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The emerging need of building an efficient Electric Vehicle (EV) charging infrastructure requires the investigation of all aspects of Vehicle-Grid Integration (VGI), including the impact of EV charging on the grid, optimal EV charging control at scale, and communication interoperability. This paper presents a cloud-based simulation and testing platform for the development and Hardware-in-the-Loop (HIL) testing of VGI technologies. Although the HIL testing of a single charging station has been widely performed, the HIL testing of spatially distributed EV charging stations and communication interoperability is limited. To fill this gap, the presented platform is developed that consists of multiple subsystems: a real-time power system simulator (OPAL-RT), ISO 15118 EV Charge Scheduler System (EVCSS), and a Smart Energy Plaza (SEP) with various types of charging stations, solar panels, and energy storage systems.

The study showcases the strength of machine learning (ML) models in imitating the operation of an advanced engine concept - the gasoline compression ignition (GCI) - at low loads. The GCI engine is prone to exceeding the limits of criteria emissions at such loads, especially at the cold start when the catalyst is not activated. One proposition to accelerate catalyst light-off is using spark-ignition. This, however, adds an extra level of complexity in identifying an optimum operation point. The ML models can be a useful tool in guiding the engine calibration process. In this study, the ML models are trained on GCI engine experiments, covering different intake conditions, injection strategies, and spark settings. The models can predict seven engine performance parameters: fuel consumption, four engine-out emissions, exhaust temperature, and coefficient of variation (COV) in indicated mean effective pressure (IMEP).

The piezoelectric cylinder pressure transducer is one of the most critical tools for internal combustion (IC) engine research and development. However, not all cylinder pressure transducers perform equally in every application, and the fidelity of transducers can vary across different models and manufacturers. Even slightly dissimilar models from the same manufacturer can have significantly different performance in areas such as sensitivity and resistance to intra-cycle thermal shock. These performance differences can lead to errors and inconsistencies in the calculation of combustion metrics like mean effective pressure (MEP), the polytropic compression and expansion exponents (PolyC and PolyE), and mass fraction burn (MFB) calculations. The variations can lead to suboptimal hardware and calibration choices during the engine development phase.

The primary goal of this project was to design and implement an oxidation catalyst specific to a high-performance spark ignited two stroke engines to reduce vehicle-out emissions. The primary challenges of two stroke catalysis at high loads include controlling the catalytic reaction temperature as well as minimizing the increase in exhaust back pressure due to the addition of a catalyst. Reaction temperature is difficult to control due to high HC and CO concentrations paired with an excess of oxygen in the exhaust stream. By limiting catalyst conversion efficiency, the reaction temperatures were controlled. Two stroke engines are also inherently sensitive to changes in exhaust back pressure and therefore location and sizing of the catalyst are key design considerations. Because of these challenges significant effort was directed toward developing the two-stroke specific catalyst design process.

The increasing demand for high-octane fuels is pushing the combustion research towards investigating new potential fuels and octane boosters. In addition to their high-octane, those additives should be environmentally friendly. In this study, the anti-knock properties of Dicyclopentadiene (DCPD) as an additive to primary reference fuels (PRF) and toluene primary reference fuels (TPRF) have been investigated. The Research octane number (RON) and Motor octane number (MON) were measured using Cooperative Fuels Research (CFR) engine for four different fuel blends; PRF 60 + 10% DCPD, PRF 60 + 20% DCPD, PRF 70 + 10% DCPD and TPRF 70 + 10% DCPD. In addition, homogenous charge compression ignition (HCCI) was also performed using the CFR engine to show the effect of DCPD on suppressing low temperature chemistry of reference fuels.

Drag reducing agents (DRAs) are extensively used to increase the capacity of pipelines to transport crude oils and finished products. The amount of DRA that can be used in gasoline is limited by the tendency of the high molecular weight DRAs to form engine deposits. The use of deposit control additives (DCAs) could help to mitigate this effect, enabling increased DRA treatment rates and improved pipeline capacity. A study has been undertaken to investigate the engine test response of these additives, and has suggested that higher DRA treat rates may be possible when accompanied by a deposit control additive to address increased intake valve deposits. Conversely, the effect on combustion chamber deposits is not clear and further studies would be required. Other engine related aspects such as intake valve deposit stick have also been investigated and under the conditions tested do not appear to be adversely affected by either the DRA or the deposit control additive.

This paper examines, for several major markets, the fuel savings achievable with advanced engine technologies as “drop-in” substitutions for existing engines, as well as from increased electric hybridization of the powertrain. Key segments of light duty vehicles in major automotive markets including the US, China, EU, Japan, India, and Saudi Arabia were examined. Representative vehicles for each market were simulated using advanced vehicle modeling tools and evaluated on the relevant local regulatory cycle or cycles. In all cases, to ensure meaningful results, the performance of a given vehicle was maintained as engine and powertrain technology was varied through appropriate resizing of powertrain components. In total, 4 engine technologies and 5 powertrain architectures were simulated for 5 different markets.

