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A prototype 2007 ISL Cummins diesel engine equipped with a diesel oxidation catalyst (DOC), diesel particle filter (DPF), variable geometry turbocharger (VGT), and cooled exhaust gas recirculation (EGR) was tested at Southwest Research Institute (SwRI) under a high-load accelerated durability cycle for 1000 hours with B20 soy-based biodiesel blends and ultra-low sulfur diesel (ULSD) fuel to determine the impact of B20 on engine durability, performance, emissions, and fuel consumption. At the completion of the 1000-hour test, a thorough engine teardown evaluation of the overhead, power transfer, cylinder, cooling, lube, air handling, gaskets, aftertreatment, and fuel system parts was performed. The engine operated successfully with no biodiesel-related failures. Results indicate that engine performance was essentially the same when tested at 125 and 1000 hours of accumulated durability operation.

An evaluation methodology has been developed for assessing the suitability of R-1234yf in vehicles. This relates primarily to evaluating the flammability of R-1234yf in the engine compartment during a frontal collision. This paper will discuss the process followed in the methodology, the technical rationale for this process, and the results of the analysis. The specific types of analysis included in the methodology are: exhaust-system thermal characterization, computer simulated crash tests, actual crash tests, teardown and examination of crashed parts, and releases of refrigerant onto hot exhaust manifolds. Each type of analysis was logically ordered and combined to produce a comprehensive evaluation methodology. This methodology has been applied and demonstrates that R-1234yf is difficult to ignite when factors that occur in frontal crashes are simultaneously considered.

This investigation utilizes a DFSS analysis approach to determine automatic transmission gear content required to minimize fuel consumption for various powertrain - vehicle systems. L18 and L27 inner arrays with automatic transmission design and shift pattern constraint parameters were varied to determine their relative influence on fuel consumption. An outer noise array consisting of two vehicles with various engines, final drive ratios and legislated emissions test cycles was used to make a robust transmission selection based on minimizing fuel consumption. The full details of the DFSS analysis method and assumptions are presented along with a detailed examination of the results. With respect to transmission design parameters, parasitic spinloss and gear mesh efficiency were found to be most important followed by the number of gears. The DFSS analysis further revealed that unique transmission design formulations are potentially required for widely varying engines.

Accurately characterizing vehicle drive cycles plays a fundamental role in assessing the performance of new vehicle technologies. Repeatable, short duration representative drive cycles facilitate more informed decision making, resulting in improved test procedures and more successful vehicle designs. With continued growth in the deployment of onboard telematics systems employing global positioning systems (GPS), large scale, low cost collection of real-world vehicle drive cycle data has become a reality. As a result of these technological advances, researchers, designers, and engineers are no longer constrained by lack of operating data when developing and optimizing technology, but rather by resources available for testing and simulation. Experimental testing is expensive and time consuming, therefore the need exists for a fast and accurate means of generating representative cycles from large volumes of real-world driving data.

A GT-Power Fast Run Model simplified from detail model for HIL is verified with a bench test using the dSPACE Simulator. Firstly, the conversion process from a detailed model to FRM model is briefly described. Then, the spark timing, fuel pulse with control for FAR, and torque level control are developed for proof of concept. Moreover a series of FRM/Simulink co-simulation and HIL tests are conducted. In the summary, the test results are presented and compared with GT detailed model simulations. The test results show that the FRM/dSPACE HIL stays consistent in most variables of interest under 0.7-0.9 real-time factor condition between 1000 - 5000 RPM. The same steady-state can be reached by RCP controllers or with GT-Power internal controllers. The transient states are close using different control algorithm. The main purpose of HIL application is achieved, despite inconsistencies in performance data like fuel consumption.

Accurate evaluation of vehicles' transient total power requirement helps achieving further improvements in vehicle fuel efficiency. When operated, the air-conditioning (A/C) system is the largest auxiliary load on a vehicle, therefore accurate evaluation of the load it places on the vehicle's engine and/or energy storage system is especially important. Vehicle simulation models, such as "Autonomie," have been used by OEMs to evaluate vehicles' energy performance. However, the load from the A/C system on the engine or on the energy storage system has not always been modeled in sufficient detail. A transient A/C simulation tool incorporated into vehicle simulation models would also provide a tool for developing more efficient A/C systems through a thorough consideration of the transient A/C system performance. The dynamic system simulation software MATLAB/Simulink® is frequently used by vehicle controls engineers to develop new and more efficient vehicle energy system controls.

