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The Ford GT Program Team was allocated just 22 months from concept to production to complete the Electrical and Electronics systems of the Ford GT. This reduced vehicle program timing - unlike any other in Ford's history -- demanded that the team streamline the standard development process, which is typically 54 months. This aggressive schedule allowed only 12 weeks to design the entire electrical and electronic system architecture, route the wire harnesses, package the components, and manufacture and/or procure all components necessary for the first three-vehicle prototype build.

A study was conducted using Ford's nine standard CFD calibration models as described in SAE paper 940323. The models are identical from the B-pillar forward but have different back end configurations. These models were created for the purpose of evaluating the effect of back end geometry variations on aerodynamic lift and drag. Detailed experimental data is available for each model in the form of surface pressure data, surface flow visualization, and wake flow field measurements in addition to aerodynamic lift and drag values. This data is extremely useful in analyzing the accuracy of the numerical simulations. The objective of this study was to determine the capability of a digital physics based commercial CFD code, PowerFLOW ® to accurately simulate the physics of the flow field around the car-like benchmark shapes.

As part of an ongoing technical collaboration between Ford and Rouge Steel Company, a comprehensive study of door slam event was undertaken. The experimental phase of the project involved measurements of accelerations at eight locations on the outer panel and strains on six locations of the inner panel. Although slam tests were conducted with window up and window down, results of only one test is presented in this paper. The CAE phase of the project involved the development of suitable “math” model of the door assembly and analysis methodology to capture the dynamics of the event. The predictability of the CAE method is examined through detailed comparison of accelerations and strains. While excellent agreement between CAE and test results of accelerations on the outer panel is obtained, the analysis predicts higher strains on the inner panel than the test. In addition, the tendency of outer panel to elastically buckle is examined.

Self-piercing rivet (SPR) joints are a key joining technology for lightweight materials, and they have been widely used in automobile manufacturing. Manual visual crack inspection of SPR joints could be time-consuming and relies on high-level training for engineers to distinguish features subjectively. This paper presents a novel machine learning-based crack detection method for SPR joint button images. Firstly, sub-images are cropped from the button images and preprocessed into three categories (i.e., cracks, edges and smooth regions) as training samples. Then, the Artificial Neural Network (ANN) is chosen as the classification algorithm for sub-images. In the training of ANN, three pattern descriptors are proposed as feature extractors of sub-images, and compared with validation samples. Lastly, a search algorithm is developed to extend the application of the learned model from sub-images into the original button images.

To assess the fuel consumption of vehicles, three sets of input data are required; drive cycles, vehicle parameters, and environmental conditions. As the first part of a series of studies on real-world fuel consumption, this study focuses on the drive cycles. In principle, drive cycles should represent real-world usage. Some of them aim at a specific usage such as a city driving condition or an aggressive driving style. However, the definition of city or aggressive driving is very subjective and difficult to quantitatively correlate with the real-world usage. This study proposes a methodology to quantify the speed and dynamics of drive cycles, or vehicle speed traces in general, against the real-world usage. After reviewing parameter sets found in other studies, relative cubic speed (RCS) and positive kinetic energy (PKE) are selected to represent the speed and dynamics through energy flow balance at the wheels.

The transportation sector is facing three revolutions: shared mobility, electrification, and autonomous driving. To inform decision making and guide smart transportation system development at the city-level, it is critical to model and evaluate how travelers will behave in these systems. Two key components in such models are (1) individual travel demands with high spatial and temporal resolutions, and (2) travelers’ sociodemographic information and trip purposes. These components impact one’s acceptance of autonomous vehicles, adoption of electric vehicles, and participation in shared mobility. Existing methods of travel demand generation either lack travelers’ demographics and trip purposes, or only generate trips at a zonal level. Higher resolution demand and sociodemographic data can enable analysis of trips’ shareability for car sharing and ride pooling and evaluation of electric vehicles’ charging needs.

The performance of structure-borne road NVH can be cascaded down to three major systems: 1) vehicle body structure, 2) chassis/suspension, 3) tire/wheel. The forces at the body attachment points are controlled by the isolation efficiency of the chassis/suspension system and the excitation at the spindle/knuckle due to the tire/road interaction. The chassis force transmissibility is a metric to quantify the isolation efficiency. This paper presents a new experimental methodology to estimate the chassis force transmissibility from a fully assembled vehicle. For the calculation of the transmissibility, the spindle force/moment estimation and the conventional Noise Path Analysis (NPA) methodologies are utilized. A merit of the methodology provides not only spindle force to body force transmissibility but also spindle moment to body force transmissibility. Hence it enables us to understand the effectiveness of the spindle moments on the body forces.

