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Infrared spectroscopic methods are the most common methods in the automotive industry for measuring carbon monoxide (CO) and carbon dioxide (CO2) gases. Concentrations of both gases, which are emitted from the combustion of fuels, are required to be determined accurately in order to follow strict environmental regulations. Appropriate analytical techniques and accurate calibration gas mixtures are therefore critical for successful measurements. Regulatory documents such as the EPA’s Code of Federal Regulations 40 (CFR 40) part 1065.250, UN ECE-R83, and (EU) 2017/1151 recommend a nondispersive infrared (NDIR) analyzer to measure CO and CO2 concentrations in raw or diluted exhaust gas samples. Over the last decade, Fourier Transform Infrared (FTIR) spectrometry has been validated and recommended in engine exhaust certification testing as well as in engine and vehicle development activities.

In the near future, vehicles will operate autonomously and communicate with their environment. This communication includes Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) communication, and comunication with cloud-based servers (V2C). To improve the resilience of remote diagnostic communication between a vehicle and external test equipment against cyberattacks, it is imperative to understand and analyze the functionality and vulnerability of each communication system component, including the wired and wireless communication channels. This paper serves as a continuation of the SAE Journal publication on measures to prevent unauthorized access to the in-vehicle E/E system [9], explains the components of a cyber-physical system (CPS) for remote diagnostic communication, analyzes their vulnerability against cyberattacks and explains measures to improve the resiliance.

Autonomous vehicle (AV) developers test extensively on public roads, potentially putting other road users at risk. A safety case for human supervision of road testing could improve safety transparency. A credible safety case should include: (1) the supervisor must be alert and able to respond to an autonomy failure in a timely manner, (2) the supervisor must adequately manage autonomy failures, and (3) the autonomy failure profile must be compatible with effective human supervision. Human supervisors and autonomous test vehicles form a combined human-autonomy system, with the total rate of observed failures including the product of the autonomy failure rate and the rate of unsuccessful failure mitigation by the supervisor. A difficulty is that human ability varies in a nonlinear way with autonomy failure rates, counter-intuitively making it more difficult for a supervisor to assure safety as autonomy maturity improves.

Six blendstocks identified by the Co-Optimization of Fuels & Engines Program were used to prepare fuel blends using a fixed blendstock for oxygenate blending and a target RON of 97. The blendstocks included ethanol, n-propanol, isopropanol, isobutanol, diisobutylene, and a bioreformate surrogate. The blends were analyzed and used to establish interaction factors for a non-linear molar blending model that was used to predict RON and MON of volumetric blends of the blendstocks up to 35 vol%. Projections of efficiency increase, volumetric fuel economy increase, and tailpipe CO2 emissions decrease were produced using two different estimation techniques to evaluate the potential benefits of the blendstocks. Ethanol was projected to provide the greatest benefits in efficiency and tailpipe CO2 emissions, but at intermediate levels of volumetric fuel economy increase over a smaller range of blends than other blendstocks.

Cummins has recently launched next-generation aftertreatment technology, the Single ModuleTM aftertreatment system, for medium-duty and heavy-duty engines used in on-highway and off-highway applications. Besides meeting EPA 2010+ and Euro VI regulations, the Single ModuleTM aftertreatment system offers 60% volume and 40% weight reductions compared to current aftertreatment systems. In this work, we present model-based approaches that were systematically adopted in the design and development of the Cummins Single ModuleTM aftertreatment system. Particularly, a variety of analytical and experimental component-level and system-level validation tools have been used to optimize DOC, DPF, SCR/ASC, as well as the DEF decomposition device.

With progressing electrification of automotive powertrains and demands to meet increasingly stringent emission regulations, a combination of an electric motor and downsized turbocharged spark-ignited engine has been recognized as a viable solution. The SI engine must be optimized, and preferentially downsized, to reduce tailpipe CO2 and other emissions. However, drives to increase BMEP (Brake Mean Effective Pressure) and compression ratio/thermal efficiency increase propensities of knocking (auto-ignition of residual unburnt charge before the propagating flame reaches it) in downsized engines. Currently, knock is mitigated by retarding the ignition timing, but this has several limitations. Another option identified in the last decade (following trials of similar technology in aircraft combustion engines) is water injection, which suppresses knocking largely by reducing local in-cylinder mixture temperatures due to its latent heat of vaporization.

For better understanding, model development and its validation of in-cylinder soot formation processes of Gasoline Direct Injection (GDI) engines, crank-angle-resolved mass and size distribution of in-cylinder soot during a GDI combustion cycle were investigated via optical measurements and total cylinder sampling technique in an optically accessible Rapid Compression and Expansion Machine (RCEM). A direct-injection, spark-ignited and single-shot combustion event was achieved in the RCEM operated with engine speed 600 rpm, compression ratio 9.0, equivalence ratio 0.9 and natural aspiration. A three-component (iso-octane 65%, n-heptane 10%, toluene 25%) gasoline surrogate fuel and a multi-hole injector shared within the Japanese SIP Innovative Combustion Technology research program were used.