A rough road ride assessment provides an insightful evaluation of vehicle responses beyond the frequency range of suspension or steering modes. This is when body structure influence on the vehicle performance can be detected by vehicle occupants. In this paper, a rough road is used to evaluate vehicle ride performance and multi-body simulation (MBS) models are developed along with finite-element (FE) representations of the vehicle body and structure. To produce high fidelity simulation results in the frequency range of interest, various vehicle subsystem modeling contents are examined. A case study of a vehicle model with two different structures is provided. Time histories and frequency based analyses are used to obtain insights into the effects of body structure on vehicle responses. Finally, two metrics (‘Isolation’ and ‘Shake’) are used to distinguish the vehicle ride performance.

This paper presents a methodology to represent automatic transmission clutch-to-clutch shift dynamics with a two degree of freedom, lumped inertia model. The method of reducing the automatic transmission to a lumped, two inertia model as a function of shift and input shaft acceleration is detailed using a full kinematic representation of the automatic transmission. For a given clutch-to-clutch shift maneuver there are two dependent equations that utilize the two lumped inertias and represent the response of the transmission system from input to output shaft. Applicability of the method is shown for planetary automatic and layshaft dual clutch transmissions. Typical clutch-to-clutch shift maneuvers are illustrated with the two inertia model for power on upshifts and downshifts.

Increasing fuel costs and the desire for reduced dependence on foreign oil has brought the diesel engine to the forefront of future medium-duty vehicle applications in the United States due to its higher thermal efficiency and superior durability. The main obstacle to the increased use of diesel engines in this platform is the upcoming extremely stringent, Tier 2 emission standard. In order to succeed, diesel vehicles must comply with emissions standards while maintaining their excellent fuel economy. The availability of technologies such as common rail fuel injection systems, low sulfur diesel fuel, NOX adsorber catalysts (NAC), and diesel particle filters (DPFs) allow the development of powertrain systems that have the potential to comply with these future requirements. In meeting the Tier 2 emissions standards, the heavy light-duty trucks (HLDTs) and medium-duty passenger vehicles (MDPVs) will face the greatest technological challenges. In support of this, the U.S.

Increasing fuel costs and the desire for reduced dependence on foreign oil has brought the diesel engine to the forefront of future medium-duty vehicle applications in the United States due to its higher thermal efficiency and superior durability. The main obstacle to the increased use of diesel engines in this platform is the upcoming extremely stringent, Tier 2 emission standard. In order to succeed, diesel vehicles must comply with emissions standards while maintaining their excellent fuel economy. The availability of technologies such as common rail fuel injection systems, low sulfur diesel fuel, NOX adsorber catalysts (NAC), and diesel particle filters (DPFs) allow the development of powertrain systems that have the potential to comply with these future requirements. In meeting the Tier 2 emissions standards, the heavy light-duty trucks (HLDTs) and medium-duty passenger vehicles (MDPVs) will face the greatest technological challenges. In support of this, the U.S.

In automotive noise control, the hood liner is an important acoustic part for mitigating engine noise. The random incidence absorption coefficient is used to quantify the component level acoustic performance. Generally, air gaps, type of substrate materials, density of the substrate materials and Air Flow Resistivity (AFR) of the cover scrim are the dominant control factors in the sound absorption performance. This paper describes a systematic experimental investigation of how these control factors affect flat sample performance. The first stage of this study is full factorial measurement based on current available solutions from sound absorber suppliers. The acoustic absorption of different hood liner constructions, with variations in materials, density, air gaps, and scrims was measured.

Aerodynamic vehicle design improvements require flow simulation driven iterative shape changes. The 3-D flow field simulations (CFD analysis) are not explicitly descriptive in providing the direction for aerodynamic shape changes (reducing drag force or increasing the down-force). In recent times, aerodynamic shape optimization using the adjoint method has been gaining more attention in the automotive industry. The traditional DOE (Design of Experiment) optimization method based on the shape parameters requires a large number of CFD flow simulations for obtaining design sensitivities of these shape parameters. The large number of CFD flow simulations can be significantly reduced if the adjoint method is applied. The main purpose of the present study is to demonstrate and validate the adjoint method for vehicle aerodynamic shape improvements.

This paper presents the most recent advancement in the vehicle development process using the one-step or auto Transfer Path Analysis (TPA) in conjunction with the superelement, component mode synthesis, and automated multi-level substructuring techniques. The goal is to identify the possible ways of energy transfer from the various sources of excitation through numerous interfaces to given target locations. The full vehicle model, consists of superelements, has been validated with the detailed system model for all loadcases. The forces/loads can be from rotating components, powertrain, transfer case, chain drives, pumps, prop-shaft, differential, tire-wheel unbalance, road input, etc., and the receiver can be at driver/passenger ears, steering column/wheel, seats, etc. The traditional TPA involves two solver runs, and can be fairly complex to setup in order to ensure that the results from the two runs are consistent with subcases properly labeled as input to the TPA utility.

