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The Particulate Matter Index (PMI) is a tool that provides an indication of a fuel’s tendency to produce Particulate Matter (PM) emissions. Currently, the index is being used by various fuel laboratories and the Automotive OEMs as a tool to understand the gasoline fuel’s impact on both PM from engine hardware and vehicle-out emissions. In addition, a newer index that could be used to give an indication of the PM tendency of the gasoline range fuels, called the Particulate Evaluation Index (PEI), is shown to have a good correlation to PMI. The data used in those indices are collected from chemical analytical methods. This paper will compare gas chromatography (GC) methods used by three laboratories and discuss how the different techniques may affect the PMI and PEI calculation.

Fuel lean combustion and exhaust gas dilution are known to increase the thermal efficiency and reduce NOx emissions. In this study, experiments are performed to understand the effect of equivalence ratio on flame kernel formation and flame propagation around the spark plug for different low turbulent velocities. A series of experiments are carried out for propane-air mixtures to simulate engine-like conditions. For these experiments, equivalence ratios of 0.7 and 0.9 are tested with 20 percent mass-based exhaust gas recirculation (EGR). Turbulence is generated by a shrouded fan design in the vicinity of J-spark plug. A closed loop feedback control system is used for the fan to generate a consistent flow field. The flow profile is characterized by using Particle Image Velocimetry (PIV) technique. High-speed Schlieren visualization is used for the spark formation and flame propagation.

An investigation of high speed direct injection (DI) compression ignition (CI) engine combustion fueled with gasoline (termed GDICI for Gasoline Direct-Injection Compression Ignition) in the low temperature combustion (LTC) regime is presented. As an aid to plan engine experiments at full load (16 bar IMEP, 2500 rev/min), exploration of operating conditions was first performed numerically employing a multi-dimensional CFD code, KIVA-ERC-Chemkin, that features improved sub-models and the Chemkin library. The oxidation chemistry of the fuel was calculated using a reduced mechanism for primary reference fuel combustion. Operation ranges of a light-duty diesel engine operating with GDICI combustion with constraints of combustion efficiency, noise level (pressure rise rate) and emissions were identified as functions of injection timings, exhaust gas recirculation rate and the fuel split ratio of double-pulse injections.

The diesel particulate filters (DPF) are considered the most robust technologies for particle emission reduction both in terms of mass and number. On the other hand, the increase of the backpressure in the exhaust system due to the accumulation of the particles in the filter walls leads to an increase of the engine fuel consumption and engine power reduction. To limit the filter loading, and the backpressure, a periodical regeneration is needed. Because of the growing interest about particle emission both in terms of mass, number and size, it appears important to monitor the evolution of the particle mass and number concentrations and size distribution during the regeneration of the DPFs. For this matter, in the presented work the regeneration of a catalyzed filter was fully analyzed. Particular attention was dedicated to the dynamic evolution both of the thermodynamic parameters and particle emissions.

The present paper describes the results of a cooperative research project between GM Powertrain Europe and Istituto Motori - CNR aimed at studying the capability of GM Combustion Closed-Loop Control (CLCC) in enabling seamless operation with high biodiesel blending levels in a modern diesel engine for passenger car applications. As a matter of fact, fuelling modern electronically-controlled diesel engines with high blends of biodiesel leads to a performance reduction of about 12-15% at rated power and up to 30% in the low-end torque, while increasing significantly the engine-out NOx emissions. These effects are both due to the interaction of the biodiesel properties with the control logic of the electronic control unit, which is calibrated for diesel operation. However, as the authors previously demonstrated, if engine calibration is re-tuned for biodiesel fuelling, the above mentioned drawbacks can be compensated and the biodiesel environmental inner qualities can be fully deployed.

A Machine Learning (ML) approach is presented to correlate in-cylinder images of early flame kernel development within a spark-ignited (SI) gasoline engine to early-, mid-, and late-stage flame propagation. The objective of this study was to train machine learning models to analyze the relevance of flame surface features on subsequent burn rates. Ultimately, an approach of this nature can be generalized to flame images from a variety of sources. The prediction of combustion phasing was formulated as a regression problem to train predictive models to supplement observations of early flame kernel growth. High-speed images were captured from an optically accessible SI engine for 357 cycles under pre-mixed operation. A subset of these images was used to train three models: a linear regression model, a deep Convolutional Neural Network (CNN) based on the InceptionV3 architecture and a CNN built with assisted learning on the VGG19 architecture.