Compared to conventional diesel combustion (CDC), isobaric combustion can achieve a similar or higher indicated efficiency, lower heat transfer losses, reduced nitrogen oxides (NOx) emissions; however, with a penalty of soot emissions. While the engine performance and exhaust emissions of isobaric combustion are well known, the overall flame development, in particular, the flow-field details within the flames are unclear. In this study, the performance analysis of CDC and two isobaric combustion cases was conducted, followed by high-speed imaging of Mie-scattering and soot luminosity in an optically accessible, single-cylinder heavy-duty diesel engine. From the soot luminosity imaging, qualitative flow-fields were obtained using flame image velocimetry (FIV). The peak motoring pressure (PMP) and peak cylinder pressure (PCP) of CDC are kept fixed at 50 and 70 bar, respectively.

Isobaric combustion has shown the potential of improving engine efficiency by lowering the heat transfer losses. Previous studies have achieved isobaric combustion through multiple injections from a single central injector, controlling injection timing and duration of the injection. In this study, we employed three injectors, i.e. one centrally mounted (C) on the cylinder head and two side-injectors (S), slant-mounted on cylinder head protruding their nozzle tip near piston-bowl to achieve the isobaric combustion. This study visualized the flame development of isobaric combustion, linking flow-field details to the observed trends in engine efficiency and soot emissions. The experiments were conducted in an optically accessible single-cylinder heavy-duty diesel engine using n-heptane as fuel. Isobaric combustion, with a 50 bar peak pressure, was achieved with three different injection strategies, i.e. (C+S), (S+C), and (S+S).

The high-pressure isobaric combustion has been proposed as the most suitable combustion mode for the double compre4ssion expansion engine (DCEE) concept. Previous experimental and simulation studies have demonstrated an improved efficiency compared to the conventional diesel combustion (CDC) engine. In the current study, isobaric combustion was achieved using a single injector with multiple injections. Since this concept involves complex phenomena such as spray to spray interactions, the computational models were extensively validated against the optical engine experiment data, to ensure high-fidelity simulations. The considered optical diagnostic techniques are Mie-scattering, fuel tracer planar laser-induced fluorescence (PLIF), and natural flame luminosity imaging. Overall, a good agreement between the numerical and experimental results was obtained.

This paper investigates the application of torque weighting to vibration dose value. This is done as a means to enhance correlation of perceived drive comfort directly to driver pedal commands while rejecting uncorrelated inputs. Current industry standards for vehicle comfort are formulated and described by ISO2631, which is a culmination of research with single or multi-axis vibration of narrow or broadband excitation. The standard is capable of estimating passenger comfort to vibrations, however, it only accounts for reaction vibrations to controlled inputs and not perceived vibration request vs. response vibration. Metrics that account for torque inputs and the vibration response create actionable estimates of dosage due to driver torque requests without uncorrelated inputs. This reduces the need for additional accelerometers and special compensating algorithms when road or track testing. The use case for the proposed modified metric is during the powertrain calibration process.

Robust autonomous navigation in unstructured environments is an unsolved problem and critical to the operation of autonomous military and rescue ground vehicles. Two-dimensional path planners operating on occupancy grids or costs maps can produce infeasible paths when the operational area includes complex terrain. Recently, sample-based path planners that plan on LiDAR-acquired point-cloud maps have been proposed. These approaches require no discretization of the operational area and provide direct pose estimation by modeling vehicle and terrain interaction. In this paper, we show that direct sample-based path planning on point clouds is effective and robust in unstructured environments. Robustness is demonstrated by completing a system parameter sensitivity analysis of the system in an Unreal simulation environment and partnered with field validation.

Off-road vehicle operation requires constant decision-making under great uncertainty. Such decisions are multi-faceted and range from acquisition decisions to operational decisions. A major input to these decisions is terrain information in the form of soil properties. This information needs to be propagated to path planning algorithms that augment them with other inputs such as visual terrain assessment and other sensors. In this sequence of steps, many resources are needed, and it is not often clear how best to utilize them. We present an integrated approach where a mission’s overall performance is measured using a multiattribute utility function. This framework allows us to evaluate the value of acquiring terrain information and then its use in path planning. The computational effort of optimizing the vehicle path is also considered and optimized. We present our approach using the data acquired from the Keweenaw Research Center terrains and present some results.