Transient automotive sounds often possess a complex internal structure resulting from one or more impacts combined with mechanical and acoustic cavity resonances. This structure can be revealed by a new technique for obtaining translation-invariant scalograms from orthogonal discrete wavelet transforms. These scalograms are particularly well suited to the visualization of complex sound transients which span a wide dynamic range in time (ms to s) and frequency (∼100Hz to ∼10kHz). As examples, scalograms and spectrograms of door latch closing events from a variety of automotive platforms are discussed and compared in light of the subjective rankings of the sounds.

The objective of this paper is to develop a test capable of ranking lift-gates based on strain concentration levels reflected in fringe characteristics in the known stress/strain concentration and fracture vicinity. First, the system (lift gate glass, adhesive and appliqué) is chosen as test sample since the subsystem (local appliqué) does not exhibit the failure mode observed in the field test. Subsequently, it has been identified that the thermal component (rather than mechanical) is the predominant load by laser scanning vibrometry and confirmed via field test data. Next, digital shearography has been selected as the measurement and visualization tool of strain distribution due to its various advantages such as full field view and non-contact advantages. Finally, the test method has been applied to rank and optimize the structural configuration around appliqués' to reduce / eliminate failure.

Thermal comfort in automotive seating has been studied and discussed for a long time. The available research, because it is focused on the components, has not produced a model that provides insight into the human-seat system interaction. This work, which represents the beginning of an extensive research program, aims to establish the foundation for such a model. This paper will discuss the key physiological, psychological, and biomechanical factors related to perceptions of thermal comfort in automotive seats. The methodology to establish perceived thermal comfort requirements will also be presented and discussed.

The world urbanization is growing rapidly, bringing many challenges for people to move in dense metropolitan regions. Public transportation is not able to attend the whole demand, and individual transportation modes are struggling with traffic congestion and stringent regulations to reduce its attractiveness, such as the license plate restriction in São Paulo. On the other hand, enablers like smartphones mass penetration, GPS connected services and shared economy have opened space to a whole new range of possible solutions to improve people perception on urban mobility. This work aims to evaluate the modal choice behavior models and understand the success factor of current mobility solutions in the city of São Paulo. The data available through origin/destination researches will be used to validate the models used in this work.

Plug in hybrid electric vehicles (PHEVs) have gained interest over last decade due to their increased fuel economy and ability to displace some petroleum fuel with electricity from power grid. Given the complexity of this vehicle powertrain, the energy management plays a key role in providing higher fuel economy. The energy management algorithm on PHEVs performs the same task as a hybrid vehicle energy management but it has more freedom in utilizing the battery energy due to the larger battery capacity and ability to be recharged from the power grid. The state of charge (SOC) profile of the battery during the entire driving trip determines the electric energy usage, thus determining overall fuel consumption.

Recently, papers have been published purporting to study the effect of rear axle tramp during tread separation events, and its effect on vehicle handling [1, 2]. Based on analysis and physical testing, one paper [1] has put forth a mathematical model which the authors claim allows vehicle designers to select shock damping values during the development process of a vehicle in order to assure that a vehicle will not experience axle tramp during tread separations. In the course of their work, “lumpy” tires (tires with rubber blocks adhered to the tire's tread) were employed to excite the axle tramp resonance, even though this method has been shown not to duplicate the physical mechanisms behind an actual tread belt separation. This paper evaluates the theories postulated in [1] by first analyzing the equations behind the mathematical model presented. The model is then tested to see if it agrees with observed physical testing.

Reductions of hardware costs as well as implementations of new innovative functions are the main drivers of today's automotive electronics. Indeed more and more resources are spent on adapting existing solutions to different environments. At the same time, due to the increasing number of networked components, a level of complexity has been reached which is difficult to handle using traditional development processes. The automotive industry addresses this problem through a paradigm shift from a hardware-, component-driven to a requirement- and function-driven development process, and a stringent standardization of infrastructure elements. One central standardization initiative is the AUTomotive Open System ARchitecture (AUTOSAR). AUTOSAR was founded in 2003 by major OEMs and Tier1 suppliers and now includes a large number of automotive, electronics, semiconductor, hard- and software companies.

