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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.

With the increasing number of ECUs in modern automotive applications (70 in high end cars), designers are facing the challenge of managing complex design tasks, such as the allocation of software tasks over a network of ECUs. The allocation may be dictated by different attributes (performance, cost, size, etc.). The task of validating a given allocation can be achieved either via static analysis (e.g., for cost, size) and/or dynamic analysis (e.g. via performance simulation - for timing constraints). This paper brings together two key concepts: algorithmic and optimization techniques to be used during static analysis and virtual integration platforms for simulation-based exploration. The two concepts together provide the pillars for a constraint-driven / simulation-based approach, tailored to optimize the entire ECU network according to a cost function defined by the user.

Modular systems provide the ability to achieve product variety through the combination and standardization of components. The trend within the automotive industry is towards modular systems. The automotive manufacturers separate the vehicle into modular systems (chunks), which may be built and tested off line before assembled for vehicle installation. In this paper, software that utilizes a new clustering algorithm for development of integrated and modular vehicle architectures is presented. The algorithm and software tool presented in this paper allows the system designer to automate and optimize the vehicle system development process. A vehicle system model that identifies all the functional (data, and control) interfaces between vehicle system functions is the input to the software tool. The software tool is capable of analyzing this input data and determining the vehicle system architecture by optimally grouping (integrating) functions into physical modules.

Automotive HW/SW architectures are becoming increasingly complex to support the deployment of new safety, comfort, and energy-efficiency features. Such architectures include several software tasks (100+), messages (1000+), computational and communication resources (70+ CPUs, 10+ buses), and (smart) sensors and actuators (20+). To cope with the increasing system complexity at lowest development and product costs, highest safety, and fastest time to market, model-based rapid-prototyping development processes are essential. The processes, coupled with optimization steps aimed at reducing the number of software and hardware resources while satisfying the safety requirements, enable reduction of the system complexity and ease downstream testing/validation efforts. This paper describes a novel model-based design exploration and optimization process for the deployment of a set of software tasks on a dual-processor control module implementing a fail-safe strategy.

Rising costs continue to be a problem within the automotive industry. One way to address these rising costs is through modularity. Modular systems provide the ability to achieve product variety through the combination and standardization of components. Modular design approaches used in development of vehicle electrical, electronic, and software (EES) systems allow sharing of architectures/modules between different product lines (vehicles). This modular design approach may provide economies of scale, reduced development time, reduced order lead-time, easier product diagnostics, maintenance and repair. Other benefits of this design approach include development of a variety of EES systems through component swapping and component sharing. In this paper, new optimization algorithms and software tools are presented that allow vehicle EES system design engineers to develop modular architectures/modules that can be shared across vehicle platforms (for OEMs) and across OEMs (for suppliers).

Modular systems provide the ability to achieve product variety through the combination and standardization of components. In this paper, a methodology that combines the system modeling, integration analysis, and optimization techniques for development of modular electrical/electronic vehicle systems is presented. The approach optimizes integration and interactions of the electrical/electronic system elements and creates functional and physical modules for the system. The approach proposed in this paper is systematic and can be used to support product development and decision making in engineering design.

The rapid growth of vehicle feature content continues to challenge automotive designers. The total vehicle feature content seriously impacts the manufacturing complexity of any single vehicle. Traditional strategies for introducing new features into high-content luxury vehicles before moving the feature into economy vehicles have been undermined by the fast moving consumer electronics field. The challenge for automotive OEM and Tier 1 suppliers is to optimize the vehicle architecture in order to provide more efficient means of introducing features expediently and efficiently. Therefore, any production vehicle's Electrical, Electronic, & Software (EES) architecture must successfully support modular sourcing, modular assembly, global manufacturing schemes, cost and weight issues.