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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.

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.

Combustion flame geometry calculation is a critical task in the design and analysis of combustion engine chamber. Combustion flame directly influences the fuel economy, engine performance and efficiency. Currently, many of the flame geometry calculation methods assume certain specific chamber and piston top shapes and make some approximations to them. Even further, most methods can not handle multiple spark plug set-ups. Consequently, most of the current flame geometry calculation methods do not give accurate results and have some built-in limitations. They are particularly poor for adapting to any kind of new chamber geometry and spark plug set-up design. This report presents a novel methodology which allows the accurate calculation of flame geometry regardless of the chamber geometry and the number of spark plugs. In this methodology, solid models are used to represent the components within the chamber and unique attributes (colors) are attached respectively to these components.

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.

A new, systematic, sensitivity based design process for weight reduction is presented. Traditionally, a trial and error method is used when a design fails to meet the weight and the design criteria, which often conflict. This old approach not only is time and cost consuming but also does not provide insight into structural behavior. This proposed process uses state-of-the-art technologies such as design sensitivity analysis, numerical optimization, graphical user interface, etc. It handles multi-discipline design criteria simultaneously and provides design engineers insight into structural responses for frequency, durability, and stiffness concerns and a means for systematic weight reduction and quality improvement. The new design process has been applied for the weight reduction of advanced truck frame designs. Results show that a significant weight savings has been achieved while all design criteria are met.

As automakers strategize approaches to sustainable vehicle technologies, alternative powertrains must be considered to reduce future fleet vehicle emissions and improve energy security. These alternative vehicles include different fuels and electrification. The ultimate for on-road CO2 reductions is a zero emission vehicle, which can be achieved by either a hydrogen fuel cell or battery electric vehicle. These vehicles would also require a renewable energy source to provide their propulsion energy in order to achieve maximum sustainability for both CO2 reduction and energy security. Renewable energy sources such as wind or solar result in heat or electricity that needs to be generated into an energy carrier such as hydrogen or stored in a battery. When examining these options based strictly on the efficiency path, previous analysis have concluded fuel cell vehicles may not be an appropriate suitability strategy in comparison to battery electric vehicles.

This paper begins with a comparison of the automotive power supply and loads in the early 1950's (near the end of the six-volt era) to the modern counterpart in the early 1990's (possibly near the end of the 12-volt era). A typical power supply specification sheet is developed based on the in-vehicle performance characteristics. From this summary, two attributes are noted: first, the system voltage is not very stable and second, transient protection is limited. With this awareness and the knowledge that the power supply of the future will need architectural change, a review of the design assumptions using a total system view and a long term outlook is advanced. Using a rule based design process and employing available technology to enhance the power system architecture, a number of elements are proposed for consideration in new designs.

The objective of this work is to provide a relatively simple battery energy storage and loss model that can be used for technology screening and design/sizing studies of hybrid electric vehicle powertrains. The model dynamic input requires only power demand from the battery terminals (either charging or discharging), and outputs internal battery losses, state-of-charge (SOC), and pack temperature. Measured data from a vehicle validates the model, which achieves reasonable accuracy for current levels up to 100 amps for the size battery tested. At higher current levels, the model tends to report a higher current than what is needed to create the same power level shown through the measured data. Therefore, this battery model is suitable for evaluating hybrid vehicle technology and energy use for part load drive cycles.

In vehicle design, response surface model (RSM) is commonly used as a surrogate of the high fidelity Finite Element (FE) model to reduce the computational time and improve the efficiency of design process. However, RSM introduces additional sources of uncertainty, such as model bias, which largely affect the reliability and robustness of the prediction results. The bias of RSM need to be addressed before the model is ready for extrapolation and design optimization. This paper further investigates the Bayesian inference based model extrapolation method which is previously proposed by the authors, and provides a systematic and integrated stochastic bias corrected model extrapolation and robustness design process under uncertainty. A real world vehicle design example is used to demonstrate the validity of the proposed method.

This paper discusses the testing for retentivity of non-volatile memories. The physics associated with the reliable production of various non-volatile data storage devices has long been a topic of debate. The ability to reliably produce devices which endure erase/write cycling and retain data for extended periods of time has been questionable. Recent improvements in IC processing has given rise to claims of enhancements in both of these areas. Non-volatile memories are attractive in many automotive electronic applications where battery backup is neither convenient or feasible, but because of reliability concerns they have not found their way into critical applications. In applications like odometer or emission control calibrations it is imperative that memory retention is assured. In order to verify the reliability of the various available non-volatile memory devices, an accelerated test program was instituted.

Ribbed floor panels have been widely applied in vehicle body structures to reduce interior noise. The conventional approach to evaluate ribbed floor panel designs is to compare natural frequencies and local stiffness. However, this approach may not result in the desired outcome of the reduction in radiated noise. Designing a “quiet” floor panel requires minimizing the total radiated noise resulting from vibration of the floor panel. In this study, the objective of ribbed floor panel design is to reduce the total radiated sound power by optimizing the rib patterns. A parametric study was conducted first to understand the effects of rib design parameters such as rib height, width, orientation, and density. Next, a finite element model of a simplified body structure with ribbed floor panel was built and analyzed. The structural vibration profile was generated using MSCINastran, and integrated with the acoustic boundary element model.