In a math-based control algorithm design, model-based simulation and testing are very important as an integral part of design process. There are many advantages of using modeling and simulation in the algorithm design. In this paper, Algorithm-in-the-Loop and Hardware-in-the-Loop approaches are adopted for a transmission control algorithm development. A practical approach is introduced on how to test the control algorithms with a reliable plant (virtual engine, transmission, and vehicle) model in the closed-loop simulation. In using combination of open-loop and closed-loop simulations, various key behavior test cases are developed and documented for the success of control algorithms development. Secondly, the same test cases are reused and verified against the production codes, which are automatically generated from the math-based control algorithm models.

Fleet managers need a tool to assist them in assessing their need to comply with EPAct and to provide them with the ability to obtain information that will allow them to make alternative fuel vehicle purchasing decisions. This paper will describe the Web-based tool that will inform a fleet manager, based on their geographic location, the type of fleet they own or operate, and the number and types of vehicles in their fleet, whether or not they need to meet the requirements of EPAct, and, if so, the percentage of new vehicle purchases needed to comply with the law. The tool provides detailed specifications on available OEM alternative fuel vehicles, including the purchase cost of the vehicles, fuel and fuel system characteristics, and incentives and rebates surrounding the purchase of each vehicle. The full set of federal, state, and local incentives is made available through the tool, as well as detailed access to refueling site and dealership locations.

Response Surface Models are often used as a surrogate for expensive black-box functions during optimization to reduce computational cost. Often, the CAE analysis models are highly nonlinear and multi-modal. A response surface approximation of such analysis as a result is highly multi-modal; i.e. it contains multiple local optima. A gradient-based optimizer working with such a response surface will often converge to the nearest local optimum. There does not exist any method to guarantee a global optima for non-convex multi-modal functions. For such problems, we propose an efficient algorithm to find as many distinct local optima as possible. The proposed method is specifically designed to work in large dimensions (about 100 ∼ 1000 design variables and similar number of constraints) and can identify most of the locally optimal solutions in a reasonable amount of time.

Cummins Westport Inc. (CWI) released for production the latest version of its C8.3G natural gas engine, the C Gas Plus, in July 2001. This engine has increased ratings for horsepower and torque, a full-authority engine controller, wide tolerance to natural gas fuel (the minimum methane number is 65), and improved diagnostics capability. The C Gas Plus also meets the California Air Resources Board optional low-NOx (2.0 g/bhp-h) emission standard for automotive and urban buses. Two pre-production C Gas Plus engines were operated in a Viking Freight fleet for 12 months as part of the U.S. Department of Energy's Fuels Utilization Program. In-use exhaust emissions, fuel economy, and fuel cost were collected and compared with similar 1997 Cummins C8.3 diesel tractors. CWI and the West Virginia University developed an ad-hoc test cycle to simulate the Viking Freight fleet duty cycle from in-service data collected with data loggers.

Diesel particulate filter (DPF) is a widely used emission control device on diesel vehicles. The DPF captures the particulate matter coming from the engine exhaust and periodically burns the collected soot via the regeneration process. There are various trigger mechanisms for this regeneration, such as distance, time, fuel and simulation. Another method widely used in the industry is the pressure drop across the filter. During calibration, relation between the pressure sensor reading and soot mass in the filter is established. This methodology is highly effective in successful DPF operation as pressure sensor is a live signal that can account for any changes in engine performance over time or any unforeseen hardware failures. On the other hand, any erroneous feedback from the sensor can lead to inaccurate soot mass prediction causing unnecessary regenerations or even needless DPF plugging concerns.

A dog clutch, if successfully implemented in an automatic transmission, provides better packaging and the potential for improved fuel economy. The technical requirements for this concept are examined through modeling and simulation. As a first step, a physics-based component level model is developed that provides an understanding of the basic contact and impact dynamics. The model is compared to a built-in AMESim block to establish confidence. This component level model is then integrated into a powertrain system model within the AMESim environment. As a test bed, the powertrain model is exercised to simulate a friction plate to dog clutch shift in a 6-speed automatic transmission. The analysis helps to define the slip speed target at the onset of the dog clutch engagement while ensuring shift requirements are met. Finally, the model is validated by comparing the simulated results with measured dynamometer data.

The Computer-Aided Engineering of Automotive Batteries (CAEBAT) Phase 1 project is a U.S. Department of Energy-funded, multi-year project which is aimed at developing a complete CAE tool set for the automotive battery pack design. This paper reports the application of the full field approach of the CAEBAT which is developed by the General Motors-led industry team, for a 24-cell liquid-cooled prototype battery pack. It also summarizes the verification of the approach by comparing the simulation results with the measurement data. The simulation results using the Full Field Approach are found to have a very good agreement with the measurement data.