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A correlation of aerodynamic wind tunnels was initiated between Chrysler, Ford and General Motors under the umbrella of the United States Council for Automotive Research (USCAR). The wind tunnels used in this correlation were the open jet tunnel at Chrysler's Aero Acoustic Wind Tunnel (AAWT), the open jet tunnel at the Jacobs Drivability Test Facility (DTF) that Ford uses, and the closed jet tunnel at General Motors Aerodynamics Laboratory (GMAL). Initially, existing non-competitive aerodynamic data was compared to determine the feasibility of facility correlation. Once feasibility was established, a series of standardized tests with six vehicles were conducted at the three wind tunnels. The size and body styles of the six vehicles were selected to cover the spectrum of production vehicles produced by the three companies. All vehicles were tested at EPA loading conditions. Despite the significant differences between the three facilities, the correlation results were very good.

In order to maximize customer satisfaction in today's global market place, the quality of products and services need to be improved continually. Increased focus on quality, with the attendant proliferation of methods and tools, has created the need for a comprehensive framework to guide the selection of the tools. Individuals within an organization need to know what tools are appropriate in a given situation, and when, where and how the knowledge gained from an effort should be documented. In addition, a common nomenclature to convey quality related information to each other would avoid confusion and improve the communication process thus improving the effectiveness and productivity of the organization. This paper integrates tools that have evolved recently with the old tools that have been in use for a number of years.

Variant modeling techniques have been developed to allow systems engineers to model multiple similar variants in a product line as a single variant model. In this paper, we expand on this past work to explore the extent to which variant modeling in SysML can be applied to a broad range of dissimilar systems, covering the entire domain of ground vehicles, in single reference architecture model. Traditionally, a system’s structure is decomposed into subsystems and components. However, this method is found to be ineffective when modeling variants that are functionally similar but structurally different. We propose to address this challenge by first decomposing the system not only by subsystem but also by high-level function. This pattern is particularly useful for situations where two variants perform the same function, but one variant performs the function using one subsystem, whereas the other variant performs the same function using one or more different subsystems.

Many descriptions of product development are based on a timeline of activity. Timelines typically do not characterize the underlying strategy and flexibility embodied in the technical activity that actually takes place between activity nodes. Timelines alone will inhibit evolving to a more rational approach to product development. The view of engineering described in this paper is a functional view of engineering. It is what engineers do. It is aligned with the technical tools used by engineers. It applies to both product development and manufacturing. It's purpose is to enhance understanding of the function of engineering activities, including reliability.

The advancement in connectivity technology is driving a shift in business models in almost every field. Automakers need to adapt to a new business model in which the platform (automobile) and the mobility solutions (Devices and Services) are enabled by a strong dynamic connectivity. To succeed in this business model, it is imperative to deliver an unparalleled customer experience. Traditional customer experiences focused only in the platform (automobile) are no longer sufficient to address the mobility needs. The development of in-vehicle features should consider both the platform and the connectivity in a single development scope. This paradigm shift sets new challenges for the in-vehicle features designers. Designers have to speak not only the language of the experience but rather a language to address different levels of abstractions to ensure effective communication with all stakeholders and developers including those outside the organization.

The use of finite element modeling (FEM) tools is part of the most of the current product development projects of the automotive industry companies, replacing an important part of the physical tests with lower costs, higher speed and with increasing accuracy by each day. In addition to this, computer-aided engineering (CAE) tools can be either used after the product is released, at any moment of the product life, in many different situation as a new feature release, to validate a more cost-efficient design proposal or to help on solving some manufacturing problem or even a vehicular field issue. Different from the phase where the product is still under development, when standard virtual test procedures are performed in order to validate the vehicle project, in this case, where engineers expertise plays a very important role, before to proceed with any standard test it is fundamental to understand the physics of the phenomena that is causing the unexpected behavior.

