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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.

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.

An engine-aftertreatment computational model was developed to support in-loop performance simulations of tailpipe emissions and fuel consumption associated with a range of heavy-duty (HD) truck drive cycles. For purposes of this study, the engine-out exhaust dynamics were simulated with a combination of steady-state engine maps and dynamic correction factors that accounted for recent engine operating history. The engine correction factors were approximated as dynamic first-order lags associated with the thermal inertia of the major engine components and the rate at which engine-out exhaust temperature and composition vary as combustion heat is absorbed or lost to the surroundings. The aftertreatment model included catalytic monolith components for diesel exhaust oxidation, particulate filtration, and selective catalytic reduction of nitrogen oxides (NOx) with urea.

It is widely recognized in the automotive industry that, in very cold climatic conditions, the driving range of an Electric Vehicle (EV) can be reduced by 50% or more. In an effort to minimize the EV range penalty, a novel thermal energy storage system has been designed to provide cabin heating in EVs and Plug-in Hybrid Electric Vehicles (PHEVs) by using an advanced phase change material (PCM). This system is known as the Electrical PCM-based Thermal Heating System (ePATHS) [1, 2]. When the EV is connected to the electric grid to charge its traction battery, the ePATHS system is also “charged” with thermal energy. The stored heat is subsequently deployed for cabin comfort heating during driving, for example during commuting to and from work. The ePATHS system, especially the PCM heat exchanger component, has gone through substantial redesign in order to meet functionality and commercialization requirements.

Cabin heating of current electric vehicle (EV) designs is typically provided using electrical energy from the traction battery, since waste heat is not available from an engine as in the case of a conventional automobile. In very cold climatic conditions, the power required for space heating of an EV can be of a similar magnitude to that required for propulsion of the vehicle. As a result, its driving range can be reduced very significantly during the winter season, which limits consumer acceptance of EVs and results in increased battery costs to achieve a minimum range while ensuring comfort to the EV driver. To minimize the range penalty associated with EV cabin heating, a novel climate control system that includes thermal energy storage from an advanced phase change material (PCM) has been designed for use in EVs and plug-in hybrid electric vehicles (PHEVs).

Two hybrid powertrain configurations, including parallel and series hybrids, were simulated for fuel economy, component energy loss, and emissions control in Class 8 trucks over both city and highway driving conditions. A comprehensive set of component models describing engine fuel consumption, emissions control, battery energy, and accessory power demand interactions was developed and integrated with the simulated hybrid trucks to identify heavy-duty (HD) hybrid technology barriers. The results show that series hybrid is absolutely negative for fuel-economy improvement of long-haul trucks due to an efficiency penalty associated with the dual-step conversions of energy (i.e. mechanical to electric to mechanical).

We compare the simulated fuel economy and emissions for both conventional and hybrid class 8 heavy-duty diesel trucks operating over multiple urban and highway driving cycles. Both light and heavy freight loads were considered, and all simulations included full aftertreatment for NOx and particulate emissions controls. The aftertreatment components included a diesel oxidation catalyst (DOC), urea-selective catalytic NOx reduction (SCR), and a catalyzed diesel particulate filter (DPF). Our simulated hybrid powertrain was configured with a pre-transmission parallel drive, with a single electric motor between the clutch and gearbox. A conventional heavy duty (HD) truck with equivalent diesel engine and aftertreatment was also simulated for comparison. Our results indicate that hybridization can significantly increase HD fuel economy and improve emissions control in city driving. However, there is less potential benefit for HD hybrid vehicles during highway driving.

Knowledge of how fuel chemistry and properties affect engine response is necessary for effective engine control. It may also be possible to tailor fuels to specific combustion modes, engine geometries, or for desired outputs to generate lower emissions and/or higher IMEP and efficiency. Fuel chemistry and properties have different effects on engine performance in CI and HCCI combustion. In this study, experiments were performed using a 517cc Hatz single-cylinder diesel engine and the same engine converted to run in HCCI mode, both equipped with advanced combustion analysis equipment. Engine performance results were modeled statistically with respect to fuel properties, operating parameters, and engine type to determine the extent to which fuel characteristics influence engine response, and how the response differs between the two combustion modes. Experiments were performed using 16 fuels: ULSD, 9 FACE diesel fuels, and 6 P20 blends of unprocessed plant oils.