In this multi-phase study, fuel and lubricant effects on stochastic preignition (SPI) were examined. First, the behavior of fuels for which SPI data had previously been collected were characterized in terms of their combustion and emissions behavior, and correlations between these characteristics and their SPI behavior were examined. Second, new SPI data was collected for a matrix of fuels that was constructed to test and confirm hypotheses that resulted from interpretation of the earlier data in the study and from data in open literature. Specifically, the extent to which the presence of heavy components in the fuel affected SPI propensity, and the extent to which flame initiation propensity affected SPI propensity, were examined. Finally, the interaction of fuels with lubricants expected to exhibit a range of SPI propensities was examined.

NOx pollution from diesel engines has been stated as causing over 10 000 pre-mature deaths annually and predictions are showing that this level will increase [1]. In order to decrease this growing global problem, exhaust after-treatment systems for diesel engines have to be improved, this is especially so for vehicles carrying freight as their use of diesel engines is expected to carry on into the future [2]. The most common way to reduce diesel engine NOx out emissions is to use SCR. SCR operates by injecting aqueous Urea solution, 32.5% by volume (AUS-32), that evaporates prior the catalytic surface of the SCR-catalyst. Due to a catalytic reaction within the catalyst, NOx is converted nominally into Nitrogen and Water. Currently, the evaporative process is enhanced by aggressive mixer plates and long flow paths.

In most large-eddy simulation (LES) applications to two-phase engine flows, the liquid-air interactions need to be accounted for as source terms in the respective governing equations. Accurate calculation of these source terms requires the relative velocity “seen” by liquid droplets as they move across the flow, which generally needs to be estimated using a turbulent dispersion model. Turbulent dispersion modeling in LES is very scarce in the literature. In most studies on engine spray flows, sub-grid scale (SGS) models for the turbulent dispersion still follow the same stochastic approach originally proposed for Reynolds-averaged Navier-Stokes (RANS). In this study, an SGS dispersion model is formulated in which the instantaneous gas velocity is decomposed into a deterministic part and a stochastic part. The deterministic part is reconstructed using the approximate deconvolution method (ADM), in which the large-scale flow can be readily calculated.

Cavitation and cavitation-induced erosion have been observed in fuel injectors in regions of high acceleration and low pressure. Although these phenomena can have a large influence on the performance and lifetime of injector hardware, questions still remain on how these physics should be accurately and efficiently represented within a computational fluid dynamics model. While several studies have focused on the validation of cavitation predictions within canonical and realistic injector geometries, it is not well documented what influence the numerical and physical parameters selected to represent turbulence and phase change will have on the predictions for cavitation erosion propensity and severity. In this work, a range of numerical and physical parameters are evaluated within the mixture modeling approach in CONVERGE to understand their influence on predictions of cavitation, condensation and erosion.

This study is a continuation of previous work assessing the robustness of a Cummins XPI common rail injection system operating with gasoline-like fuel. All the hardware from the original study was retained except for the high pressure pump head and check valves which were replaced due to cavitation damage. An additional 400 hour NATO cycle was run on the refurbished fuel system to achieve a total exposure time of 800 hours and detect any other significant failure modes. As in the initial investigation, fuel system parameters including pressures, temperatures and flow rates were logged on a test bench to monitor performance over time. Fuel and lubricant samples were taken every 50 hours to assess fuel consistency, metallic wear, and interaction between fuel and oil. High fidelity driving torque and flow measurements were made to compare overall system performance when operating with both diesel and light distillate fuel.

The availability of connectivity and autonomy enabled resources, within the automotive sector, has primarily been considered for driver assist technologies and for extending the levels of vehicle autonomy. It is not a stretch to imagine that the additional information, available from connectivity and autonomy, may also be useful in further improving powertrain functions. Critical powertrain subsystems that must operate with limited or uncertain knowledge of their environment stand to benefit from such new information sources. Unfortunately, the adoption of this new information resource has been slow within the powertrain community and has typically been limited to the obvious problem choices such as battery charge management for electric vehicles and efforts related to fuel economy benefits from adaptive/coordinated cruise control. In this paper we discuss the application of connectivity resources in the management of an aftertreatment sub-system, the Diesel Particulate Filter (DPF).