Computationally efficient tire models are needed to meet the timing and accuracy demands of the iterative vehicle design process. Axisymmetric, circumferentially isotropic, planar, discretized models defined by their quasi-static constraint modes have been proposed that are parameterized by a single stiffness parameter and two shape parameters. These models predict the deformed shape independently from the overall tire stiffness and the forces acting on the tire, but the parameterization of these models is not well defined. This work develops an admissible domain of the shape parameters based on the deformation limitations of a physical tire, such that the tire stiffness properties cannot be negative, the deformed shape of the tire under quasi-static loading cannot be dominated by a single harmonic, and the low spatial frequency components must contribute more than higher frequency components to the overall tire shape.

The Auto Industry is responding to the environment and energy conservation concerns by ramping up production of hybrid electric vehicles (HEV). As the initial hurdles of making the powertrain operate are overcome, challenges such as making the powertrain feel more refined and intuitive remain. This paper investigates one of the key parameters for delivering that refinement: engine RPM behavior. Ideal RPM behavior is explored and included in the design of a control system. System implications are examined with regard to the effect of engine RPM scheduling on Battery usage and vehicle responsiveness.

Designing a high-quality door sealing system at low cost is an economic and technological engineering challenge. Robust design is a systematic and efficient technique to meet this challenge of design optimization for performance, quality, and cost. This technique, also called parameter design, focuses on making product and process designs insensitive (i.e. robust) to hard-to-control variations called noise factors. In this paper, we illustrate and apply the principles of robust design using a response model approach to a door sealing system design problem where vehicle interior sound is the primary response being studied. The Appendix contains a glossary of all italicized words for reference.

The design of the Ford Cortina Estate Car converted to propulsion by currently available batteries is described, and results for power train component performance test and vehicle driving characteristics are given. Concept and purpose of this test vehicle are discussed, and chassis and body modifications are described. Design of the electric power train, employing a dc commutator motor and dc solid state chopper controller, is developed. The car instrumentation is described and operating experience in several driving modes is reported. A discussion of battery characteristics concludes the paper.

The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is participating in the 2008 - 2011 EcoCAR: The NeXt Challenge Advanced Vehicle Technology Competition series organized by Argonne National Laboratory (ANL) and sponsored by General Motors (GM) and the U.S. Department of Energy (DoE). Following GM's vehicle development process, HEVT established goals that meet or exceed the competition requirements for EcoCAR in the design of a plug-in, range-extended hybrid electric vehicle. The challenge involves designing a crossover SUV powertrain to reduce fuel consumption, petroleum energy use and well-to-wheels (WTW) greenhouse gas (GHG) emissions. In order to interface with and control the vehicle, the team added a National Instruments (NI) CompactRIO (cRIO) to act as a hybrid vehicle supervisory controller (HVSC).

Designing a vehicle body involves meeting numerous performance requirements related to different attributes such as NVH, Durability, Safety, and others. Multi-Disciplinary Optimization (MDO) is an efficient way to develop a design that optimizes vehicle performance while minimizing the weight. Since a body design evolves in course of the product development cycle, it is essential to repeat the MDO process several times as a design matures and more accurate data become available. This paper presents a real life application of the MDO process to reduce weight while optimizing performance over the design cycle of the 2015 Mustang. The paper discusses the timing and results of the applied Multi-Disciplinary Optimization process. The attributes considered during optimization include Safety, Durability and Body NVH. Several iterations of MDO have been performed at different milestones in the design cycle leading to a significant weight reduction of the already optimized design by over 16kg.

In the current design process, the designer generates the detailed geometry of the component based on experience. Prototypes of this design are built and tested to verify the performance. This design - build - test iterative process is continued until performance targets/criteria are met. Computer Aided Engineering is often used to verify the design. This paper presents a new product development process to substantially reduce the number of design - analysis - build - test iterations. This Upfront Analysis Driven Design process incorporates several state of the art technologies in finite element structural analysis, optimization, and Computer Aided Design. This process ensures a near optimum design in the first design level itself.

A need exists for a reliable and economical analytical aid for designing vehicle structures for controlled crash energy management. Several of the crash simulation methods, currently available to the designer in the evaluation and development of vehicle structures for crash, are reviewed with respect to their capabilities and shortcomings as design aids. An analytical technique is presented, structured along the lines of a design aid and based on a finite element beam model concept. The functions of the various elements of the code are discussed in the context of typical crash events one needs to consider in the design of a structure composed of beam- and column-type elements. The heart of the proposed system code VRUSH is a component code SECOLLAPSE which monitors and predicts crush responses of thin wall structural components and which controls the input into the system code. Also presented are models generated by SECOIXAPSE for typical loading cases.

Obstacle detection technology has made significant progress in the last five years in the important product areas of quality, performance and affordability. Ford Motor Company's market research indicates that our customers are very interested in new safety features. Drivers consider obstacle detection and collision warning technology as the next breakthrough in safety technology. Ford recognizes the importance of moving from the collision mitigation to the collision avoidance paradigm. Fortunately, the first step in collision avoidance can be taken by equipping the vehicle with reliable and affordable obstacle detection sensors