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Using Model-Based Design, engineers model complex systems and simulate them on their desktop environment for analysis and design purposes. Model-Based Design supports a wide variety of C/C++ code generation applications that include stand-alone simulation, rapid control prototyping, hardware-in-the-loop testing, and production or embedded code deployment. Many of these code generation scenarios impose different requirements on the generated code. Stand-alone simulations usually need to run fast, for parameter sweep or Monte Carlo studies, but do not need to execute in true hard real-time. Hardware-in-the-loop tests by definition use engine control unit (ECU) component hardware that requires a hard real-time execution environment to protect the physical devices. Code generated for production ECUs must satisfy hard real-time, efficiency, legacy code, and other requirements involving verification and validation efforts.

Model-Based Design has become a standard in the automotive industry. In addition to the well-documented advantages that come from modeling control algorithms, [1,2,3,4] modeling plants can lead to more robust designs. Plant modeling enables engineers to test a controller with multiple plant parameters, and to simulate nominal or ideal values. Modeling variable physical parameters provides a better representation of what can be expected in production. Monte Carlo analysis is a standard method of simulating variability that occurs in real physical parameters. Automotive companies use Monte Carlo testing to ensure high quality, robust designs. Due to time and resource constraints, engineers often examine only a limited number of key parameters rather than an entire set. This leaves the design vulnerable to problems caused by missing the full potential impact of parameters that were unvaried during testing.

When developing production software for fixed-point Engine Control Units (ECUs), it is important to consider the transition from floating-point to fixed-point algorithms. Systems engineers frequently design algorithms in floating-point math, usually double precision. This represents the ideal algorithm behavior without much concern for its final realization in production software and hardware. Software engineers and suppliers in mass production environments, however, are concerned with production realities and often need to convert these algorithms to fixed-point math for their integer-only hardware. A key task is to design scale factors that maximize code efficiency by minimizing the bytes used, while also minimizing quantization effects such that the fixed-point algorithms match the floating-point results within an acceptable numerical margin.

Typically, battery models are complex and difficult to parameterize to match real-world data. Achieving a good generalized fit between measured and simulated results should be done using a variety of laboratory data. Numerical optimizations can ensure the best possible fit between a simulation model and measured data, given a set of constraints. In this paper, we propose a semi-automated process for parameterizing a lithium polymer battery (LiPB) cell simulation model that is able to satisfy constraints on the optimized parameters. This process uses a number of measured data sets under a variety of conditions. An iterative numerical optimization algorithm using Simulink Parameter Estimation was implemented to estimate parameter values by minimizing error between measured and simulated results.

Model-Based Design with automatic code generation has long been employed for rapid prototyping and is increasing being used for mass production deployment. With the focus on production usage, comes the need to implement a comprehensive V&V strategy involving models and resulting code. A main principal of Model-Based Design is that generated code should behave like the simulation model. It should also be possible to verify that the model or design was fully implemented in the code. As a result, the transformation of models into generated code must be done in a way that facilitates traceability between the model and code. Also automated tests should be performed to determine that the code executes properly in its final software and hardware environments. For example in a typical commercial vehicle application, the control algorithm and plant model are simulated together in a system simulation environment.

This paper presents the business purpose, software architecture, technology integration, and applications of the Cummins Vehicle Mission Simulation (VMS) software. VMS is the value-based analysis tool used by the marketing, sales, and product engineering functions to simulate vehicle missions quickly and to gauge, communicate, and improve the value proposition of Cummins engines to customers. VMS leverages the best of software architecture practices and proven technologies available today. It consists of a close integration of MATLAB and Simulink with Java, XML, and JDBC technologies. This Windows compatible application software uses stand-alone mathematical models compiled using Real Time Workshop. A built-in MySQL database contains product data for engines, driveline components, vehicles, and topographic routes. This paper outlines the database governance model that facilitates effective management, control, and distribution of engine and vehicle data across the enterprise.

Modern electronic control units (ECUs) are increasingly being developed using Model-Based Design with production code generation. With this approach, systems and software engineers model and simulate algorithms using block diagrams, state machines and data dictionaries. Code is then automatically generated from these models and placed into rapid prototyping or production microprocessors. The model structure and code generation configuration options significantly impact the efficiency and clarity of the design and resulting code. While clarity and efficiency may not be much of an issue when performing initial rapid prototyping evaluations on high performance computers, it is a significant concern in formal software development processes targeting low-cost, low-performance mass production ECUs. This paper describes model style guidelines and the best practices for automatically designing and generating optimized fixed-point and floating-point code.