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A vehicle model is an important factor in the development of vehicle control systems. Various vehicle models having different complexities, assumptions, and limitations have been developed and applied to many different vehicle control systems. A 14 DOF vehicle model that includes a roll center as well as non-linear effects due to vehicle roll and pitch angles and unsprung mass inertias, is developed. From this model, the limitations and validity of lower order models which employ different assumptions for simplification of dynamic equations are investigated by analyzing their effect on vehicle roll response through simulation. The possible limitation of the 14 DOF model compared to an actual vehicle is also discussed.

This paper provides a method whereby vehicle stopping performance can be specified, measured, and compared independently of the test surface. The method provides for an independent measure of the prevailing friction potential of the test surface which is used to normalize the measured stopping performance of the test vehicle. The concept presented is tailored toward a safety argument and toward rulemaking as a potential adaptation to braking effectiveness requirements which currently exist. A new mobile tire dynamometer, developed for this program, is discussed, as are the results of a demonstration test program.

This paper describes a case study led by Science Applications International Corporation (SAIC) of Dayton, OH USA and Dearborn Group Inc. to prove the feasibility of employing Telematics technologies to the vehicle test and measurement industry. Many test functions can be automated through the use of secure wireless technologies. For example, vehicle data can be dynamically monitored on the vehicle and data meeting pre-determined criteria could be downloaded via the wireless communications center. Additionally, central, real-time wireless monitoring of vehicle fleets provides the vehicle fleet manager with the ability to manage multiple tests simultaneously, thus improving efficiencies and potentially reducing manpower costs and compressing test schedules.

To advance vehicle lightweighting, chopped carbon fiber sheet molding compound (SMC) is identified as a promising material to replace metals. However, there are no effective tools and methods to predict the mechanical property of the chopped carbon fiber SMC due to the high complexity in microstructure features and the anisotropic properties. In this paper, a Representative Volume Element (RVE) approach is used to model the SMC microstructure. Two modeling methods, the Voronoi diagram-based method and the chip packing method, are developed to populate the RVE. The elastic moduli of the RVE are calculated and the two methods are compared with experimental tensile test conduct using Digital Image Correlation (DIC). Furthermore, the advantages and shortcomings of these two methods are discussed in terms of the required input information and the convenience of use in the integrated processing-microstructure-property analysis.

Acoustical materials are widely used in automotive vehicles and other industrial applications. Two important parameters namely Sound Transmission Loss (STL) and absorption coefficient are commonly used to evaluate the acoustical performance of these materials. Other parameters, such as insertion loss, noise reduction, and loss factors are also used to judge their performance depending on the application of these materials. A systematic comparative study of STL and absorption coefficient was conducted on various porous acoustical materials. Several dozen materials including needled cotton fiber (shoddy) and foam materials with or without barrier/scrim were investigated. The results of STL and absorption coefficient are presented and compared. As expected, it was found that most of materials are either good in STL or good in absorption. However, some combinations can achieve a balance of performance in both categories.

Ethanol fuel has received renewed attention in recent years because of its oxygenate content and its potential to reduce greenhouse gas emissions from spark ignition engines. The economic impact on farm industry has been one of the drivers for its use in engines in the U.S. Although ethanol, in various blends, has been used in automotive engines for almost a decade the fuel has seldom been utilized in off-highway engines where the fuel systems are not well controlled. This investigation was conducted to evaluate exhaust emissions and combustion characteristics of E85 fuel in an off-highway engine used in farm equipment. A single-cylinder, four-stroke, spark ignition engine equipped with a carburetor was used to investigate combustion and exhaust emissions produced by gasoline and blends of gasoline and ethanol fuels. The engine fuel system was modified to handle flow rates required by the engine. A variable size-metering orifice was used to control air-to-fuel ratios.

The purpose of present numerical study was to extend the operating range of alcohol (methanol and ethanol) fueled Homogeneous Charge Compression Ignition (HCCI) engine under low load conditions. Ignition of pure methanol and ethanol under HCCI mode of operation requires high intake temperatures and misfires at low loads are common in HCCI engines. Three methods have been adapted to optimize the use of methanol and ethanol for HCCI operation without increasing the intake temperature. First, blending methanol and ethanol with ignition improver, namely di-methyl ether (DME) and di-ethyl ether (DEE), was used to increase the cetane number and ignitability of premixed charge. Second, based on the blended fuels, the spark assistance was used to reduce required intake temperature for auto-ignition. Third, DME and DEE were directly injected to methanol and ethanol operated HCCI engine, in the form of Reactivity Controlled Compression Ignition (RCCI) combustion.