An over-expanded spark ignited engine was investigated in this work via engine simulation with a design constrained, mechanically actuated Atkinson cycle mechanism. A conventional 4-stroke spark-ignited turbo-charged engine with a compression ratio of 9.2 and peak brake mean effective pressure of 22 bar was selected for the baseline engine. With geometry and design constraints including bore, stroke, compression ratio, clearance volume at top dead center (TDC) firing, and packaging, one over-expanded engine mechanism with over expansion ratio (OER) of 1.5 was designed. Starting with a validated 1D engine simulation model which included calibration of the in-cylinder heat transfer model and SI turbulent combustion model, investigations of the Atkinson engine including cam optimization was studied. The engine simulation study included the effects of offset of piston TDC locations as well as different durations of the 4-strokes due to the mechanism design.

The variability in gasoline energy content, though most frequently not a consumer concern, is an issue of concern for vehicle manufacturers in demonstrating compliance with regulatory requirements. Advancements in both vehicle technology, test methodology, and fuel formulations have increased the level of visibility and concern with regard to the energy content of fuels used for regulatory testing. The R factor was introduced into fuel economy calculations for vehicle certification in the late 1980s as a means of addressing batch-to-batch variations in the heating value of certification fuels and the resulting variations in fuel economy results. Although previous studies have investigated values of the R factor for modern vehicles through experimentation, subsequent engine studies have made clear that it is difficult to distinguish between the confounding factors that influence engine efficiency when R is being studied experimentally.

Medium duty vehicles come in many design variations, which makes testing them all for CO2 impractical. As a result there are multiple ways of reporting CO2 emissions. Actual tests may be performed, data substitution may be used, or CO2 values may be estimated using an analytical correction. The correction accounts for variations in road load force coefficients (f0, f1, f2), weight, and axle ratio. The EPA Analytically Derived CO2 equation (EPA ADC) was defined using a limited set of historical data. The prediction error is shown to be ±130 g/mile and the sensitivities to design variables are found to be incorrect. Since the absolute CO2 is between 500 and 1,000 g/mi, the equation has limited usefulness. Previous work on light duty vehicles has demonstrated a linear relationship between vehicle fuel consumption, powertrain properties and total vehicle work. This relationship improves the accuracy and avoids co-linearity and non-orthogonality of the input variables.

This review paper covers major regulatory and technology developments in 2018 pertinent to tailpipe emissions of greenhouse gases and criteria pollutants. Europe has proposed ambitious reductions in CO2 limits for both light- and heavy-duty sectors. The challenge is compounded with changing measurement norms and a significant shift away from fuel efficient diesels in the light-duty (LD) space. Both incremental and step changes are being made to advance internal combustion. New studies show that in-use NOx emissions from diesels can be much lower than required by the Euro 6 regulation. Discussions have already started on Euro 7 regulations, and the leading regulatory concepts and proposed technical solutions are provided. In the heavy-duty (HD) sector, the progress is outlined in improving engine and vehicle fuel efficiency through the US Department of Energy’s (DOE’s) SuperTruck II program and other representative studies.

1 The efficiency of internal combustion engines remains a research challenge given the mechanical friction and thermodynamic losses. Although incremental engine design changes continue to emerge, the harvesting of waste heat represents an immediate opportunity to address improved energy utilization. An external mobile thermal recovery system for gasoline and diesel engines is proposed for use in parking lots based on phase change material cartridges. Heat is extracted via a retrofitted conduction plate beneath the engine block after engine shutoff. An autonomous robot attaches the cartridge to the plate and transfers the heat from the block to the Phase Change Material (PCM) and returns later to retrieve the packet. These reusable cartridges are then driven to a Heat Extraction and Recycling Tower (HEART) facility where a heat exchanger harvests the thermal energy stored in the cartridges.

A new CFD simulation model and methodology for oil jet piston cooling has been developed using the modern level set approach. In contrast to the widely used volume of fluid (VOF) method, the level set approach explicitly tracks the interface surface between oil and air, using an additional field equation. The method has been extensively tested on two- and three-dimensional examples using results from literature for comparison. Furthermore, several applications of oil jet piston cooling on Ford engines have been investigated and demonstrated. For example, three-dimensional simulations of piston cooling nozzle jets on a diesel engine have been calculated and compared to test-rig measurements. Laminar jets, as well as jets with droplets and fully atomized jets, have been simulated using realistic material properties, surface tension, and gravity.

With the emergence of Driving Automation Systems (SAE levels 1-5), the necessity arises for methods of evaluating these systems. However, these systems are much more challenging to evaluate than traditional safety features (SAE level 0). This is because an understanding of the Driving Automation system’s response in all possible scenarios is desired, but prohibitive to comprehensively test. Hence, this paper attempts to evaluate one such system, by modeling its behavior. The model generated parameters not only allow for objective comparison between vehicles, but also provide a more complete understanding of the system. The model can also be used to extrapolate results by simulating other scenarios without the need for conducting more tests. In this paper, low speed automated driving (also known as Traffic Jam Assist (TJA)) is studied. This study focused on the longitudinal behavior of automated vehicles while following a lead vehicle (LV) in traffic jam scenarios.