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A combination of laboratory reactor measurements and vehicle FTP testing has been combined to demonstrate a method for diagnosing the formation of NO2 from a diesel oxidation catalyst (DOC). Using small cores from a production DOC and simulated diesel exhaust, the laboratory reactor experiments are used to support a model for DOC chemical reaction kinetics. The model we propose shows that the ability to produce NO2 is chemically linked to the ability of the catalyst to oxidize hydrocarbon (HC). For thermally damaged DOCs, loss of the HC oxidation function is simultaneous with loss of the NO2 production function. Since HC oxidation is the source of heat generated in the DOC under regeneration conditions, we conclude that a diagnostic of the DOC exotherm is able to detect the failure of the DOC to produce NO2. Vehicle emissions data from a 6.6 L Duramax HD pick-up with DOC of various levels of thermal degradation is provided to support the diagnostic concept.

In order to extend the operability limit of the gasoline compression ignition (GCI) engine, as an avenue for low temperature combustion (LTC) regime, the effects of parametric variations of engine operating conditions on the performance of six-stroke GCI (6S-GCI) engine cycle are numerically investigated, using an in-house 3D CFD code coupled with high-fidelity physical sub-models along with the Chemkin library. The combustion and emissions were calculated using a skeletal chemical kinetics mechanism for a 14-component gasoline surrogate fuel. Authors’ previous study highlighted the effects of the variation of injection timing and split ratio on the overall performance of 6S-GCI engine and the unique mixing-controlled burning mode of the charge mixtures during the two additional strokes. As a continuing effort, the present study details the parametric studies of initial gas temperature, boost pressure, fuel injection pressure, compression ratio, and EGR ratio.

Increasing regulatory demand for efficiency has led to development of novel combustion modes such as HCCI, GCI and RCCI for gasoline light duty engines. In order to realize HCCI as a compression ignition combustion mode system, in-cylinder compression temperatures must be elevated to reach the autoignition point of the premixed fuel/air mixture. This should be co-optimized with appropriate fuel formulations that can autoignite at such temperatures. CFD combustion modeling is used to model the auto ignition of gasoline fuel under compression ignition conditions. Using the fully detailed fuel mechanism consisting of thousands of components in the CFD simulations is computationally expensive. To overcome this challenge, the real fuel is represented by few major components of create a surrogate fuel mechanism. In this study, 9 variations of gasoline fuel sets were chosen as candidates to run in HCCI combustion mode.

Gasoline compression ignition (GCI) technology shows the potential to obtain high thermal efficiencies while maintaining low soot and NOx emissions in light-duty engine applications. Recent experimental studies and numerical simulations have indicated that high reactivity gasoline-like fuels can further enable the benefits of GCI combustion. However, there is limited empirical data in the literature studying the gasoline compression ignition process at relevant in-cylinder conditions, which are required for further optimizing combustion system designs. This study investigates the temporal and spatial evolution of the compression ignition process of various high reactivity gasoline fuels with research octane numbers (RON) of 71, 74 and 82, as well as a conventional RON 97 E10 gasoline fuel. A ten-hole prototype gasoline injector specifically designed for GCI applications capable of injection pressures up to 450 bar was used.

In-cylinder ion sensing is a subject of interest due to its application in spark-ignited (SI) engines for feedback control and diagnostics including: combustion knock detection, rate and phasing of combustion, and mis-fire On Board Diagnostics (OBD). Further advancement and application is likely to continue as the result of the availability of ignition coils with integrated ion sensing circuitry making ion sensing more versatile and cost effective. In SI engines, combustion knock is controlled through closed loop feedback from sensor metrics to maintain knock near the borderline, below engine damage and NVH thresholds. Combustion knock is one of the critical applications for ion sensing in SI engines and improvement in knock detection offers the potential for increased thermal efficiency. This work analyzes and characterizes the ionization signal in reference to the cylinder pressure signal under knocking and non-knocking conditions.

A comprehensive experimental and theoretical approach was undertaken to understand the phenomenon of pre-ignition and to assess parameters to improve or even eliminate it completely. Oil mixing with fuel was identified as the leading theory of self ignition of the fuel. End of compression temperature has to meet a minimum level for pre-ignition to take place. In this work a comprehensive list of parameters were identified that have a direct and crucial role in the onset of pre-ignition including liner wetting, injection targeting, stratification, mixture motion and oil formulation. Many secondary effects were identified including ring dynamics, ring tension, spark plug electrode temperature and coolant temperature. CFD has been extensively used to understand test results including wall film, A/F ratio distribution and temperature at the end of compression when looked at in the context of fuel evaporation and mixing.

