One single simulation model in development deployed broadly across organization is beneficial in order to reduce effort in maintenance, and to ensure consistency, and possibly multi fidelity. To run simulation over multiple simulation environments one needs to separate the model from the execution environment.

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2. ONE MODEL IN MULTIPLE ENVIRONMENTS
• Define once
 Reduces effort to develop and maintain
 Ensures consistency
• Deploy broadly
 Bring the model to those that can benefit from it
 Open standards, manageable and affordable usage
 Matlab/Simulink, MS Excel, Driver-in-the-Loop, ....
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3. 80+
ONE MODEL
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MIL/SIL/HIL
© Modelon 2016
Model authoring
Model
Deployment

4. VALUE OF ONE MODEL
• Consistent model across organization
and use-cases
• Choose execution tool depending on purpose
• FMI open standard format: model supported by 80+ CAE tools
• Modelica open standard: user extensibility and choice of tool vendor
• Unlimited model deployment: cost for authoring – not per user
You are in control of your model IP!
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”Being able to dig into the details and study how each
component or subsystem affects the entire vehicle
behavior is the key to an efficient and systematic analysis.
Bengt Jacobson, Volvo Car Corporation

6. • Parameter management is consistent regardless of application
• Modify data without regenerating execution code
PARAMETERIZATION
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Execution environment
Courtesy: Volvo
© Modelon 2016 6
• Hardpoints
• Spring data
• Damper data
• Bushing data
• Part data
• Parameters
Authoring environment

7. PARAMETERIZATION-INDEPENDENT TOPOLOGY
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No parameterization/default
parameters

8. OVERLAY DATA ON TOPOLOGY
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• Hardpoints
• Spring data
• Damper data
• Bushing data
• Part data
• Parameters
Data-aware components
Supports different file formats
xml, json, mat
User extensible
Utilities to read and write data

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”“The openness of the code essentially gives us the
advantages of an in-house tool without having to have a
software development team in-house.”
Mats Beckman, Volvo Car Corporation

10. INTEROPERABILITY
• System architecture
• Vehicle model with
external models for:
 Brakes
 Steering
 Powertrain
 Tires
 Controls
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Courtesy: Nissan
(©Modelon 2016 10

11. INTEROPERABILITY
• Combine with 3rd party models, steering example
Native Modelica Imported FMU Model for export
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12. ASSEMBLY
• Same/similar complexity
parametric geometry
• Driven by part data from
CAD
MULTI FIDELITY
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COMMON INTERFACE
All model fidelity interchangeable
in all supported execution
environments
BEHAVIORAL
• Driven by KnC data
• Compatible with many
different data formats
• Executable faster than
realtime on a single core
Courtesy: Volvo
PARAMETRIC GEOMETRY
• Driven by geometry (hardpoints)
• Reads existing data format
• Executable on realtime on
multiple cores
REQUIREMENTS
• Early phase models
• Low fidelity
• Very fast execution
Requirements
verification
System design
optimization
Verification

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”[We now have] an intuitive and highly customizable
design [that] runs powerful physical models through FMI.
Vehicle Energy Management Engineering (VEME),
Ford Motor Company

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15. MULTI-DOMAIN: INTEGRATED VEHICLE SIMULATION
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Lateral stability
Drivability
Launch
performance
Fuel economy
Emissions Thermal
management
Transmission
Prius
Alison
Dual clutch
Manual
Engine
I4
Downsizing
Cooling
Engine
Transmission
Battery
Motor
Air Conditioning circuit
Electric drive
Electric
motor and
front diff
Chassis
McPherson
Multi-link
Battery
Lithium-Ion
Modelica and FMI technologies are used to investigate hybridization:
• From: Conventional Rear Wheel Drive (RWD)
• To: Hybrid Electric Drive by
• Downsizing engine
• Change of transmission
• Adding front axle electric drive
Key enablers of the technologies:
• Cover multiple domains and their interaction in the same
system model
• Plug-and-play compatible component and system models,
• System architectures designs
• Component based validation
• Multiple fidelity levels within the same framework
• Export of models to other tools and applications

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”Modelica was very suited to make such a multi-discipline and
multi-domain investigation by model-based development
Yutaka Hirano, Toyota Motor Corporation

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18. REAL TEST RIG
(EXISTING)
ADDITION
CUSTOM ENVIRONMENT: VIRTUAL TEST RIG
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Presentation at 1st
Japanese Modelica
Conference, Tokyo,
May 23-24

19. CUSTOM ENVIRONMENT: ADAS
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Presentation at 1st Japanese Modelica Conference, Tokyo, May 23-24

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”The vehicle model [allows] engineers to replace, modify,
add and refine system blocks such as tires, aerodynamics,
engine, suspension, driveline [and to] link external blocks.
Andrea Toso and Allesandro Moroni, Dallara

21. REALTIME
• High Immersion Realism
• Track testing too slow and expensive
• Even at later development stages
• Detailed models required early in design
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SAE PAPER 2014-01-0099 PROFESSIONAL DRIVING
SIMULATORS TO DESIGN FIRST-TIME RIGHT RACE CARS

22. REALTIME
• High fidelty
• High accuracy
• Prepared for paralellization
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Presentation at 1st Japanese Modelica Conference, Tokyo, May 23-24

23. Multi-Domain
Multi-domain by design
• Interaction and cross-dependencies between
subsystems and physical domains captured
• Facilitates simultaneous engineering
• Allow for integrated design and optimization
Shift control
Cooling
Hybrid electric
Gearbox
Hydraulics
Engine
A/C
SUMMARY: KEYS TO MODEL-BASED SYSTEMS DESIGN
Multi-Fidelity
Inherent support for working with mixed fidelities
• Get the architecture right
• Keep the design connected and consistent
• Continuously evaluate design against requirements
• Executable specs <-> Detailed design
BEHAVIORAL – PARAMETRIC – VERIFICATION
SYSTEM–SUBSYSTEM–PART
Multi-Simulation
Formal and open description
• Physics capture
• Constraint and cost definition
• Analysis and decision support
• Reduction/elemination of real
tests
Examples:
• Dynamic simulation
• Steady-state
• Optimization
• Realtime/XIL
• Controls design
• Robust design
• Requirements definition
• Formal analysis9/13/2016 © Modelon 2016
Multi-Access
Models accessible for use by
everyone that needs it, on their
conditions.

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