. However, our previous research has been unable to accurately predict measured reformate composition at the end of the NVO period using simple single-zone models. In this work, we use a stochastic reactor model (SRM) closed cycle engine simulation to predict reformate composition accounting for in-cylinder temperature and mixture stratification. The SRM model is less computationally intensive than CFD simulations while still allowing the use of large chemical mechanisms to predict intermediate species formation rates.

Partially premixed low temperature combustion (LTC) in diesel engines is a strategy for reducing soot and NOX formation, though it is accompanied by higher unburned hydrocarbon (UHC) emissions compared to conventional mixing-controlled diesel combustion. In this work, two independent methods of quantifying light UHC species from a diesel engine operating in early LTC (ELTC) modes were compared: Fourier transform infrared (FT-IR) spectroscopy and gas chromatography-mass spectroscopy (GC-MS). A sampling system was designed to capture and transfer exhaust samples for off-line GC-MS analysis, while the FT-IR sampled and quantified engine exhaust in real time. Three different ELTC modes with varying levels of exhaust gas recirculation (EGR) were implemented on a modern light-duty diesel engine. GC-MS and FT-IR concentrations were within 10 % for C2H2, C2H4, C2H6, and C2H4O. While C3H8 was identified and quantified by the FT-IR, it was not detected by the GCMS.

Many dual fuel technologies have been proposed for diesel engines. Implementing dual fuel modes can lead to emissions reductions or increased efficiency through using partially premixed combustion and fuel reactivity control. All dual fuel systems have the practical disadvantage that a secondary fuel storage and delivery system must be included. Reforming the primary diesel to a less reactive vaporized fuel on-board has potential to overcome this key disadvantage. Most previous research regarding on-board fuel reforming has been focused on producing significant quantities of hydrogen. However, only partially reforming the primary fuel is sufficient to vaporize and create a less volatile fuel that can be fumigated into an engine intake. At lower conversion efficiency and higher equivalence ratio, reforming reactors retain higher percentage of the inlet fuel’s heating value thus allowing for greater overall engine system efficiency.

In this work, engine-out particle mass (PM) and particle number (PN) emissions were experimentally examined from a gasoline direct injection (GDI) engine operating in two lean combustion modes and one stoichiometric mode with a fuel of known properties. Ten steady state operating points, two constant speed load steps, and an engine cold start were examined. Results showed that solid particles emitted from the engine under steady state stoichiometric conditions had a uniquely broad size distribution that was relatively flat between the diameters of 10 and 100 nm. In most operating conditions, lean homogenous modes can achieve lower particle emissions than stoichiometric modes while improving engine thermal efficiency. Alternatively, lean stratified operating modes resulted in significantly higher PN and PM emissions than both lean homogeneous and stoichiometric modes with increased efficiency only at low engine load.

It is advantageous to increase the specific power output of diesel engines and to operate them at higher load for a greater portion of a driving cycle to achieve better thermal efficiency and thus reduce vehicle fuel consumption. Such operation is limited by excessive smoke formation at retarded injection timing and high rates of cylinder pressure rise at more advanced timing. Given this window of operation, it is desired to understand the influence of fuel properties such that optimum combustion performance and emissions can be retained over the range of fuels commonly available in the marketplace. Data are examined from a direct-injection single-cylinder research engine for eight common diesel fuels including soy-based biodiesel blends at two high load operating points with no exhaust gas recirculation (EGR) and at a moderate load with four levels of EGR.

A key challenge for the practical introduction of dual-fuel reactivity controlled compression ignition (RCCI) combustion modes in diesel engines is the requirement to store two fuels on-board. This work demonstrates that partially reforming diesel fuel into less reactive products is a promising method to allow RCCI to be implemented with a single stored fuel. Experiments were conducted using a thermally integrated reforming reactor in a reformed exhaust gas recirculation (R-EGR) configuration to achieve RCCI combustion using a light-duty diesel engine. The engine was operated at a low engine load and two reformed fuel percentages over ranges of exhaust gas recirculation (EGR) rate and main diesel fuel injection timing. Results show that RCCI-like emissions of NOx and soot were achieved load using the R-EGR configuration. It was also shown that complete fuel conversion in the reforming reactor is not necessary to achieve sufficiently low fuel reactivity for RCCI combustion.

Engine-out particle size distributions and soot mass emissions were measured from a gasoline direct injection (GDI) engine fueled by seven different gasoline formulations. Additionally, particle size distributions were simultaneously measured downstream of a catalyzed gasoline particulate filter (GPF) to determine the size resolved filtration efficiency. Stoichiometric, lean homogeneous, and lean stratified combustion modes were studied at four steady-state engine conditions. The particulate matter (PM) Index was calculated for each fuel as a function of the double bond equivalent and vapor pressure of the fuel components. There was generally poor correlation between particle number (PN)/PM mass emissions and the PM Index for steady state stoichiometric conditions with clean injectors, which emitted low particle concentrations.

The impact of compression ratio on engine efficiency is well known. A plethora of mechanical concepts have been proposed for altering engine compression ratio in real time. Some of these, like free-piston configurations or complex crank-slider mechanisms have the added ability to alter piston trajectory along with compression ratio. This secondary modality raises the question: Is there a more optimal piston position versus crank-angle profile for spark-ignition (SI) engines than the near-sinusoidal motion produced by a traditional four-bar crank-slider mechanism? Very little published literature directly addresses this question. This work presents the results of a quasi-dimensional SI engine model using piston trajectory as an input. Specific trajectory traits including increased dwell at top dead center and asymmetric compression and expansion strokes were swept. The trajectory also was optimized using a single objective genetic algorithm with 60 individuals and 40 generations.