Today's automotive and electronics technologies are evolving so rapidly that educators and industry are both challenged to re-educate the technological workforce in the new area before they are replaced with yet another generation. In early November 2009 Ford's Product Development senior management formally approved a proposal by the University of Detroit Mercy to transform 125 of Ford's “IC Engine Automotive Engineers” into “Advanced Electric Vehicle Automotive Engineers.” Two months later, the first course of the Advanced Electric Vehicle Program began in Dearborn. UDM's response to Ford's needs (and those of other OEM's and suppliers) was not only at the rate of “academic light speed,” but it involved direct collaboration of Ford's electric vehicle leaders and subject matter experts and the UDM AEV Program faculty.

Geometric Dimensioning and Tolerancing is used to describe the allowed feature variations regarding the product design. Tolerance specification is important in many stages of all phases on product development. The product development engineering need to define the symbols to use on the Feature Control Frame of every component. Since the component function has an increment on its complexity year over year, it is not trivial to define those symbols anymore. The determination of dimensional tolerance shall be preceded by careful specification of the types of tolerance and symbols that will be applied in controlled features. Poor tolerance specifications can increase the production cost, require late product changes or lead to legal issues.

The largest automobile companies have several corporate, regulatory and customer requirements to integrate into engineering of development [1]. These information need to be split in technical team called disciplines as electrical, chassis, powertrain, etc. The advanced engineering team is responsible to conduct this process with general purpose of facilitating the managing and tracking of creation and execution of the total vehicle/system. However, the interrelation, complexity and lack of engineer's know-how of these systems have been creating innumerous issues into development, launch, manufactory and quality. Insufficient dedicated tools, requirement definitions and poor initial programs formulation are some reasons of these issues. It means that the ability applied in advanced engineering principles and analytical techniques in an automotive engineering context have to be improved.

The automotive industry is currently experiencing a massive transformation, one like it has not quite seen in the past. With the advent of highly software-driven, always on, connected vehicles, the automotive industry is experiencing itself at a crossroads. While the traditional component-driven design approach to vehicle development worked in the favor of the industry for decades due to vehicles being mostly mechanical in nature, the industry now finds itself struggling to develop well-integrated vehicle solutions with the large dependency on software systems. The fast-paced nature of the software world makes it imperative to approach the development of automobiles from a Systems Engineering perspective. A function-based approach to the development of vehicle architectures can ensure cohesive systems development and a well-integrated vehicle.

This paper is divided into two parts: Part 1 - Systems engineering fundamentals Part 2 - Engine cooling design from a systems engineering perspective In Part 1, we explain how the task of designing a complex system can be made easier by the application of Systems Engineering principles. (This part is self contained and may be of general interest to those who have no special interest in engine cooling). Systems Engineering provides three key benefits: It facilitates communication: Requirements define the problem, they allow team members to see their own work in context Key information is standardized and made easier to visualize and verify. An “audit trail” is maintained ensuring that important information is documented, and human memory is no longer relied on for important decisions. Translates requirements into design.

A robust Vehicle Model Architecture (VMA) has been developed to support model-based Vehicle System Control (VSC) design work and, in general, model-based vehicle system engineering activities. It is based on a logical breakdown of the vehicle into key subsystems with supporting bus infrastructure for distribution of signals between subsystems. Primary physical interfaces between the top level subsystems have been defined. Subsystem models that comply with these interfaces can be easily plugged into the architecture for complete simulation of vehicle systems. The VMA encourages model re-use and sharing between project teams and, furthermore, removes key obstacles to sharing of models with suppliers.

To reduce the cost of prototype and physical test, CAE analysis has been widely used to evaluate the vehicle performance during product development process. Combining CAE analysis and optimization approach, vehicle design process can be implemented more efficiently with affordable cost. Reliability based design optimization (RBDO) formulation considers variations of input variables, such as component gauges and material properties. As a result, the design obtained by using RBDO is more reliable and robust compared to those by deterministic optimization. The RBDO process starts from running simulation at DOE sampling data points, generating surrogate models (response surface) and performing robust and reliability based design optimization on the surrogate models by using Monte Carlo simulation. This paper presents a RBDO framework in Excel enviroment.

Simulink is a very successful and popular method for modelling and auto-coding embedded automotive features, functions and algorithms. Due to its history of success, university feeder programs, and large third party tool support, it has, in some cases, been applied to areas of the software system where other methods, principles and strategies may provide better options for the software and systems engineers and architects. This paper provides approaches to determine when best to apply UML and when best to apply Simulink to a typical automotive feature. Object oriented software design patterns as well as general guidelines are provided to help in this effort. This paper's intent is not to suggest a replacement for Simulink but to provide the software architects and designers additional options when decomposing high level requirements into reusable software components.