Although the urea-SCR technology exhibits high NO reduction efficiency over a wide range of temperatures among the lean NO reduction technologies, further improvement in low-temperature performance is required to meet the future emission standards and to lower the system cost. In order to improve the catalyst technologies and optimize the system performance, it is critical to understand the reaction mechanisms and catalyst behaviors with respect to operating conditions. Urea-SCR catalysts exhibit poor NO reduction performance at low-temperature operating conditions (T ≺ 150°C). We postulate that the poor performance is either due to NH₃ storage inhibition by species like hydrocarbons or due to competitive adsorption between NH₃ and other adsorbates such as H₂O and hydrocarbons in the exhaust stream. In this paper we attempt to develop one-dimensional models to characterize inhibition and competitive adsorption in Fe-zeolite-based urea-SCR catalysts based on bench reactor experiments.

We describe a study to identify potential biofuels that enable advanced spark ignition (SI) engine efficiency strategies to be pursued more aggressively. A list of potential biomass-derived blendstocks was developed. An online database of properties and characteristics of these bioblendstocks was created and populated. Fuel properties were determined by measurement, model prediction, or literature review. Screening criteria were developed to determine if a bioblendstock met the requirements for advanced SI engines. Criteria included melting point (or cloud point) < -10°C and boiling point (or T90) <165°C. Compounds insoluble or poorly soluble in hydrocarbon were eliminated from consideration, as were those known to cause corrosion (carboxylic acids or high acid number mixtures) and those with hazard classification as known or suspected carcinogens or reproductive toxins.

More stringent emissions regulations are continually being proposed to mitigate adverse human health and environmental impacts of internal combustion engines. With that in mind, it has been proposed that vehicular particulate matter (PM) emissions should be regulated based on particle number in addition to particle mass. One aspect of this project is to study different sample handling methods for number-based aerosol measurements, specifically, two different methods for removing volatile organic compounds (VOCs). One method is a thermodenuder (TD) and the other is an evaporative chamber/diluter (EvCh). These sample-handling methods have been implemented in an engine test cell with a spark-ignited direct injection (SIDI) engine. The engine was designed for stoichiometric, homogeneous combustion.

Over the past several years a task group within the SAE Automotive Corrosion and Protection (ACAP) Committee has conducted extensive on-vehicle field testing and numerous accelerated lab tests with the goal of establishing a standard accelerated test method for cosmetic corrosion evaluations of finished aluminum auto body panels. This project has been a cooperative effort with OEM, supplier, and consultant participation and was also supported in part by DOE through USAMP (AMD 309). The focus of this project has been the identification of a standardized accelerated cosmetic corrosion test that exhibits the same appearance, severity, and type of corrosion products that are exhibited on identical painted aluminum panels exposed to service relevant environments. Multi-year service relevant exposures were conducted by mounting panels on-vehicles in multiple locations in the US and Canada.

There has been an increasing interest in exploring the potential to reduce energy consumption of future connected and automated vehicles. People have extensively studied various eco-driving implementations that leverage preview information provided by on-board sensors and connectivity, as well as the control authority enabled by automation. Quantitative real-world evaluation of eco-driving benefits is a challenging task. The standard regulatory driving cycles used for measuring exhaust emissions and fuel economy are not truly representative of real-world driving, nor for capturing how connectivity and automation might influence driving trajectories. To adequately consider real-world driving behavior and potential “off-cycle” impacts, this paper presents four collaborative evaluation methods: large-scale simulation, in-depth simulation, vehicle-in-the-loop testing, and vehicle road testing.

It has long been recognized that the key to achieving stringent emission standards such as ULEV is the control of cold-start hydrocarbons. This paper describes a new approach for achieving excellent cold-start hydrocarbon control. The most important component in the system is a catalyst that is highly active at ambient temperature for the exothermic CO oxidation reaction in an exhaust stream under net lean conditions. This catalyst has positive order kinetics with respect to CO for CO oxidation. Thus, as the concentration of CO in the exhaust is increased, the rate of this reaction is increased, resulting in a faster temperature rise over the catalyst.

This paper examines some key aspects of the manufacturing process that “ Transformation Induced Plasticity” (TRIP) steels would be exposed to, and systematically evaluate how the forming and thermal histories affect final strength and ductility of the material. We evaluate the effects of in-service temperature variations, such as under hood and hot/cold cyclic conditions, to determine whether these conditions influence final strength, ductility and energy absorption characteristics of several available TRIP steel grades. As part of the manufacturing thermal environment evaluations, stamping process thermal histories are included in the studies. As part of the in-service conditions, different pre-straining levels are included. Materials from four steel suppliers are examined. The thermal/straining history versus material property relationship is established over a full range of expected thermal histories and selected loading modes.

