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The Particle Number–Portable Emission Measurement System (PN-PEMS) came into force with Euro VI Phase E regulations starting January 1, 2022. However, positive ignition (PI) engines must comply from January 1, 2024. The delay was due to the unavailability of the PN-PEMS system that could withstand high concentrations of water typically present in the tailpipe (TP) of CNG vehicles, which was detrimental to the PN-PEMS systems. Thus, this study was designed to evaluate the condensation particle counter (CPC)-based PN-PEMS measurement capabilities that was upgraded to endure high concentration of water. The PN-PEMS measurement of solid particle number (SPN23) greater than 23 nm was compared against the laboratory-grade PN systems in four phases. Each phase differs based upon the PN-PEMS and PN system location and measurements were made from three different CNG engines. In the first phase, systems measured the diluted exhaust through constant volume sampler (CVS) tunnel.

Ammonia is one of the carbon-free alternatives considered for power generation and transportation sectors. But ammonia’s lower flame speed, higher ignition energy, and higher nitrogen oxides emissions are challenges in practical applications such as internal combustion engines. As a result, modifications in engine design and control and the use of a secondary fuel to initiate combustion such as natural gas are considered for ammonia-fueled engines. The higher-octane number of methane (the main component in natural gas) and ammonia allows for higher compression ratios, which in turn would increase the engine's thermal efficiency. One simple approach to initiate and control combustion for a high-octane fuel at higher compression ratios is to use a spark plug. This study experimentally investigated the operation of a heavy-duty compression ignition engine converted to spark ignition and ammonia-methane blends.

Biodiesel is a promising alternative to traditional diesel fuel due to its similar combustion properties to diesel and lower carbon emissions on a well-to-wheel basis. However, combusting biodiesel still generates hydrocarbon (HC), CO, NOx and particulate matter (PM) emissions, similar to those from traditional diesel fuel usage. Therefore, aftertreatment systems will be required to reduce these emissions to meet increasingly stringent emission regulations to minimize the impact to the environment. Diesel oxidation catalysts (DOC) are widely used in modern aftertreatment systems to convert unburned HC and CO, to partially convert NO to NO2 to enhance downstream selective catalytic reaction (SCR) catalyst efficiency via fast SCR and to periodically clean-up DPF via controlled soot oxidation. In this work, we focus on the performance difference between biodiesel and diesel over a commercial DOC catalyst to identify the knowledge gap during the transition from diesel fuel to biodiesel.

The steam reforming of CH4 plays a crucial role in the high-temperature activity of natural gas three-way catalysts. Despite existing reports on sulfur inhibition in CH4 steam reforming, there is a limited understanding of sulfur storage and removal dynamics under various lambda conditions. In this study, we utilize a 4-Mode sulfur testing approach to elucidate the dynamics of sulfur storage and removal and their impact on three-way catalyst performance. We also investigate the influence of sulfur on CH4 steam reforming by analyzing CH4 conversions under dithering, rich, and lean reactor conditions. In the 4-Mode sulfur test, saturating the TWC with sulfur at low temperatures emerges as the primary cause of significant three-way catalyst performance degradation. After undergoing a deSOx treatment at 600 °C, NOx conversions were fully restored, while CH4 conversions did not fully recover.

Diesel oxidation catalyst (DOC) is one of the critical catalyst components in modern diesel aftertreatment systems. It mainly converts unburned hydrocarbon (HC) and CO to CO2 and H2O before they are released to the environment. In addition, it also oxidizes a portion of NO to NO2, which improves the NOx conversion efficiency via fast SCR over the downstream selective catalytic reduction (SCR) catalyst. HC light-off tests, with or without the presence of NOx, has been typically used for DOC evaluation in laboratory. In this work, we aim to understand the influences of DOC light-off experimental conditions, such as initial temperature, initial holding time, HC species, with or without the presence of NOx, on the DOC HC light-off behavior. The results indicate that light-off test with lower initial temperature and longer initial holding time (at its initial temperature) leads to higher DOC light-off temperature.

The prediction accuracy of a three-way catalyst (TWC) model is highly associated with the ability of the model to incorporate the reaction kinetics of the emission process as a lambda function. In this study, we investigated the O2 and H2 concentration profiles of TWC reactions and used them as critical inputs for the development of a global TWC model. We presented the experimental data and global kinetic model showing the impact of thermal degradation on the performance of the TWC. The performance metrics investigated in this study included CH4, NOx, and CO conversions under lean, rich, and dithering light-off conditions to determine the kinetics of oxidation reactions and reduction/reforming/water-gas shift reactions as a function of thermal aging. The O2 and H2 concentrations were measured using mass spectrometry to track the change in the oxidation state of the catalyst and to determine the mechanism of the reactions under these light-off conditions.