Available methodologies for model bias identification are mainly regression-based approaches, such as Gaussian process, Bayesian inference-based models and so on. Accuracy and efficiency of these methodologies may degrade for characterizing the model bias when more system inputs are considered in the prediction model due to the curse of dimensionality for regression-based approaches. This paper proposes a copula-based approach for model bias identification without suffering the curse of dimensionality. The main idea is to build general statistical relationships between the model bias and the model prediction including all system inputs using copulas so that possible model bias distributions can be effectively identified at any new design configurations of the system. Two engineering case studies whose dimensionalities range from medium to high will be employed to demonstrate the effectiveness of the copula-based approach.

Motor vehicle crashes involving child Vulnerable Road Users (VRUs) remain a critical public health concern in the United States. While previous studies successfully utilized the crash scenario typology to examine traffic crashes, these studies focus on all types of motor vehicle crashes thus the method might not apply to VRU crashes. Therefore, to better understand the context and causes of child VRU crashes on the U.S. road, this paper proposes a multi-step framework to define crash scenario typology based on the Fatality Analysis Reporting System (FARS) and the Crash Report Sampling System (CRSS). A comprehensive examination of the data elements in FARS and CRSS was first conducted to determine elements that could facilitate crash scenario identification from a systematic perspective. A follow-up context description depicts the typical behavioral, environmental, and vehicular conditions associated with an identified crash scenario.

This paper presents a distributed control system framework for next-generation automotive control systems, in which various control units are connected with CAN bus. The framework is a software platform that performs communication between control units and invocation of application programs. The framework includes necessary functions for data transmission to meet end-to-end timing constraints in distributed control systems. Application programmers don't have to write any communication procedure but focus on developing application programs with appropriate API (Application Program Interface). The framework is based on driving force management and also OSEK, which is a standard real-time operating system (OSEK-OS) and a communication protocol (CAN) for automotive control. We are now applying our prototype framework to an adaptive cruise control system in our experimental vehicle.

Developing a new technology requires decision-makers to understand the technology's implications on an organization's objectives, which depend on user needs targeted by the technology. If these needs are common between two organizations, collaboration could result in more efficient technology development. For hybrid truck design, both commercial manufacturers and the military have similar performance needs. As the new technology penetrates the truck market, the commercial enterprise must quantify how the hybrid's superior fuel efficiency will impact consumer purchasing and, thus, future enterprise profits. The Army is also interested in hybrid technology as it continues its transformation to a more fuel-efficient force. Despite having different objectives, maximizing profit and battlefield performance, respectively, the commercial enterprise and Army can take advantage of their mutual needs.

Due to magnesium alloy's poor weldability, other joining techniques such as laser assisted self-piercing rivet (LSPR) are used for joining magnesium alloys. This research investigates the fatigue performance of LSPR for magnesium alloys including AZ31 and AM60. Tensile-shear and coach peel specimens for AZ31 and AM60 were fabricated and tested for understanding joint fatigue performance. A structural stress - life (S-N) method was used to develop the fatigue parameters from load-life test results. In order to validate this approach, test results from multijoint specimens were compared with the predicted fatigue results of these specimens using the structural stress method. The fatigue results predicted using the structural stress method correlate well with the test results.

The combustion of dual-fuel engines usually uses a pilot flame to burn out a background fuel inside a cylinder under high compression. The background fuel can be either a gaseous fuel or a volatile liquid fuel, commonly with low reactivity to prevent premature combustion and engine knocking; whereas the pilot flame is normally set off with the direct injection of a liquid fuel with adequate reactivity that is suitable for deterministic auto-ignition with a high compression ratio. In this work, directly injected butanol is used to generate the pilot flame, while intake port injected ethanol or butanol is employed as the background fuel. Compared with the conventional diesel-only combustion, dual-fuel operations not only broaden the fuel applicability, but also enhance the potential for clean combustion, in high efficiency engines. The amount of background fuel and the scheduling of pilot flame are investigated through extensive laboratory experiments.

