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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.

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.

Great work has been done already in developing robust engineering techniques to improve ideal functions for systems and sub systems. Characterizing an ideal function as a dynamic response type is most preferred way to build quality into a product over a range of input signal values. However, when it is difficult to measure ideal functions, symptomatic outputs such as oil leaks, vibrations, and squeaks, are selected and treated as “Smaller-the-Better” response in non-dynamic response manner. A better approach is to reduce the symptomatic responses over the entire usage range. In order to accomplish this goal, engineers often switch output response and signal axes and apply dynamic response formulation for making the design robust. In this paper, a new and better formulation is proposed and compared with the other formulation. These two formulations were applied on a real automotive case study of decklid bobble and inaccuracies associated with the other formulation were discussed.

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.