The nine CRC fuels for advanced combustion engines (FACE fuels) have been evaluated in a simple, premixed HCCI engine under varying conditions of fuel rate, air-fuel ratio, and intake temperature. Engine performance was found to vary mainly as a function of combustion phasing as affected by fuel cetane and engine control variables. The data was modeled using statistical techniques involving eigenvector representation of the fuel properties and engine control variables, to define engine response and allow optimization across the fuels for best fuel efficiency. In general, the independent manipulation of intake temperature and air-fuel ratio provided some opportunity for improving combustion efficiency of a specific fuel beyond the direct effect of targeting the optimum combustion phasing of the engine (near 5 CAD ATDC).

We describe novel results of ongoing research at 3DX-RAY Ltd and Oak Ridge National Laboratory using new, commercially available, nondestructive x-ray techniques to make engineering measurements of diesel particulate filters (DPF). Nondestructive x-ray imaging and data-analysis techniques were developed to detect and visualize the small density changes corresponding to the addition of substances such as soot and ash to DPF monoliths. The usefulness of this technique was explored through the analysis of field-aged samples, accelerated-aged samples, and the synthetic addition of ash and soot to clean DPF samples. We demonstrate the ability to visualize and measure flaws in substrates and quantify the distribution of ash and soot within the DPF. We also show that the technology is sensitive enough for evaluations of soot and ash distribution and thermal damage without removing the DPF from its metal casing.

To address the growing need for accurate predictions of combustion phasing and emissions for development of advanced engines, a more accurate definition of model fuels and their associated chemical-kinetics mechanisms are necessary. Wide variations in street fuels require a model-fuel blending methodology to allow simulation of fuel-specific characteristics, such as ignition timing, emissions, and fuel vaporization. We present a surrogate-blending technique that serves as a practical modeling tool for determination of surrogate blends specifically tailored to different real-fuel characteristics, with particular focus on model fuels for gasoline engine simulation. We start from a palette of potential model-fuel components that are based on the characteristic chemical classes present in real fuels. From this palette, components are combined into a surrogate-fuel blend to represent a real fuel with specific fuel properties.

The accelerated ash loading of diesel particulate filters (DPFs) with diesel oxidation catalysts (DOCs) mounted upstream by lube-oil derived products was investigated using a single cylinder diesel engine and fuel blended with 5% lube oil. An ash loading protocol is developed which combines soot loading, active soot regeneration, and periodic shutdowns for filter weighing. Active regeneration is accomplished by exhaust injection of diesel fuel, initiated by a backpressure criteria and providing DPF temperatures up to 700°C. In developing this protocol, five DPFs of various combinations of substrates (cordierite, silicon carbide, and mullite) and washcoats (none, low PGM, and high PGM) are used and evaluated. The initial backpressure and rate of backpressure increase with ash varied with each of the DPFs and ash was observed to have an effect on the active soot light-off temperature for the catalyzed DPFs.

While gasoline surrogate development has progressed in the areas of more complex surrogate mixtures and in kinetic modeling tools and mechanism development, it is generally recognized that further development is still needed. This paper represents a small step in supporting this development by providing comparisons between experimental engine data and surrogate-based kinetic models. In our case, the HCCI engine data comes from a port-injected, single-cylinder research engine with intake-air heating for combustion phasing control. Timing sweeps were run at constant fuel rate for three market gasolines and five surrogate mixtures. Modeling was done using the CHEMKIN software with a gasoline mechanism set containing 1440 species and 6572 reactions. Five pure compounds were selected for the surrogate blends and include iso-octane, n-heptane, toluene, methylcyclohexane, and 1-hexene.