In this paper, a new algorithm, named Nonlinear Optimization Method (NOM) has been mathematically and computationally developed for several geometric elements. The initial condition of the NOM is obtained by LSM, then the minimum zone is optimized in accordance with tolerancing principles in ANSI Y14.5.1M. The results are verified to be the Minimum Zone Evaluation (MZE) for the inspected geometric features. The algorithm, together with its computational realization programs, are proved to be considerably reliable and robust for practical applications.

The fidelity of the hybrid electric vehicle simulation is increased with the integration of a computationally-efficient finite-element based electric machine model, in order to address optimization of component design for system level goals. In-wheel electric motors are considered because of the off-road military application which differs significantly from commercial HEV applications. Optimization framework is setup by coupling the vehicle simulation to the constrained optimization solver. Utilizing the increased design flexibility afforded by the model, the solver is able to reshape the electric machine's efficiency map to better match the vehicle operation points. As the result, the favorable design of the e-machine is selected to improve vehicle fuel economy and reduce cost, while satisfying performance constraints.

Modeling tailpipe NOx emissions has always been difficult due to the complexity of the numerous factors involved in the catalytic conversion of the pollutant. Most emissions modeling has been based on steady state driving. A parameterized algebraic model for second-by-second tailpipe emissions of NOx for a composite Tier 1 car is presented employing data from the Federal Test Procedure Revision Project (FTPRP). Calculating fuel rate from measured engine out values, the catalytic converter is physically modeled based on the fuel rate history and a few fitted parameters. Under certain conditions, the changes in fuel rate are related to trends in the air to fuel ratio. The model accurately predicts the time dependence of hot stabilized tailpipe NOx emissions in the FTP bag 3 and US06 driving cycles. Modeling of low power driving, as in bag 2, is not as successful.

In recent years, with the increase of traffic accidents and traffic jams, lane change, as one of the most important and commonly automatic driving operations for autonomous vehicles, is receiving attention in academia. It is considered to be one of the important solutions that play an important role in improving road traffic safety and efficiency. However, most existing lane-changing models are rule-based lane-changing models. These models only assume a one-direction impact of surrounding vehicles on the lane-changing vehicle. In fact, lane change is a process of mutual interaction between vehicles due to the complexity and uncertainty of the traffic environment. Moreover, the safety and efficiency of existing lane-changing decision algorithms need to be improved. In this paper, we proposed a multivehicle cooperative control approach with a distributed control structure to control the model.

A global, systems-level model which characterizes the thermal behavior of internal combustion engines is described in this paper. Based on resistor-capacitor thermal networks, either steady-state or transient thermal simulations can be performed. A two-zone, quasi-dimensional spark-ignition engine simulation is used to determine in-cylinder gas temperature and convection coefficients. Engine heat fluxes and component temperatures can subsequently be predicted from specification of general engine dimensions, materials, and operating conditions. Emphasis has been placed on minimizing the number of model inputs and keeping them as simple as possible to make the model practical and useful as an early design tool. The success of the global model depends on properly scaling the general engine inputs to accurately model engine heat flow paths across families of engine designs. The development and validation of suitable, scalable submodels is described in detail in this paper.

Percussive riveting is a widely used way of fastening in the field of aircraft assembly, which used to be done manually. Nowadays, replacing the traditional percussive riveting with automated percussive riveting becomes a trend worldwide, which improves the quality of riveting significantly. For the automated riveting system used in aircraft assembly, reliability is of great importance, deserving to be deeply researched and fully enhanced. In this paper, a high reliable automated percussive riveting system integrated into a dual robot drilling and riveting system is proposed. The riveting system consists of the hammer part and the bucking bar part. And both parts have been optimized to enhance the reliability. In the hammer side, proximity switches are fully used to detect the state of rivet insertion.

The components in a hybrid electric vehicle (HEV) powertrain include the battery pack, an internal combustion engine, and the electric machines such as motors and possibly a generator. These components generate a considerable amount of heat during driving cycles. A robust thermal management system with advanced controller, designed for temperature tracking, is required for vehicle safety and energy efficiency. In this study, a hybridized mid-size truck for military application is investigated. The paper examines the integration of advanced control algorithms to the cooling system featuring an electric-mechanical compressor, coolant pump and radiator fans. Mathematical models are developed to numerically describe the thermal behavior of these powertrain elements. A series of controllers are designed to effectively manage the battery pack, electric motors, and the internal combustion engine temperatures.