Computer Aided Engineering (CAE) has become a vital tool for product development in automotive industry. Various computer models for occupant restraint systems are developed. The models simulate the vehicle interior, restraint system, and occupants in different crash scenarios. In order to improve the efficiency during the product development process, the model quality and its predictive capabilities must be ensured. In this research, an objective model validation metric is developed to evaluate the model validity and its predictive capabilities when multiple occupant injury responses are simultaneously compared with test curves. This validation metric is based on the probabilistic principal component analysis method and Bayesian statistics approach for multivariate model assessment. It first quantifies the uncertainties in both test and simulation results, extracts key features, and then evaluates the model quality.

Cross-section properties and joint stiffness properties of the body structure define its characteristic behavior. During the transitional product development process, body structure joints are optimized on an individual basis to reduce cost and weight. The objective of this paper is to present a methodology to analyze the entire body structure design by optimizing each body joint for stiffness and cost. This methodology utilizes joint sensitivity data from FEA, section properties, and cost/weight data. When the joint stiffness status does not meet the target during the design process, the methodology is an effective tool in making decisions regarding the gage increase/decrease for each part constituting body structure joints. Additionally, the methodology has been applied to body structure joints and door upper frame separately.

The front-end design process in the automotive industry today is time consuming and expensive. Although CFD (Computational Fluid Dynamics) modeling is helpful, many vehicle development tests in different wind tunnels are still required to balance the competing requirements of power train cooling, vehicle aerodynamics, climate control, styling, body structure, and product cost. For example, engine cooling and climate control heat exchangers require adequate airflow to achieve their performance. But, this airflow increases cooling drag and can compromise vehicle handling. Internal air deflectors (ducting) are often used to make the frontal opening more efficient and help prevent heat recirculation from the hot engine compartment to the A/C condenser at idle. But this increases product cost and can compromise underhood temperature. A more efficient and faster process is needed to support these trade-off discussions.

Design for Six Sigma has been applied successfully in many industries, but industry differences present challenges that influence implementation. Implementers in the automotive industry face the challenge of integrating DFSS into mature and complex product development systems. This requires a clear definition of the DFSS role in an organization and clear guidelines for project scope. But effective definitions and guidelines will vary depending on an organization's place in the supply chain. In addition, varying levels of project scope present challenges in determining effective metrics. Organizations are best served by a flexible set of metrics that drive the right behavior at all organizational levels. Once roles and metrics are established, balancing DFSS with other ongoing programs becomes a challenge. Over time, the elements of DFSS should be integrated into a holistic system that produces high quality, market-winning products.

Vehicle weight-driven design comes amid rising higher fuel efficiency standards and must meet the criteria—pass proving ground (PG) test events that are equivalent to customer usage. Computer-aided engineering (CAE) fatigue analysis for PG is a successful push behind to digitally simulate vehicle durability performance with high fidelity. The need for vehicle weight reduction often arises in the vehicle development final phases when CAE methods, time, and tangible cost-effective opportunities are limited or nonexistent. In this research, a new CAE methodology is developed to identify opportunities for lightweight hole placement in the chassis structure and deliver a cost-effective lightweight solution with no additional impact on fatigue life. The successful application of this new methodology exhibits the effectiveness of the truck frame, which is the key chassis structure to support the body, suspension, and powertrain.

In a recent time, which new vehicle lines comes with a huge number of sensors, control units, embedded technologies, and the complexity of these systems (electronics, electrical and electromechanical parts) increases in an exponential way. Considering these events, the expressive generated data amount grows in the same pace, so, consume, transform, and analyze all these data to better understand the modern customer, their needs and how they use the car features becomes necessary. Through that scenario, connected vehicles developed by Ford Motor Company has been generating opportunities to feature’s improvement and cost reduction based on data analysis. This growing quantity of data might be used to optimize feature systems and help engineering teams to understand how the features have been used and enhance the systems engineering design for new or existing features.