The aim of this paper is to computationally investigate the combustion behavior and energy recovery processes of a six-stroke gasoline compression ignition (6S-GCI) engine that employs a continuously variable valve duration (CVVD) technique, under highly diluted, low-temperature combustion (LTC) conditions. The effects of variation of parameters concerning injection spray targeting (number of fuel injector holes. injector nozzle size and spray included angle) and combustion chamber geometry (piston bowl design) are analyzed using an in-house 3D CFD code coupled with high-fidelity physical sub-models with the Chemkin library in conjunction with a skeletal chemical kinetics mechanism for a 14-component gasoline surrogate fuel.

Gasoline compression ignition (GCI) has been shown to offer benefits in the NOx-soot tradeoff over conventional diesel combustion while still achieving high fuel efficiency. However, due to gasoline’s low reactivity, it is challenging for GCI to attain robust ignition and stable combustion under cold operating conditions. Building on previous work to evaluate glow plug-assisted GCI combustion at cold idle, this work evaluates the use of a spark plug to assist combustion. The closed-cycle 3-D CFD model was validated against GCI test results at a compression ratio of 17.3 during extended cold idle operation under laboratory-controlled conditions. A market representative, ethanol-free, gasoline (RON92, E0) was used in both the experiment and the numerical analysis. Spark-assisted simulations were performed by incorporating an ignition model with the spark energy required for stable combustion at cold start.

As the world searches for ways to reduce humanity’s impact on the environment, the automotive industry looks to extend the viable use of the gasoline engine by improving efficiency. One way to improve engine efficiency is through more effective control. Torque-based control is critical in modern cars and trucks for traction control, stability control, advanced driver assistance systems, and autonomous vehicle systems. Closed loop torque-based engine control systems require feedback signal(s); indicated mean effective pressure (IMEP) is a useful signal but is costly to measure directly with in-cylinder pressure sensors. Previous work has been done in torque and IMEP estimation using crankshaft acceleration and ion sensors, but these systems lack accuracy in some operating ranges and the ability to estimate cycle-cycle variation.

This paper presents a methodology that optimizes the periods of engine operation on a selected route for a Plug-in Hybrid Electric Vehicle (PHEV) or Hybrid Electric Vehicle (HEV) using Connected Vehicle data to minimize energy consumption. The study was conducted using a Reduced-Order Powertrain model of second-generation Chevrolet Volt. The method utilizes the Backward Induction Dynamic Programming algorithm to come up with an optimal control mode matrix of engine operation along the selected route for various battery states of charge. The objective of this method is to make use of Vehicle Connectivity to minimize the energy utilization of an HEV by using the speed and elevation profile of a selected route transmitted to the vehicle via V2X communication systems.

The effects of intake pressure (Pin), start of injection (SOI), injection pressure (Pinj), injection split ratio (Qsplit), internal and external exhaust gas recirculation rates were varied to optimize several key parameters at a partially pre-mixed combustion low load/low speed condition using CFD. These include indicated specific fuel consumption (ISFC), combustion phasing (CA50), maximum rate of pressure rise (MRPR), maximum cylinder pressure (Pmax), indicated specific NOx (sNOx), indicated specific hydrocarbons (sHC) and Filter Smoke Number (FSN) emissions. Low-load point (6 bar indicated mean effective pressure (IMEP), 1500 revolutions per minute (RPM)) was selected where Pin varied between 1.25 and 1.5 bar, SOI between -100 and -10 crank angle degree (CAD) after top dead center (aTDC), Pinj between 100 and 200 bar, split ratio between 0 and 0.5, EGR between 0 and 45% and internal EGR measured by rebreathing valve height was varied between 0 and 4.5 mm.

The efficiency and emission potential of pre-chamber combustion in a Miller cycle light duty gasoline engine operated under part load was evaluated. Several pre-chamber designs that examine the engine performance tradeoffs with nozzle diameter, pre-chamber volume, number of nozzles, and pre-chamber fuel enrichment were investigated for both excess air and cooled external EGR dilution strategies. The introduction of pre-chamber jet ignition was observed to significantly reduce the main-chamber combustion duration while reducing cyclic variability under dilute conditions, benefiting from the long-reach ignition jets and enhanced turbulence. However, the pre-chamber design that provided the fastest combustion led to reduced brake efficiency primarily due to increased wall heat loss. Maintaining the total nozzle area while increasing the number of nozzles was identified as a means to minimize the additional heat loss and maintain fast burn rates.

Pre-chamber combustion (PCC) engines allow extending the lean limit of operation compared to common SI engines, thus being a candidate concept for the future clean transportation targets. To understand the fundamental mechanisms of the main chamber charge ignition in PCC engines, the effects of the composition in the pre-chamber were investigated numerically. A well-stirred reactor combustion model coupled with a methane oxidation mechanism reduced from GRI 3.0 was used. An open-cycle simulation was run with initialization at exhaust valve opening (EVO). For posterior simulations, the initial flow field was attained by mapping the field variables obtained from the full cycle simulation. The entire simulation domain (pre-chamber and main chamber) global excess air ratio (λ) was set to 1.3.