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To cope with the increasing number of advanced features (e.g., smart-phone integration and side-blind zone alert.) being deployed in vehicles, automotive manufacturers are designing flexible hardware architectures which can accommodate increasing feature content with as fewer as possible hardware changes so as to keep future costs down. In this paper, we propose a formal and quantitative definition of flexibility, a related methodology and a tool flow aimed at maximizing the flexibility of an automotive hardware architecture with respect to the features that are of greater importance to the designer. We define flexibility as the ability of an architecture to accommodate future changes in features with no changes in hardware (no addition/replacement of processors, buses, or memories). We utilize an optimization framework based on mixed integer linear programming (MILP) which computes the flexibility of the architecture while guaranteeing performance and safety requirements.

Current manufacture of alternative energy sources for automobiles, such as fuel cells and lithium-ion batteries, uses repeating energy modules to achieve targeted balances of power and weight for varying types of vehicles. Specifically for lithium-ion batteries, tens to hundreds of identical plastic parts are assembled in a repeating fashion; this assembly of parts requires complex dimensional planning and high degrees of quality control. This paper will address the aspects of dimensional quality for repeated, injection molded thermoplastic battery components and will include the following: First, dimensional variation associated with thermoplastic components is considered. Sources of variation include the injection molding process, tooling or mold, lot-to-lot material differences, and varying types of environmental exposure. Second, mold tuning and cavity matching between molds for multi-cavity production will be analyzed.

Industry standards and practices define a number of mathematical and physical methods to estimate the cargo carrying volume capacity of a vehicle. While some have roots dating back decades, others try to assess the utility of the space for cargo by subjective measurements. Each these methods have their own inherent merits and deficiencies. The purpose of this paper is to highlight the differences in calculated cargo volume amongst the following practices: Society of Automobile Engineers (SAE) J1100[1] International Organization for Standardization (ISO 3832)[2], Global Car manufacturer's Information Exchange group (GCIE)[3], Consumer Reports[4]. This paper provides a method and associated rationale for constructing a new cargo volume calculation practice that attempts to harmonize these procedures into a more contiguous practice. This homologation will benefit publishing industry, vehicle manufacturers and customers alike.

In designing vehicles with significant electric driving range, optimizing vehicle energy efficiency is a key requirement to maximize the limited energy capacity of the onboard electrochemical energy storage system. A critical factor in vehicle energy efficiency is the vehicle mass. Optimizing mass allows for the possibility of either increasing electric driving range with a constant level of electrochemical energy storage or holding the range constant while reducing the level of energy storage, thus reducing storage cost. In this paper, a methodology is outlined to study the tradeoff between the battery cost savings achieved by vehicle mass reduction for a constant electric driving range and the cost associated with lightweighting a vehicle. This methodology enables informed business decisions about the available engineering options for lightweighting early in the vehicle development process. The methodology was applied to a compact extended-range electric vehicle (EREV) concept.

Roof racks are designed for carrying luggage during customers' travels. These rails need to be strong enough to be able to carry the luggage weight as well as be able to withstand aerodynamic loads that are generated when the vehicle is travelling at high speeds on highways. Traditionally, roof rail gage thickness is increased to account for these load cases (since these are manufactured by extrusion), but doing so leads to increased mass which adversely affects fuel efficiency. The current study focuses on providing the guidelines for strategically placing lightening holes and optimizing gage thickness so that the final design is robust to noise parameters and saves the most mass without adversely impacting wind noise performance while minimizing stress. The project applied Design for Six Sigma (DFSS) techniques to optimize roof rail parameters in order to improve the load carrying capacity while minimizing mass.

Vehicle development typically occurs by independently documenting requirements for individual subsystems, then packaging these subsystems into the vehicle and testing the feature operation at a higher level, across multiple subsystems. Many times, this independent development process results in integration problems at the vehicle level, such as incomplete feature execution, unexpected operation and information disconnects. The development team is left to debug and create inefficient patches at the vehicle level due to time constraints and / or planned release dates. Without architecting solutions at the feature level, miscommunication of expected feature operation leads to wasted time, re-work and customer dissatisfaction. While the development of vehicle level technical specifications provide feature expectations at the vehicle level, they do not solve the problem of how this operation is to be applied across multiple systems.

Because it is common to attach plastic parts to other plastic, metal, or ceramic assemblies with mechanical fasteners that are often stronger and stiffer than the plastic with which they are mated, it is important to be able to predict the retention of the fastener in the polymeric component. The ability to predict this information allows engineers to more accurately estimate length of part service life. A study was initiated to understand the behavior of threaded fasteners in bosses molded from engineering thermoplastic resins. The study examined fastening dynamics during and after insertion of the fastener and the effects of friction on the subsequent performance of the resin. Tests were conducted at ambient temperatures over a range of torques and loads using several fixtures that were specially designed for the study. Materials evaluated include modified-polyphenylene ether (M-PPE), polyetherimide (PEI), polybutylene terephthalate (PBT), and polycarbonate (PC).

A process for creating a Customer Correlated, Accelerated, Life Test is presented. This process, which results in a model for predicting reliability, has been applied to a cold weather piston scuff problem. In this paper, the authors will discuss development of frequency distributions for customer environmental and operational use, establishment of customer based failure criteria, development of an accelerated test based on degradation, selection of testing strategies, data analyses, and measurement techniques.

A method is described for predicting the reliability and useful life of an automotive powertrain system using a warranty database or from warranty records. The database requires failure corrections for misdiagnosis from duplicate data, trouble-not-identified records and multiple failure modes. Compensations not included in the database for high-mileage drop-out and warranty repairs less than the deductible amount, are also necessary. As an example, the cumulative hazard function of the Bathtub Hazard Rate distribution is fitted to the converted removal data of a typical automotive powertrain, to determine the product life characteristics. An algorithm written in Basic language is used to obtain the analytical results.

A measure of functional degradation or its surrogate is more informative than attribute data such as failure time when improving designs or developing meaningful tests. This paper delineates the advantages and disadvantages of using degradation data, and the situations under which one should or should not use degradation analysis. A comparison has been done between degradation testing, testing to failure, and bogey testing. The steps necessary to apply degradation analysis techniques to product design have been described: determine scope of the project, plan degradation test, collect data, analyze data, and document results. A case study has been used to demonstrate the whole process of degradation analysis techniques.

The GD₃ or GD Cubed method of problem prevention has been applied to product changes and to test results at the component, subsystem and vehicle level. GD₃ stands for Good Design - Good Discussion - Good Dissection. Good Discussion of changes (Design Review Based on Failure Mode) identifies BUDS of PROBLEMS that may arise from interfaces and areas of change. Good Dissection (Design Review Based on Test Results) is applied to physical test samples during and after tests to identify Buds of Problems that may not be obvious from inspection of the parts or test results. The paper first describes implementation of the GD₃ principles and methods supporting Good Discussion (DRBFM) and Good Dissection, and then discusses how they are applied and embedded in the Vehicle Development Process at General Motors Co.