Upcoming, stricter diesel exhaust emissions standards will likely require aftertreatment architectures with multiple diesel exhaust fluid (DEF) introduction locations. Managing NH3 slip with technologies such as an ammonia slip catalyst (ASC) will continue to be critical in these future aftertreatment systems. In this study, we evaluate the impact of SO2 exposure on a state-of-the-art commercially available ASC. SO2 is co-fed at 0.5 or 3 ppmv to either approximate or accelerate a real-world exhaust SO2 impact. ASC performance during sulfur co-feeding is measured under a wide variety of simulated real-world conditions. Results indicate that the loss of NO conversion during SCR is dependent on the cumulative SO2 exposure, regardless of the inlet SO2 concentration. Meanwhile, N2O formation under SCR conditions is nonlinearly affected by SO2 exposure, with formation increasing during 0.5 ppmv SO2 exposure but decreasing in the presence of 3 ppmv SO2.

Stoichiometric natural gas engines with three-way catalysts emit less NOx and CH4 due to their higher efficiency compared to lean-burn natural gas engines. Although hydrothermal aging of three-way catalysts has been extensively studied, a deeper understanding beyond hydrothermal aging is needed to explain real-world performance, especially for natural gas engines with near-zero NOx emissions. In this investigation, field-aged three-way catalysts were characterized to identify the contribution of chemical aging to their overall performance. It was found that the sulfur species on the field-aged TWCs were entirely distributed along the catalyst length, showing a decreasing trend, whereas phosphorous contamination was mainly observed at the inlet section of the three-way catalysts, and the phosphorous concentration declined sharply along the axial length.

Safety, fuel economy and uptime are key requirements for the operation of heavy-duty line-haul trucks within a fleet. With the penetration of connectivity and automation technologies, energy optimal and safe operation of the trucks are further improved through Advanced Driver Assistance System (ADAS) features and automated technologies as in truck platooning. Understanding the braking capability of the vehicle is very important for optimal ADAS and platooning control system design and integration. In this paper, the importance of tire connectivity and tire conditions on truck stopping distance are demonstrated through testing. The test data is further utilized to develop tire models for integration in an optimal vehicle automation for platooning. New ways to produce and use the tire related information in real-time optimal control of platooning trucks are proposed and the contribution of tire information in fuel economy is quantified through simulations.

Machine learning algorithms are effective tools to reduce the number of engine dynamometer tests during internal combustion engine development and/or optimization. This paper provides a case study of using such a statistical algorithm to characterize the heat transfer from the combustion chamber to the environment during combustion and during the entire engine cycle. The data for building the machine learning model came from a single cylinder compression ignition engine (13.3 compression ratio) that was converted to natural-gas port fuel injection spark-ignition operation. Engine dynamometer tests investigated several spark timings, equivalence ratios, and engine speeds, which were also used as model inputs. While building the model it was found that adding the intake pressure as another model input improved model efficiency.

The combustion process in spark-ignition engines can vary considerably cycle by cycle, which may result in unstable engine operation. The phenomena amplify in natural gas (NG) spark-ignition (SI) engines due to the lower NG laminar flame speed compared to gasoline, and more so under lean burn conditions. The main goal of this study was to investigate the main sources and the characteristics of the cycle-by-cycle variation in heavy-duty compression ignition (CI) engines converted to NG SI operation. The experiments were conducted in a single-cylinder optically-accessible CI engine with a flat bowl-in piston that was converted to NG SI. The engine was operated at medium load under lean operating conditions, using pure methane as a natural gas surrogate. The CI to SI conversion was made through the addition of a low-pressure NG injector in the intake manifold and of a NG spark plug in place of the diesel injector.

Machine learning models were shown to provide faster results but with similar accuracy to multidimensional computational fluid dynamics or in-depth experiments. This study used a support-vector machine (SVM), a set of related supervised learning methods, to predict the dynamic performance (i.e., engine power and torque) of a heavy-duty natural gas spark ignition engine. The single-cylinder four-stroke test engine was fueled with methane. The engine was operated at different spark timings, mixture equivalence ratios, and engine speeds to provide the data for training and testing the proposed SVM. The results indicated that the performance and accuracy of the built regression model were satisfactory, with correlation coefficient quantities all larger than 0.95 and root-mean-square errors close to zero for both training and validation datasets.

The application of cooperative adaptive cruise control (CACC) to heavy-duty trucks known as truck platooning has shown fuel economy improvements over test track ideal driving conditions. However, there are limited test data available to assess the performance of CACC under real-world driving conditions. As part of the Cummins-led U.S. Department of Energy Funding Opportunity Announcement award project, truck platooning with CACC has been tested under real-world driving conditions and the results are presented in this paper. First, real-world driving conditions are characterized with the National Renewable Energy Laboratory’s Fleet DNA database to define the test factors. The key test factors impacting long-haul truck fuel economy were identified as terrain and highway traffic with and without advanced driver-assistance systems (ADAS).

