Model-Based Systems Engineering and Control System ...
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Model-Based Systems Engineering and Control System
Development via Virtual Hardware-in-the-Loop
Simulation
Dr. Larry MichaelsArgonne National Laboratory
Michael KropinskiGeneral Motors
SAE 2010-01-2325
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Improves quality Provides math-based environment for more thorough multidisciplinary
integration and testing in the virtual environment before hardware builds Reduces cost and time to production
Sorts technologies quickly to reduce hardware build iterations Promotes parallel and integrated virtual development of control systems
and hardware Enables fast to market with new technologies
Delivers better-integrated, initial designs that balance Fuel Economy, Emissions and Drivability (FEED) requirements.
Provides common methods and tools for comparing/evaluating technologies.
Facilitates efficient, seamless link from research to production to maximize reuse of work products and eliminate waste.
Benefits of Modeling & Simulation
SAE 2010-01-2325
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System Simulation Need
Vehicle systems are becoming more complex and integrated Need to study interactions between systems
Variation analysis and robustness studies Evaluate sensitivity to parameters
Virtual integration vehicle Eliminate need to wait for hardware and complete
software availability
SAE 2010-01-2325
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System Simulation Challenges
Need for flexible modeling Level of fidelity suitable to task
Many complex models need to be connected Hundreds of inputs and outputs Time consuming and error prone to do manually
All control system functionality must be included Many control function models are not available Existing software in production
SAE 2010-01-2325
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Autonomie Development
Argonne and GM– 3 year CRADA Plug-and-Play Powertrain and Vehicle Model
Architecture and Development tool Successor to PSAT Proposal for industry standard modeling
architecture Supports model reuse and flexible system re-
configurability
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Control System Development Process
System Requirements
System Design
Software Design
Coding
SoftwareIntegration
Hardware/SoftwareIntegration
System Integration & Calibration
MIL
RP
OTRP SIL
PIL
HIL
MIL:RP:OTRP:PCG:SIL:PIL:HIL:
Model-in-the-LoopRapid PrototypingOn-Target Rapid PrototypingProduction Code GenerationSoftware-in-the-LoopProcessor-in-the LoopHardware-in-the Loop
PCG
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System Simulation and DesignDevelop plant models for all sub-systems
Physical Models Empirical Models Initialization/preprocessing files to calibrate plant models from test data
Build a complete system simulation in Autonomie
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Virtual Algorithm Development
Perform Algorithm design in the virtual environment (MIL) Add Simulink algorithm model to the simulation Design the algorithm in the context of the system, including SIL
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Algorithm ModelSIL block
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Testing and Validation (SIL) Integrate production code into the Autonomie vehicle model (SIL) Test in the virtual environment Use to represent control functionality that’s not modeled
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Testing and Validation (SIL)Autonomie manages/automates interconnections between subsystems having hundreds of Input/Output signals (e.g., controller I/O)
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SummaryEstablishes tool and framework for enterprise-wide collaboration
Common framework for all MBD activities
Provides complete user customization by an open architecture
Simulates from single components, subsystems to entire vehicles
Manages models, data, processes, results and controller code from research to production
A software environment and standard framework
SAE 2010-01-2325
Additional information at www.autonomie.net
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