New techniques have been developed to visualize soot deposition in both traditional and new diesel particulate filter (DPF) substrate materials using a modified cyanoacrylate fuming technique. Loading experiments have been conducted on a variety of single channel DPF substrates to develop a deeper understanding of soot penetration, soot deposition characteristics, and to confirm modeling results. Early results indicate that stabilizing the soot layer using a vaporized adhesive (Cynoacrylate) may allow analysis of the layer with new methods.

This paper describes the application of pore-scale filtration simulations to the advanced ceramic material (ACM) developed for use in advanced diesel particulate filters. The application required the generation of a three-dimensional substrate geometry to provide the boundary conditions for the flow model. An innovative stochastic modeling technique was applied matching chord length distribution and the porosity profile of the material. Additional experimental validation was provided by the single-channel experimental apparatus. Results show that the stochastic reconstruction techniques provide flexibility and appropriate accuracy for the modeling efforts. Early investigation efforts imply that needle length may provide a mechanism for adjusting performance of the ACM for diesel particulate filter (DPF) applications. New techniques have been developed to visualize soot deposition in both traditional and new DPF substrate materials.

The molecular filtration of sulfur components in ultra low sulfur diesel (ULSD) fuel is described. A comprehensive screening of potential sulfur removal chemistries has yielded a sorbent which has the capability to efficiently remove organo-sulfur components in ULSD fuel. This sorbent has been used to treat ULSD fuel on a heavy duty engine equipped with NOx adsorber after-treatment technology and has been shown to lengthen the time between desulfation steps for the NOx adsorber. The fuel properties, cetane number and aromatics content, etc., have not been changed by the removal of the sulfur in the fuel with the exception of the lubricity which is reduced.

The SNR-technique is a new NOx aftertreatment system for lean burn gasoline and diesel applications. The objective of SNR is NOx removal from lean exhaust gas by NOx adsorption and subsequent selective external recirculation and decomposition of NOx in the combustion process. The SNR-project is composed of two major parts. Firstly the development of NOx adsorbents which are able to store large quantities of NOx in lean exhaust gas, and secondly the NOx decomposition by the combustion process. Emphasis of this paper is the investigation of NOx reduction in the combustion process, including experimental investigation and numerical simulation. The NOx decomposition process has been proven in diesel and lean-burn gasoline engines. Depending on the type of engine NOx-conversion rates up to 90 % have been observed. Regarding the complete SNR-system, including the efficiency of the adsorbing material and the NOx decomposition by the combustion, a NOx removal of more than 50% is achievable.

Selective NOx recirculation (SNR), involving adsorption, selective external recirculation and decomposition of the NOx by the combustion process, is itself a promising technique to abate NOx emissions. Three types of materials containing Ba: barium aluminate, barium tin perovskite and barium Y-zeolites have been developed to adsorb NOx under lean-burn or Diesel conditions, with or without the presence of S02. All these materials adsorb NO2 selectively (lean-burn conditions), and store it as nitrate/nitrite species. The desorption takes place by decomposition of these species at higher temperatures. Nitrate formation implies also sulfate formation in the presence of SO2 and SO3, while the NO2/SO2 competition governs the poisoning of such catalysts.

Recent work has shown that energy efficiencies as well as yields and selectivities of the NOx reduction reaction can be enhanced by combining a plasma discharge with select catalysts. While analysis of gas phase species with a chemiluminescent NOx meter and mass spectrometer show that significant removal of NOx is achieved, high background concentrations of nitrogen preclude the measurement of nitrogen produced from NOx reduction. Results presented in this paper show that N2 from NOx reduction can be measured if background N2 is replaced with helium. Nitrogen production results are presented for a catalyst system where the catalyst is in the plasma region and where the catalyst is downstream from the plasma. The amount of N2 produced is compared with the amount of NOx removed as measured by the chemiluminescent NOx meter. The measured nitrogen from NOx reduction accounts for at least 40% of the total NOx removed for both reactor configurations.

The performance characteristics of a commercial lean-NOX trap catalyst were evaluated between 200 and 500°C, using H2, CO, and a mixture of both H2 and CO as reductants before and after different high-temperature aging steps, from 600 to 750°C. Tests included NOX reduction efficiency during cycling, NOX storage capacity (NSC), oxygen storage capacity (OSC), and water-gas-shift (WGS) and NO oxidation reaction extents. The WGS reaction extent at 200 and 300°C was negatively affected by thermal degradation, but at 400 and 500°C no significant change was observed. Changes in the extent of NO oxidation did not show a consistent trend as a function of thermal degradation. The total NSC was tested at 200, 350 and 500°C. Little change was observed at 500°C with thermal degradation but a steady decrease was observed at 350°C as the thermal degradation temperature was increased.