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For a series plug-in hybrid electric vehicle (PHEV), it is critical that batteries be sized to maximize vehicle performance variables, such as fuel efficiency, gasoline savings, and zero emission capability. The wide range of design choices and the cost of prototype vehicles calls for a development process to quickly and systematically determine the design characteristics of the battery pack, including its size, and vehicle-level control parameters that maximize the net present value (NPV) of a vehicle during the planning stage. Argonne National Laboratory has developed Autonomie, a modeling and simulation framework. With support from The MathWorks, Argonne has integrated an optimization algorithm and parallel computing tools to enable the aforementioned development process. This paper presents a study that utilized the development process, where the NPV is the present value of all the future expenses and savings associated with the vehicle.

Lookup tables and functions are widely used in real-time embedded automotive applications to conserve scarce processor resources. To minimize the resource utilization, these lookup tables (LUTs) commonly use custom data structures. The lookup function code is optimized to process these custom data structures. The legacy routines for these lookup functions are very efficient and have been in production for many years. These lookup functions and the corresponding data structures are typically used for calibration tables. The third-party calibration tools are specifically tailored to support these custom data structures. These tools assist the calibrators in optimizing the control algorithm performance for the targeted environment for production. Application software typically contains a mix of both automatically generated software and manually developed code. Some of the same calibration tables may be used in both auto generated and hand-code [ 1 ] [ 2 ].

When transitioning to Model-Based Design for embedded systems development, it is essential to consider an overall plan spanning people, development processes, and tools. A common sense approach when beginning any process improvement activity is to first identify the problem to be solved and then develop a plan to implement the solution. When transitioning to Model-Based Design, performing the transition in an iterative manner - do, learn, adjust, and repeat - has been shown to be most effective. The end goal is a development process where the model is the design, verification is done throughout the development process using simulation, and the implementation of the entire application onto target hardware is highly automated. Faced with design and organizational complexity, time, quality, and cost pressures, the transition is akin to changing a flat tire while moving down the highway. Choosing the right first steps are key to a successful transition.

As the demand for high-integrity and mission-critical embedded software intensifies, many organizations have adopted Model-Based Design to overcome the challenges associated with design complexity, reliability, quality, and time-to-market for embedded-systems development. The breadth and scope of projects applying Model-Based Design continues to increase rapidly, resulting in models that are exceptionally large and complex. Consequently, project teams have increased in size, thereby increasing the need for communication and collaboration. Model-Based Design facilitates parallel development in large-scale modeling projects by enabling multiple project teams to independently design models, integrate them with others, generate production code, and verify different model components within a larger collaborative infrastructure.