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Calibration of automotive engines to ensure compliance with emission regulations is a critical phase in product development. Control of engine-out particulate emissions, which directly impact the environment and public health, is particularly important. Detailed physics-based models are typically used to gain a rich understanding of the complex physical phenomena that drive the soot particle formation in an engine cylinder. However, such models often fail to correctly represent the highly dynamic nature of the underlying mechanisms under transient combustion conditions. Moreover, most physics-based models were initially developed for diesel engine applications and their applicability to gasoline engines remains questionable due to differences in flame structure and fuel-wall interactions. Black-box models have been previously proposed to predict engine-out soot emissions, but their lack of physical interpretability is an unsolved drawback.

As demand for consumer electric vehicles (EVs) has drastically increased in recent years, manufacturers have been working to bring heavy-duty EVs to market to compete with Class 6-8 diesel-powered trucks. Many high-profile companies have committed to begin electrifying their fleet operations, but have yet to implement EVs at scale due to their limited range, long charging times, sparse charging infrastructure, and lack of data from in-use operation. Thus far, EVs have been disproportionately implemented by larger fleets with more resources. To aid fleet operators, it is imperative to develop tools to evaluate the electrification potential of heavy-duty fleets. However, commercially available tools, designed mostly for light-duty vehicles, are inadequate for making electrification recommendations tailored to a fleet of heavy-duty vehicles.

In cold climates, cells in the high voltage battery of an electric vehicle are subject to environment-related performance degradation leading to a decrease in effective range. Active battery temperature regulation is often implemented in battery electric vehicles (BEVs) to mitigate the detrimental effects of extreme ambient temperatures on battery state of health and effective nominal capacity. However, low ambient temperature also impacts driver comfort leading to added auxiliary power demands to regulate the cabin temperature. This work focuses on evaluating the increased auxiliary power demand from vehicle heating, ventilation, and air condition (HVAC) systems in cold climates. Practical driving data was periodically collected from an instrumented medium-duty delivery vehicle over several cold winter months in Minnesota, USA. A simplified empirical model to estimate HVAC power requirements was developed from relevant temperature and air speed measurements within the vehicle.

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.

This paper presents the results from experiments and Computational Fluid Dynamics (CFD) simulations performed to understand the impact of piston geometry on ignition delay for Dimethyl Ether (DME)/air mixtures inside a Rapid Compression Machine (RCM). Three piston shapes and two dilution ratios are studied using CFD simulations validated by experiments. The three piston geometries under consideration are: a flat piston, a piston with an enlarged crevice, and a bowl piston. Key phenomena analyzed in the study include fluid flow patterns, heat transfer, temperature homogeneity of the mixture, and ignition delay. The CFD model provides reasonable predictions of ignition delay when compared with experimental data. Simulations indicate that flat and bowl pistons show similar heat transfer, ignition delay, and combustion characteristics, while the enlarged creviced piston shows lower peak temperatures and a cooler mixture core due to higher wall heat transfer.

To achieve full automation in self-driving vehicles, environmental perception sensing accuracy is critically important. However, ambient particles in adverse weather like foggy, rainy, or snowy conditions can significantly scatter the incident laser beam, and therefore contaminate the intensity and accuracy of light detection and ranging (LiDAR) sensors. Especially compared to the rapidity of technology development in self-driving vehicles, there is a significant lack of documented research on LiDAR systems with wavelength longer than 1 μm for application in Advanced Driver-Assistance Systems. In this work, experimental studies were performed with a state-of-the-art 1.55 μm wavelength automotive-grade LiDAR system in a controlled laboratory fog chamber. The goal of the research is to correlate laser attenuation and the optical properties of fog particles.

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.

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.

Range extender (REx) engines have promise for providing low-cost energy for future battery electric vehicles. Due to their restricted operation range, REx engines provide an opportunity to implement system-level schemes that are less attractive for engines designed for highly transient operation. This paper explores a thermochemical recuperation (TCR) scheme for a 2-cylinder BMW spark-ignition REx engine using a 1-D model implemented in GT-Power™. The TCR reactor employs a unique catalytic heat exchange configuration that enables efficient transfer of exhaust sensible and chemical enthalpy to steam reform the incoming fuel. The engine model without the TCR reactor was validated using experimental emissions and performance data from a BMW engine operating on a test stand. A custom integrated heat exchanger and catalyst model was created and integrated with the validated engine. A parametric modeling sweep was conducted with iso-octane as fuel over a range of reformed fuel fraction.

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.

A limitation currently facing internal combustion engine research and development is the lack of a direct method to accurately measure in-cylinder temperature. The rate at which an engine cycle evolves is too rapid for conventional, direct measurement transducers such as thermocouples or thermistors. This paper presents the experimental results of a novel method for determining time-resolved in-cylinder temperature using ultrasonic thermometry. The technique involved sampling an ultrasonic signal reflected from the top of the moving piston and measuring piston position using an optical encoder connected to the engine crankshaft. The known flight distance and measured time of flight (ToF) was used to determine path-averaged temperature. ToF of the ultrasonic signal was precisely determined using an unscented Kalman filtering technique. Experiments were conducted using a motored (non-combusting) engine without compression at two engine speeds and three known intake temperatures.

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.

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.

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-cylinder reforming of injected fuel during an auxiliary negative valve overlap (NVO) period can be used to optimize main-cycle auto-ignition phasing for low-load Low-Temperature Gasoline Combustion (LTGC), where highly dilute mixtures can lead to poor combustion stability. When mixed with fresh intake charge and fuel, these reformate streams can alter overall charge reactivity characteristics. The central issue remains large parasitic heat losses from the retention and compression of hot exhaust gases along with modest pumping losses that result from mixing hot NVO-period gases with the cooler intake charge. Accurate determination of total cycle energy utilization is complicated by the fact that NVO-period retained fuel energy is consumed during the subsequent main combustion period. For the present study, a full-cycle energy analysis was performed for a single-cylinder research engine undergoing LTGC with varying NVO auxiliary fueling rates and injection timing.

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 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.