Evaluation of the platooning legislative space suggests a limited near-term opportunity for autonomous vehicles as currently only nine states have platooning and autonomous favorable legislations. An extensive closed-loop vehicle model simulation was conducted to quantify two-truck platooning fuel economy entitlement benefits across all United States (US) interstate routes (I-xx) spanning over 40,000 miles as compared to a single truck. A simultaneous study was carried out to identify the density of Class 8 heavy-duty trucks on these interstates, using the Freight Analysis Framework (FAF) 4 database. These two studies were combined to ascertain interstates that foresee the least fuel consumption due to platooning and thus identifying states with the most platooning benefits. Identification of states with most platooning benefits provides realistic data to push for autonomous driving and platooning legislations.

The measurement of solid particles down to 10 nm is being incorporated into global technical regulations (GTR). This study explores the measurement of solid particles below 23 nm by using both current and proposed particle number (PN) systems having different volatile particle remover (VPR) methodologies and condensation particle counter (CPC) cutoff diameters. The measurements were conducted in dynamometer test cells using ten diesel and eight natural gas (NG) engines that were going under development for a variety of global emission standards. The PN systems measured solid PN from more than 700 test cycles. The results from the preliminary campaign showed a 10-280% increase in PN emissions with the inclusion of particles below 23 nm.

Performance of a natural gas two-stroke engine incorporated in a 1-kW free-piston oscillating Linear Engine Alternator (LEA) - a household electricity generator - was investigated under different resonant frequencies for pre-design phase purposes. To increase the robustness, power density, and thermal efficiencies, the crank mechanism in free-piston LEA is omitted and all moving parts of the generator operate at a fixed resonant frequency. Flexure springs are the main source of the LEA’s stiffness and the mass-spring dynamics dominates the engine’s speed. The trade-off between the engine’s performance, mass-spring system limits, and power and efficiency targets versus the LEA speed is very crucial and demands a careful investigation specifically at the concept design stages to find the optimum design parameters and operating conditions. CFD modeling was performed to analyze the effects of resonant frequency on the engine’s gas exchange behavior.

A head gasket is a component that sits between the engine block/liner and cylinder head(s) in an internal combustion engine. Its purpose is to seal high pressure combustion gasses in the cylinders and to seal coolant and engine oil. It is the most critical sealing application in an engine. As a general practice, the load deflection(L/D) characteristic is generated by the gasket manufacturer for edge molded or composite gasket types. However, in the case of a solid-sheet metallic gasket, where the gasket is expected to undergo localized yielding to provide adequate conformance and sealing, usually supplier may not be able to provide the required L/D curve due to difficulties to experimentally separate the large loads and small displacements from the elastic loads and deflections of the experimental apparatus. In absence of L/D curve, the typical analysis approach is to model gasket as 3D continuum elements available in ansys by considering nonlinear material and frictional contacts.

The conversion of compression ignition (CI) internal combustion engines to spark-ignition (SI) operation by adding a spark plug to ignite the mixture and fumigating the fuel inside the intake manifold can increase the use of alternative gaseous fuels (e.g., natural gas) in heavy-duty applications. This study proposed a novel, less-complex methodology based on the inflection points in the apparent rate of heat release (ROHR) that can identify and separate the fast-burning stage inside the piston bowl from the slower combustion stage inside the squish region (a characteristic of premixed combustion inside a diesel geometry). A single-cylinder 2L CI research engine converted to natural gas SI operation provided the experimental data needed to evaluate the methodology, at several spark timings, equivalence ratios, and engine speeds.

The conversion of existing diesel engines to natural-gas operation can reduce the dependence on petroleum imports and curtail engine-out emissions. A convenient way to perform such conversion is by adding a gas injector in the intake manifold and replacing the diesel fuel injector with a spark plug to initiate and control the combustion process. However, challenges may appear with respect to engine’s efficiency and emissions as natural-gas spark-ignition combustion inside a diesel combustion chamber is different to that in conventional spark ignition engines. For example, major difference is the phasing and duration of the fast burn, defined as the period in which the rate of heat release increases linearly with crank angle. This study presents a methodology to investigate the fast burn inside a diesel geometry using heat release data.

Recent development in hydraulic fracking made natural gas (NG) to be a promising alternative gaseous fuel for heavy-duty diesel engines. The existing compression ignition (CI) engine can be retrofitted to NG spark ignition (SI) operation by replacing the diesel injector with a spark plug and fumigating NG into the intake manifold. However, the original diesel piston geometry (flat head and bowl-in-piston chamber) was usually retained to reduce modification cost. The goal of this study was to increase the understanding of the NG lean-burn characteristics in a diesel-like, fast-burn SI combustion chamber. The experimental platform can operate in conventional (i.e., all engine parts are metal) or in optical configuration (i.e., the stock piston and cylinder block are replaced with a see-through piston and an extended cylinder block). The optical data indicated a fast-propagated flame inside the piston bowl.