Model-Based Systems Engineering and Control System ...

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

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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

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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

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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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Autonomie System Simulation

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Controller Blocks in Autonomie

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Process Selection in Autonomie

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Simulink Model Built by Autonomie

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Control Algorithm Template

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Control Algorithm Template Populated

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Autonomie UtilityThe Autonomie Utility provides an interface to GM’s CM system

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GM SIL Process Overview

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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