An investigation of high speed direct injection (DI) compression ignition (CI) engine combustion fueled with gasoline injected using a triple-pulse strategy in the low temperature combustion (LTC) regime is presented. This work aims to extend the operation ranges for a light-duty diesel engine, operating on gasoline, that have been identified in previous work via extended controllability of the injection process. The single-cylinder engine (SCE) was operated at full load (16 bar IMEP, 2500 rev/min) and computational simulations of the in-cylinder processes were performed using 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 chosen to match ignition characteristics of the gasoline fuel used for the SCE experiments.

With deflected-disc dampers, digressive force-velocity shapes are achieved via the combined effects of disc stack stiffness and hub-offset. The degree of digressiveness can be adjusted to alter vehicle performance by changing the proportion of these parameters. Optimizing this relationship can yield substantial vehicle performance improvements, but the time consuming iterative process of developing a new disc stack for each hub-offset discourages experimentation. To enable more efficient digressiveness comparisons, a regression-based computational method has been developed which converts disc stack stiffness from one hub-offset to other offsets directly, without iteration. Once an initial disc stack for one offset has been tuned by traditional methods, stacks for other offsets can be calculated that maintain overall damper control.

This paper evaluates the potential of adding higher alcohols to gasoline blendstock in an attempt to improve overall fuel performance. The alcohols considered include ethanol, normal- and iso-structures of propanol, butanol and pentanol as well as normal-hexanol (C2-C6). Fuel performance is quantified based on energy content, knock resistance as well as petroleum displacement and promising multi-component blends are systematically identified based on property prediction methods. These promising multi-component blends, as well as their respective reference fuels, are subsequently tested for efficiency and emissions performance utilizing a gasoline direct injection, spark ignition engine. The engine test results confirm that combustion and efficiency of tailored multi-component blends closely match those of the reference fuels. Regulated emissions stemming from combustion of these blends are equal or lower compared to the reference fuels across the tested engine speed and load regime.

Corrosion inhibitors (CIs) have been used for years to protect the supply and distribution hardware used for transportation of fuel from refineries and to buffer the potential organic acids present in an ethanol blended fuel to enhance storage stability. The impact of these inhibitors on spark-ignition engine fuel systems, specifically intake valve deposits, is known and presented in open literature. However, the relationship of the corrosion inhibitors to the powertrain intake valve deposit performance is not understood. This paper has two purposes: to present and discuss a second market place survey of corrosion inhibitors and how they vary in concentration in the final blended fuel, specifically E85 (Ethanol Fuel Blends); and, to show how the variation in the concentrations of the components of the CIs impacts the operation and performance of vehicles, specifically, the effects on intake valve deposit formation.

This study investigates a methodology in which the general public's subjective interpretation of vehicle handling and performance can be predicted. Several vehicle handling measurements were acquired, and associated metrics calculated, in a controlled setting. Human evaluators were then asked to drive and evaluate each vehicle in a winter driving school setting. Using the acquired data, multiple linear regression and artificial neural network (ANN) techniques were used to create and refine mathematical models of human subjective responses. It is shown that artificial neural networks, which have been trained with the sets of objective and subjective data, are both more accurate and more robust than multiple linear regression models created from the same data.

Engineering has continuously strived to improve the vehicle development process to achieve high quality designs and quick to launch products. The design process has to have the tools and capabilities to help ensure both quick to the market product and a flawless launch. To achieve high fidelity and robust design, mistakes and other quality issues must be addressed early in the engineering process. One way to detect problems early is to use the math based modeling and simulation techniques of the analysis group. The correlation of the actual vehicle performance to the predictive model is crucial to obtain. Without high correlation, the change management process begins to get complicated and costs start to increase exponentially. It is critical to reduce and eliminate the risk in a design up front before tooling begins to kick off. The push to help achieve a high rate of correlation has been initiated by engineering management, seeing this as an asset to the business.

As an innovative electric vehicle with some new approaches to energy usage and vehicle performance balance, the Chevy Volt required a special relationship between the OEM and tire supplier community. This paper details this relationship and how advanced tools and technology were leveraged between OEM and supplier to achieve tire component and overall vehicle performance results.

