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The 2005 Ford GT high performance sports car was designed and built in keeping with the heritage of the 1960's LeMans winning GT40 while maintaining the image of the 2002 GT40 concept vehicle. This paper reviews the technical challenges in designing and building a super car in 12 months while meeting customer expectations in performance, styling, quality and regulatory requirements. A team of dedicated and performance inspired engineers and technical specialists from Ford Motor Company Special Vehicle Teams, Research and Advanced Engineering, Mayflower Vehicle Systems, Roush Industries, Lear, and Saleen Special Vehicles was assembled and tasked with designing the production 2005 vehicle in record time.

“There's nothing new under the sun,” the old proverb says. But you only have to read a magazine, scan a periodical, listen to the radio, watch television, or glance at the multitude of ads that promise that such and such product is the latest trend or has up-to-date, state-of-the-art technology, to seemingly prove the old proverb wrong. However, old proverbs become old because they withstand the test of time. In this case, a hasty judgement should be withheld. Currently, as in the past, the above holds true for car radios as well. Whether in the United States, Europe, Canada or Latin America, the public has always been susceptible to last minute technological advances. It is curious then, that as far as car radio styling is concerned, their appearance has been typically rather conservative, and that it is only recently that styling has begun to change to be more in tune with the times.

Electrification is the well-accepted solution to address carbon emissions and modernize vehicle controls. Batteries play a critical in the journey of electrification and modernization with battery voltage prediction as the foundation for safe and efficient operation. Due to its strong dependency on prior information, battery voltage was estimated with recurrent neural network methods in the recent literatures exploring a variety of deep learning techniques to estimate battery behaviors. In these studies, standard recurrent neural networks, gated recurrent units, and long-short term memory are popular neural network architectures under review. However, in most cases, each neural network architecture is individually assessed and therefore the knowledge about comparative study among three neural network architecture is limited. In addition, many literatures only studied either the dynamic voltage response or the voltage relaxation.

New regulations from the state of California have established, for the first time, reactivity-based exhaust emissions standards for new vehicles and require that any clean alternative fuels needed by these vehicles be made available. Contained in these regulations are provisions for “reactivity adjustment factors” which will provide credit for vehicles which run on reformulated gasoline. The question arises: given two fuels of different chemical composition, but both meeting the criteria for CA Phase 2 gasoline (reformulated gasoline), how different might the specific reactivity of the exhaust hydrocarbons be? In this study we explored this question by examining the engine-out HC emissions from a single-cylinder version of the 5.4 L modular truck engine run on two different CA Phase 2 fuels.

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.

A cycle counting algorithm that will reduce a complex history into a series of discrete cycles is presented. The cycles determined by this technique are defined as closed stress-strain hysteresis loops of the type obtained from constant amplitude tests. Using the computer cycle counting algorithm, life predictions were made and compared with experimental results. These predictions were found to be typically within a ±3 factor of error. Also, the computer counting method was found to yield more accurate life predictions when compared to the histogram and range counting methods.

Vehicle sound quality has become an important basic performance requirement. Traditionally, automobile noise studies were focused on quietness. It is now necessary for the automobile to be more than quiet. The sound must be pleasing. This paper describes a development process to improve both vehicle noise level and sound quality. Formal experimental design techniques were utilized to quantify various hardware effects. A-weighted sound pressure level, Speech Intelligibility, and Composite Rating of Preference were the three descriptors used to characterize the vehicle's sound quality. Engineering knowledge augmented with graphical and statistical techniques were utilized during data analysis. The individual component contributions to each of the sound quality descriptors were also quantified in this study.

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.

A flow network method was developed to predict the underhood temperature distribution of an automobile. The method involves the solution of simplified energy and momentum equations of the air flow in control volumes defined by subdividing the air space between the surfaces of the underhood components and the front-end geometry. The control volumes are interconnected by ducts with branches and bends to form a flow network. Conservation of mass and momentum with appropriate pressure-loss coefficients leads to a system of algebraic equations to be solved for the flow rates through each volume. The computed flow rates are transferred to a thermal model to calculate the temperatures of the air and the major vehicle components that affect the underhood environment. The method was applied to a 1986 3.0L Taurus and compared with vehicle experiments conducted in a windtunnel.

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.

In this paper, a soft computing approach to a model-free vehicle stability control (VSC) algorithm is presented. The objective is to create a fuzzy inference system (FIS) that is robust enough to operate in a multitude of vehicle conditions (load, tire wear, alignment), and road conditions while at the same time providing optimal vehicle stability by detecting and minimizing loss of traction. In this approach, an adaptive neuro-fuzzy inference system (ANFIS) is generated using previously collected data to train and optimize the performance of the fuzzy logic VSC algorithm. This paper outlines the FIS detection algorithm and its benefits over a model-based approach. The performance of the FIS-based VSC is evaluated via a co-simulation of MATLAB/Simulink and CarSim model of the vehicle under various road and load conditions. The results showed that the proposed algorithm is capable of accurately indicating unstable vehicle behavior for two different types of vehicles (SUV and Sedan).

