By Ryan Mowry

 Graphical models of system  Entire system or just parts  Complex systems easier to understand  “Capture key requirements and demonstrate correct behavior in simulation”

 Starts after requirements  Can cover design, development, and testing

 Representation of system  Inputs  Outputs  Mathematical Operations

 Match model to target architecture  Correct data types for input/output  Interactions with other systems

 Simulate model based on inputs and parameters  Observe actual outputs of the model compared to expected  Allows testing parallel to design

 Earlier error detection  Easy to test all input ranges  Improve Verification  Able to make changes to the model to reach expected results

 Testing a model using input from another model  Models interact with one another so useful data can be obtained by testing current model with a working existing model  Earlier error detection

 Testing model in conjunction with generated or handwritten code on one machine  Useful when parts of generated code are updated to test compatibility with old code  Also for handwritten code that will be used with generated code

 Test software algorithm using model instead of needing actual hardware

 Generated code executes on target processor  Test code on target processor with system model using actual I/O (CAN)  One step from hardware testing allows for more error detecting before needing actual expensive hardware

 Use generated code for both target architectures to test code functionality  Real-time system simulates actual target device to detect more errors between systems  Last testing before integrated testing

 Verify model and simulation meet requirements  Auto-Generate code from model  Code and filenames very abstract  Hard to follow and understand

 Code already tested  Changes to model if necessary  Regenerate code from corrected model instead of changing code  Floating and fixed point conversion

 High costs in new software  Time and cost to learn the model-based approach  Greater time spent during analysis and design of the system

 Model-based Development became popular among automotive companies  Some companies thought lowered cost, others thought no difference or even higher cost  Global research study by Altran Technologies, chair of software and systems engineering, and the chair of Information Management of the University of Technology in Munich

 Cost difference in each software phase  Analysis of amount of modeling used  Error Detection  Cost Difference  Quality Criteria  Overall Cost

 Modeling helped cut overall costs of the project  Increased quality and bug detection  Less than 60% implementation of model- based development yielded the best results but depends on project

 Do not need to know programming languages  Testing sooner leads to earlier bug detection  Better overall quality  Design reuse for upgraded systems

 New Software costs  Training on new software and approach  Abstract code  Lengthy Simulations  Advantages outweigh disadvantages

 New methodology in automotive industry  New technologies create major design changes  Simulations help find balance in subsystems  Design comes after balance found through simulating

 LMS Imagine Lab  Modelica  CyDesign  Dymola  MapleSim  VisSim  MATLAB and Simulink  EicasLab  Rational Rhapsody

 Block diagram of desired system  Diagram hierarchy of components  Useful in sharing components with other models

 Graphical output to view results  Debug Simulation  Step forward to find when state changes  Or step backwards (Rewind Simulation)  Run on target hardware

 Once model and simulation meet requirements and errors have been fixed, code can be generated for system.  Simulink allows for generating code in C,C++,HDL, and PLC

 Demo Simulink Simulation process

 Broy, M.; Krcmar, H.; Zimmerman, J.; Kirstan, S.: Model-based Software Development – Its Real Benefit. EETimes, March 2011  Gegic, G,: In-the-Loop Testing Aids Embedded System Validation. mbedded_system_validation-article-FAJH_Mathworks_Jul2009- html.aspx, August 3, mbedded_system_validation-article-FAJH_Mathworks_Jul2009- html.aspx  Ledin, J.; Dickens, M.: Automatic Embedded Code Generation from Simulation Models. RTC Magazine. December  Morey, B: ‘Simulate, then Design’ Emerges as New Engineering Methodology. SAE OHE, September 1, 2011  "Simulink." Simulation and Model-Based Design. N.p., n.d. Web. 24 Oct