Yaw rate of a vehicle is highly influenced by the lateral forces generated at the tire contact patch to attain the desired lateral acceleration, and/or by external disturbances resulting from factors such as crosswinds, flat tire or, split-μ braking. The presence of the latter and the insufficiency of the former may lead to undesired yaw motion of a vehicle. This paper proposes a steer-by-wire system based on fuzzy logic as yaw-stability controller for a four-wheeled road vehicle with active front steering. The dynamics governing the yaw behavior of the vehicle has been modeled in MATLAB/Simulink. The fuzzy controller receives the yaw rate error of the vehicle and the steering signal given by the driver as inputs and generates an additional steering angle as output which provides the corrective yaw moment.

The unitized construction Aerostar compact van and wagon models have been engineered to meet a variety of consumer transportation needs. The broad range of functional and image objectives have been attained by traditional design and development programs augmented by new developmental methods and isolation components. State-of-the-art development methodologies applied early in the Aerostar program enabled prediction of the effects of design revisions intended to improve subsystem response characteristics and isolation. Developmental methods used included finite element analysis, modal analysis and synthesis, transmissibility measurements, torsional powertrain measurements, continuous wave laser holography, acoustical mode determination, acoustical intensity mapping and sensitivity studies used to project production ranges of quality.

A better understanding is needed of the electrostatic rotating bell (ESRB) application of metallic basecoat paint to automobile exteriors in order to exploit their high transfer efficiency without compromising the coating quality. This paper presents the initial results from experimental investigation of sprays from an ESRB which is designed to apply water-borne paint. Water was used as paint surrogate for simplicity. The atomization and transport regions of the spray were investigated using laser light sheet visualizations and phase Doppler particle analyzer (PDPA). The experiments were conducted at varying levels of the three important operating parameters: liquid flow rate, shaping-air flow rate, and bellcup rotational speed. The results show that bellcup speed dominates atomization, but liquid and shaping-air flow rate settings significantly influence the spray structure. The visualization images showed that the atomization occurs in ligament breakup regime.

This paper proposes a low-cost but indirect method for occupancy detection and occupant counting purpose in current and future automotive systems. It can serve as either a way to determine the number of occupants riding inside a car or a way to complement the other devices in determining the occupancy. The proposed method is useful for various mobility applications including car rental, fleet management, taxi, car sharing, occupancy in autonomous vehicles, etc. It utilizes existing on-board motion sensor measurements, such as those used in the vehicle stability control function, together with door open and closed status. The vehicle’s motion signature in response to an occupant’s boarding and alighting is first extracted from the motion sensors that measure the responses of the vehicle body. Then the weights of the occupants are estimated by fitting the vehicle responses with a transient vehicle dynamics model.

Static seating bucks have long been used as the only means to subjectively appraise the vehicle interior packages in the vehicle development process. The appraisal results have traditionally been communicated back to the requesting engineers either orally or in a written format. Any design changes have to be made separately after the appraisal is completed. Further, static seating bucks lack the flexibility to accommodate design iterations during the evolution of a vehicle program. The challenge has always been on how to build a seating buck quickly enough to support the changing needs of vehicle programs, especially in the early vehicle development phases. There is always a disconnect between what the seating buck represents and what is in the latest design (CAD), since it takes weeks or months to build a seating buck and by the time it is built the design has already been evolved. There is also no direct feedback from seating buck appraisal to the design in CAD.

Hardware benchmarking of body overall stiffness and joint stiffness of the best-in-class competitive vehicles is a common practice in the automobile industry. However, this process does not provide design insights of competitive body structures, which relate stiffness performance to key architectural designs. To overcome this drawback, a CAD body-in-prime model of a competitive vehicle is developed using laser/optical scanning technology and a corresponding CAE model is built based on the CAD data. A deep-dive structural efficiency study is conducted using this model and “pros” and “cons” of the architectural design of each individual joint and each section of major load-carrying members of this body structure are identified. This analytical benchmarking (or reverse engineering) process enables a company to adopt best-in-class design practices and achieve competitive advantages in vehicle designs.