A hybrid approach to predict road-induced loads in vehicle structures is presented. The technique involves full vehicle dynamic simulation using measured wheel forces, absolute wheel vertical displacements, and steering angle as input. The wheel vertical displacement is derived from the measured wheel acceleration. This approach avoids the use of tire-road interface modeling. It also improves the conventional loads measuring process with minimum instrumentation and data acquisition. Existing load data from a test vehicle is used to validate this approach. Computed component loads show good agreement with measurements.

Vehicle structure crashworthiness design is one of the most challenging problems in product development and it has been studied for decades. Challenges still remain, which include developing a reliable and systematic approach for general crashworthiness design problems, which can be used to design an optimum vehicle structure in terms of topology, shape, and size, and for both structural layout and material layout. In this paper, an advanced and systematic approach is presented, which is called Magic Cube (MQ) approach for crashworthiness design. The proposed MQ approach consists of three major dimensions: Decomposition, Design Methodology, and General Considerations. The Decomposition dimension is related to the major approaches developed for the crashworthiness design problem, which has three layers: Time (Process) Decomposition, Space Decomposition, and Scale Decomposition.

There have been many articles published in the last decade or so concerning the components of an electronic stability control (ESC) system, as well as numerous statistical studies that attempt to predict the effectiveness of such systems relative to crash involvement. The literature however is free from papers that discuss how engineers might develop such systems in order to achieve desired steering, handling, and stability performance. This task is complicated by the fact that stability control systems are very complex and their designs and what they can do have changed considerably over the years. These systems also differ from manufacturer to manufacturer and from vehicle to vehicle in a given maker of automobiles. In terms of ESC hardware, differences can include all the components as well as the addition or absence of roll rate sensors or active steering gears to name a few.

Drivelines used in modern pickup trucks commonly employ universal joints. This type of joint is responsible for second driveshaft order vibrations in the vehicle. Large displacements of the joint connecting the driveline and the rear axle have a detrimental effect on vehicle NVH. As leaf springs are critical energy absorbing elements that connect to the powertrain, they are used to restrain large axle windup angles. One of the most common types of leaf springs in use today is the multi-stage parabolic leaf spring. A simple SAE 3-link approximation is adequate for preliminary studies but it has been found to be inadequate to study axle windup. A vast body of literature exists on modeling leaf springs using nonlinear FEA and multibody simulations. However, these methods require significant amount of component level detail and measured data. As such, these techniques are not applicable for quick sensitivity studies at design conception stage.

European legislation covering noise, smoke emission and industrial pollution, all seeking to improve the environment are not addressed in this paper, which takes only exhaust emission and fuel complexity as its subjects. It describes how this complexity can inhibit the development capacity, thus restricting the model offering of a major european automobile manufacturer. The paper concludes that general benefit would be derived from genuine pan european emission legislation, particularly if that legislation was established at levels of control that allowed the development and use of modern engine technology.

The pulleys employed in automotive accessory drive systems very often consist of a two piece assembly; a multitude of fastening techniques are used in completing the assembly. There are numerous assembly methods and a variety of distinct pulley configurations dictated by the various automobile manufacturers in accordance with individual accessory drive needs. An expert system is being developed to evaluate the merit of multiple assembly alternatives for a specific pulley application. The expert system provides a consistent evaluation tool for assembly alternatives, balancing the influence of product cost, strength and quality considerations. The knowledge-based system is implemented in an expert system shell called AGNESS (A Generalized Network-based Expert System Shell). The expert system judges the acceptability of various pulley assembly techniques, assigning a high “merit value” to the better designs and proportionately lower values to less desirable designs.

A portable in-vehicle NVH data acquisition and analysis system is required to support the product development timing necessary to be competitive in today's automotive market. The components of such a system should include rugged hardware and software to support NVH data acquisition and analysis for frequently performed tests. The system should be easy for the vehicle development engineers to operate while producing results with a high confidence level. Once the data has been measured and analyzed, the system should support automated reporting and databasing of the results. The availability of such a system would make it easy for the vehicle development engineers to perform standardized tests with standardized analysis and reporting. Such a system has been successfully developed at Ford Motor Company.

The reach capability of drivers is currently represented in vehicle design practice in two ways. The SAE Recommended Practice J287 presents maximum reach capability surfaces for selected percentiles of a generic driving population. Driver reach is also simulated using digital human figure models. In typical applications, a family of figure models that span a large range of the target driver population with respect to body dimensions is positioned within a digital mockup of the driver's workstation. The articulated segments of the figure model are exercised to simulate reaching motions and driver capabilities are calculated from the constraints of the kinematic model. Both of these current methods for representing driver reach are substantially limited. The J287 surfaces are not configurable for population characteristics, do not provide the user with the ability to adjust accommodation percentiles, and do not provide any guidance on the difficulty of reaches that are attainable.

A finite element analysis method has been developed to assess the design of an automobile body joint. The concept of the coefficient of joint stiffness and the force distribution ratio are proposed accordingly. The coefficient of joint stiffness reveals whether a joint is stiff enough compared to its joining components. In addition, these parameters can be used to estimate the potential and the effectiveness for any further improvement of the joint design. The modeling and analysis of the proposed process are robust. The coefficient of joint stiffness could be further developed to serve as the joint design target.