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1© 2016 The MathWorks, Inc.
Addressing Challenges in Designing Powertrain
System using Model-Based Design
Vijayalayan R
Manager
Control Design Application Engineering
MathWorks India Pvt Ltd
Design
with
Simulatio
n
Executable
Specification
s
Continuous
Test and
Verification
Automatic
Code Generation
Models
2
Emission Reduction
World-wide trend
India: phasing in BS VI and new
drive cycle requirement
China: fuel injection on all road vehicles,
phasing in National 5 and beyond
Off-road: EU/US looking beyond Tier-4
Closing the gap between real-world
emission and lab testing
Change is the essence of auto industry
Algorithms and software are vital part of the solution
Rising design complexity
3
Automotive Companies Moving to Model-Based
Design to Increase “Capacity for Complexity”
INTEGRATION
IMPLEMENTATION
DESIGN
Environment Models
Physical Components
Algorithms
CAD toolsVHDL,
VerilogC, C++
TE
ST
& V
ER
IFIC
AT
ION
RESEARCH REQUIREMENTS
Other
hardwareMCU DSP FPGA ASIC
4
Design
with
Simulation
Executable
Specifications
Continuous
Test and
Verification
Automatic
Code Generation
Models
Virtual Engine
Calibration
Optimization
Model-Based System
Engineering
Frontloading
Embedded Software
Development
Engineering Data
Analytics
Current Trends in Model-Based Design
5
Design
with
Simulation
Executable
Specifications
Continuous
Test and
Verification
Automatic
Code Generation
Models
Virtual Engine
Calibration
Optimization
Model-Based System
Engineering
Frontloading
Embedded Software
Development
Engineering Data
Analytics
Current Trends in Model-Based Design
6
Iterate
Build Test
Optimize Engine
Calibration: Days
Engine Development Process
Model and Design
: 4 Years
Problem: late assessment of engine design changesIdea: front-load calibrationChallenge: computing power, unproven processMathWorks proposal: Virtual Engine Calibration Optimization Process
7
Enablers: engine CAE models, parallel computing, virtual dynamometer, cloud
15 Engine Speeds
X
15 Engine Torques
X
4 Variables
900 Calibration ValuesThrottle Position
Turbo
Wastegate AreaIgnition Timing
Air/Fuel Ratio2
31
4
8
Build TestModel, Calibration,
and Evaluate
Result
20 minutes on
225 PC Cores
Calibrate via Numerical Optimization
9
Mercedes-AMG
ChallengeOptimize engine calibration for high-performing and
environmentally-friendly powertrains
SolutionUse MathWorks tools to develop a calibration tool
that enables simultaneous testing of multiple
variables
Results Calibration process streamlined
Euro 6 compliance goals achieved
Faster and more fuel-efficient cars developed
“We developed a custom engine
calibration tool using MathWorks tools
that enables engineers at all levels of
expertise to extract the highest
possible performance from AMG
powertrains. The tool supports the
entire calibration process, from Design
of Experiments to optimization.”
Hasan Uzun
Mercedes-AMG GmbH
Link to article
AMG calibration tool main menu.
10
Design
with
Simulation
Executable
Specifications
Continuous
Test and
Verification
Automatic
Code Generation
Models
Virtual Engine
Calibration
Optimization
Model-Based System
Engineering
Frontloading
Embedded Software
Development
Engineering Data
Analytics
Current Trends in Model-Based Design
11
MODEL-BASED DESIGN ADOPTION TRENDS IN AUTOMOTIVE
Code Generation Has Become Mainstream for
Production Programs
Using Hand-Code in Models Production Code Generation from Models
Hand Code Modeled Elements None Some Most All
(Translated)
12
Subsystem
Design
Subsystem
Integration and Test
System
Integration and Test
Complete
Integration and Test
System-Level
Design
Requirements
Subsystem
Implementation
Stakeholder NeedsHIL Testing
TRENDS OF MODEL-BASED DESIGN
Frontloading Embedded Software Development
Automotive industry has
embraced HILS
Current focus: systematic
verification of software functions
in model, before HILS
Benefit: reducing manpower and
time for embedded software
Source: Continental presentation at MathWorks Automotive Conference 2008, Stuttgart, Germany
13
Subsystem
Design
Subsystem
Integration and Test
System
Integration and Test
Complete
Integration and Test
System-Level
Design
Requirements
Subsystem
Implementation
Stakeholder Needs
Optimize Design
Evaluate Design
Alternatives
Analyze System
Behavior
Analyze interaction between subsystems
Trade-off design options
Assess requirement feasibility
Size key components
Before building prototypes
System Level Simulation
14
ChallengeAccelerate the development of complex engine control
system software
SolutionDevelop a comprehensive engine model and combine it
with SIL+M testing to frontload the development process
Results Comprehensive engine model developed
Designs verified early in development
Difficult-to-test conditions simulated
Toyota Front-Loads Development of Engine
Control Systems Using Comprehensive Engine
Models and SIL+M
A Toyota engine.
Link to user story
“Simscape enables us to create a
comprehensive model of the
engine appropriate for our design
tasks that is easily understood by
all teams. Closed-loop simulations
of the ECU and engine performed
in Simulink, completed as early as
possible, are essential to our front-
loaded development process.”
Dr. Hisahiro Ito
Toyota Motor Corporation
15
Design
with
Simulation
Executable
Specifications
Continuous
Test and
Verification
Automatic
Code Generation
Models
Virtual Engine
Calibration
Optimization
Model-Based System
Engineering
Frontloading
Embedded Software
Development
Engineering Data
Analytics
Current Trends in Model-Based Design
16
Trend: Data Economy
“Information is the oil of the 21st
century, and analytics is the combustion engine”
Peter Sondergaard, Gartner Research
Develop
Predictive
Models
Access and
Explore DataPreprocess Data
Integrate Analytics
with Systems
17
Safran Engine Health Monitoring Solution
http://www.mathworks.com/company/events/conferences/matlab-virtual-conference/
Beyond Virtual Sensors: Prognostics
18
Safran Engine Health Monitoring Solution
Monitor Systems
– Detect failure indicators
– Predict time to maintenance
– Identify components
Improve Aircraft Availability
– On time departures and arrivals
– Plan and optimize maintenance
– Reduce engine out-of-service time
Reduce Maintenance Costs
– Troubleshooting assistance (isolate faulty element)
– Limit secondary damage
Performance- Modular analysis- Thermodynamic cycle
Oil System
- Smart filter- Debris
- Consumption
Liftoff- Tracking- Monitoring ignition
Control System- Sensors- Actuators- Troubleshooting assistance- Errors and Warnings
Fuel System- Smart filter- Fuel pump
General- Anomaly detection- Decision support- Fleet monitoring
- Imbalance- Vibration- Transient events
Mechanical Health
Enterprise
Integration
• Real-time analytics
• Integrated with
maintenance and service
systems
• Ad-hoc data analysis
• Analytics to predict failure
• Suite of MATLAB Analytics
• Shared with other teams
• Proof of readiness
DesktopCompiled
Shared
19
Design
with
Simulation
Executable
Specifications
Continuous
Test and
Verification
Automatic
Code Generation
Models
Virtual Engine
Calibration
Optimization
Model-Based System
Engineering
Frontloading
Embedded Software
Development
Engineering Data
Analytics
Current Trends in Model-Based Design
20
Thank You