In order to assess the possible ways of energy transfer from the various sources of excitation in a vehicle assembly to a given target location, frequency based substructuring technique and transfer path analysis are used. These methods help to locate the most important energy transfer paths for a specific problem, and to evaluate their individual effects on the target, thus providing valuable insight into the mechanisms responsible for the problem. The Source-Path-Receiver concept is used. The sources can be from the road surface, engine, transmission, transfer case, prop-shaft, differential, rotating components, chain drives, pumps, etc., and the receiver can be driver/passenger ears, steering column, seats, etc. This paper is devoted to identify the noise transfer paths and the force transmissibility among the interfaces of different components in the vehicle for the low to mid frequency range.

For many years, the computer aided math model has been the foundation for lowering cost and reducing time to market for many manufacturing industries. The automotive industry applies a variety of tools and methods to evaluate the expected vehicle performance to a forever expanding set of requirements. These mathematical predictions of performance are then repeated for both a set of design cycles and a multitude of vehicles in the product portfolio. This paper presents a CAE perspective of the unique problems of the large scale virtual vehicle engineering factory and a set of solutions. Different strategies to create the various complex math models required are explored. These strategies include using COTS FEA pre-processers, producing FEA models internal to the CAD tools, as well as custom built tools, macros and process automation tools.

Corrosion inhibitors (CIs) have been used for years to protect the supply and distribution hardware used for transportation of fuel from refineries. The impact of these inhibitors on spark ignited fuel systems, specifically intake valve deposits, is known and presented in open literature. However, the relationship of the additive concentrations to the powertrain intake valve deposit performance is not understood. This paper has two purposes: to present and discuss a market place survey of corrosion inhibitors and how they vary in concentration in the final blended fuel; and, to show how the variation in the concentrations of the CIs impact the operation and performance of vehicles, specifically, the effects on intake valve deposit formation. Commercially available corrosion inhibitor packages for both gasoline and ethanol blended fuels, specifically E85 fuels, were studied for their chemical compositions, and their impact on valves for a port fuel injection (PFI) engine.

Brake Assist System (BAS) requirements have been established by the Economic Commission for Europe (ECE) in R13H. Electronic Stability Control (ESC) systems typically have the value added function of Panic Brake Assist (PBA) which is defined as a Category C (sensitive to multiple criteria) Brake Assist System. PBA is designed to force the vehicle into Antilock Brake System (ABS) and to maintain ABS control when the driver spikes the brake pedal and then temporarily reduces brake pedal force before reasserting more brake pedal force. ECE test protocol requires the use of brake ramp applications to define the mean acceleration force (maF) curve which is used to define the brake pedal force where ABS activates (FABS). After completing the brake ramp application test maneuvers and completing the data processing to define the maF curve, FABS, upper, and FABS, lower, the test driver then proceeds to run the panic brake assist portion of the test.

The electrical architecture design of a rear back glass defrost grid system encompasses many critical criteria that must be integrated into the design. For example, the defrost clip location and interface to the glass must meet all vehicle performance requirements. If the defrost clip to the glass interface is not resistant to vibration and relative movement, detachment and loss of function can occur. This paper describes a back glass defrost clip with a solder joint that is robust to manufacturing variations and customer usage conditions. A designed experiment using Design for Six Sigma methodologies was performed to understand the effects of the attachment interface to the electrical wiring pigtail, and parameters that affect performance. The working constraints, testing set up, validation, and root cause investigation of the clip detachment phenomenon is covered in this paper.

Engine knock has been studied extensively over the years. Its undesired effects on drivability, its potential to damage an engine, and its impact on limiting the compression ratio are the main reasons why it remains a current topic of research. This paper focuses on exploiting the connection between auto-ignition and knock. A new method based on the frequency analysis of the heat release traces is proposed to detect and estimate auto-ignition/knock robustly. Filtering the heat release signal with the appropriate bandwidth is crucial to avoid misdetection. The filter settings used in this paper are found using spectral analysis of the heat release signal. By using the proposed method, it is possible to detect auto-ignition/knock even under the presence of undesired sensor resonance effects and noise from mechanical and electrical sources.

The characteristics of combustion knock metrics over a number of engine cycles can be an essential reference for knock detection and control in internal combustion engines. In a Spark-Ignition (SI) engine, the stochastic nature of combustion knock has been shown to follow a log-normal distribution. However, this has been derived from experiments done with gasoline only and applicability of log-normal distribution to dual-fuel combustion knock has not been explored. To evaluate the effectiveness and accuracy of log-normal distributed knock model for methane-gasoline blended fuel, a sweep of methane-gasoline blend ratio was conducted at two different engine speeds. Experimental investigation was conducted on a single cylinder prototype SI engine equipped with two fuel systems: a direct injection (DI) system for gasoline and a port fuel injection (PFI) system for methane.