A computational study using multi-dimensional CFD modeling was performed to investigate the effects of physical properties on diesel engine combustion characteristics with bio-diesel fuels. Properties of typical bio-diesel fuels that were either calculated or measured are used in the study and the simulation results are compared with those of conventional diesel fuels. The sensitivity of the computational results to individual physical properties is also investigated, and the results provide information about the desirable characteristics of the blended fuels. The properties considered in the study include liquid density, vapor pressure, surface tension, liquid viscosity, liquid thermal conductivity, liquid specific heat, latent heat, vapor specific heat, vapor diffusion coefficient, vapor viscosity and vapor thermal conductivity. The results show significant effects of the fuel physical properties on ignition delay and burning rates at various engine operating conditions.

The effects of variable compression ratio (CR) and fuel composition on thermal efficiency were investigated in a homogeneous charge compression ignition (HCCI) engine using blends of n-heptane and toluene with research octane numbers (RON) of 0 to 90. Experiments were conducted by performing CR sweeps at multiple intake temperatures using both unthrottled operation, and constant Φ conditions by throttling to compensate for varying air density. It was found that CR is effective at changing and controlling the HCCI combustion phasing midpoint, denoted here as CA 50. Thermal efficiency was a strong function of CA 50, with overly advanced CA 50 leading to efficiency decreases. Increases in CR at a constant CA 50 for a given fuel composition did, in most cases, increase efficiency, but the relationship was weaker than the dependence of efficiency on CA 50.

The effect of biodiesel content on homogeneous charge compression ignition (HCCI) engine performance has been investigated both experimentally and by computer simulation. Combustion experiments were performed in a single cylinder HCCI engine using blends of soy biodiesel in ultra low sulfur diesel, with concentrations ranging from 0 to 50 vol% and equivalence ratios (Φ) from 0.38 to 0.48. Data from the engine tests included combustion analysis and exhaust composition analysis with standard gaseous emissions equipment. The engine utilized a custom port fuel injection strategy to provide highly premixed charges of fuel and air, making it possible to compare the results with single zone chemical kinetics simulations that were performed using CHEMKIN III, with a reaction set including 670 species and over 3000 reactions.

In this research, small, single cylinder engines have been used to simulate larger engines in the areas of aftertreatment, combustion, and fuel formulation effects. The use of small engines reduces overall research cost and allows more rapid experiments to be run. Because component costs are lower, it is also possible to investigate more variations and to sacrifice components for materials characterization and for subsequent experiments. Using small engines in this way is very successful in some cases. In other cases, limitations of the engines influence the results and need to be accounted for in the experimental design and data analysis. Some of the results achieved or limitations found may be of interest to the small engine market, and this paper is offered as a summary of the authors' research in these areas. Research is being conducted in two areas. First, small engines are being used to study the rapid aging and poisoning of exhaust aftertreatment catalysts.

We report experimental observations of cyclic combustion variability during the transition between propagating flame combustion and homogeneous charge compression ignition (HCCI) in a single-cylinder, stoichiometrically fueled, spark-assisted gasoline engine. The level of internal EGR was controlled with variable valve actuation (VVA), and HCCI combustion was achieved at high levels of internal EGR using the VVA system. Spark-ignition was used for conventional combustion and was optionally available during HCCI. The transition region between purely propagating combustion and HCCI was mapped at multiple engine speeds and loads by incrementally adjusting the internal EGR level and capturing data for 2800 sequential cycles. These measurements revealed a complex sequence of high COV, cyclic combustion variations when operating between the propagating flame and HCCI limits.

Phosphorous in diesel exhaust is derived via engine oil consumption from the zinc dialkyldithiophosphate (ZDDP) oil additive used for engine wear control. Phosphorous present in the engine exhaust can react with an exhaust catalyst and cause loss of performance through masking or chemical reaction. The primary effect is loss of light-off or low temperature performance. Although the amount of ZDDP used in lube oil is being reduced, it appears that there may is a minimum level of ZDDP needed for engine durability. One of the ways of reducing the effects of the resulting phosphorous on catalysts might be to alter the chemical state of the phosphorous to a less damaging form or to develop catalysts which are more resistant to phosphorous poisoning. In this study, lube oil containing ZDDP was added at an accelerated rate through a variety of engine pathways to simulate various types of engine wear or oil disposal practices.