Optimizing Performance and Fuel Economy of a Dual-Clutch
Transmission Powertrain with Model-Based Design
Vijayalayan R, Senior Team Lead, Control Design Application Engineering,
MathWorks India Pvt Ltd
Pete Maloney, Senior Principal Technical Consultant, MathWorks Inc.
Wit Nursilo, Senior Application Engineer, MathWorks Inc.
© 2014 The MathWorks, Inc.1
Problem Statement
Determine Numerically Optimal Transmission
Shift-Schedule Calibration and Axle Ratio For
Dual-Clutch Powertrain Design Concept With
Accurate Engine Fuel Consumption Model
2
Challenges for the Powertrain Engineer

Building a System Level Simulation Model of Vehicle

Designing and Verifying the Controllers along with the
Vehicle Model

Optimizing the System Parameters

Speeding up the Optimization Process
3
DCT Powertrain Axle Sweep Results
4
Agenda

System Level Simulation – An Overview

Modeling System Level Simulation of Vehicle

Estimation of Fuel Economy from Measured Data

Shift Schedule Optimization

Summary/Q&A
5
System Level Simulation
What is it?


Use simulation for performance and cost optimization at the
system (vehicle) level
Establish vehicle level requirements and a system
architecture for detail design of subsystems and components
6
System Level Simulation – Why do it?
Traditional vehicle design process
System level sim enabled design process
Specify Vehicle Design
Specify vehicle design
Many Iterations
= Many Costly
Prototype
Builds
Test Vehicle
Performance
Modify
Test Vehicle
Evaluate
Results
Opportunity for further
optimization
Finalize Vehicle Design
Design Cycle Time Difficult to Predict
Test Components
Build Test Vehicle
Build System
Level Simulation
Reduce prototype
builds & dev cost
Optimal vehicle
design
Improve
predictability of
design cycle time
Build Test
Vehicle
Validate System Level
Simulation
Optimize Design
through Simulation
Modify Test Vehicle
Finalize Vehicle Design
7
Agenda

System Level Simulation – An Overview

Modeling System Level Simulation of Vehicle

Estimation of Fuel Economy from Measured Data

Shift Schedule Optimization

Summary/Q&A
8
Model Development

Engine
– Engine Model
– Engine Calibration

Transmission
– Dual Clutch Transmission
– Auto-Driver (“Forward Model’)
– Vehicle
PI

Control
Control
– Engine Control
– Transmission Shift Schedule
9
Dual Clutch Transmission Model

6 speed Dual-Clutch Transmission

Vehicle Dynamics
10
Measured Data
Statistical Modeling
Engine and Calibrations

Statistical methods produce
accurate engine model
with optimized calibrations
Engine Calibrations
Statistical Models
– Design experiments
and collect data
– Generate engine models
using statistical methods
– Generate optimized calibrations
using analytical methods
Simulink Model
Physical
Testing
Design of
Experiments
High Fidelity
Simulation
Results
Data
Modeling
Calibration
Generation
Engine Model
Calibrations
11
Control Calibration is Included in the
Model
- Generate Optimal Engine Calibrations From Engine Model With
Numerical Optimization -
12
Tuning Abstracted Models
to Match Simulation Results
Model:
Detailed
Control
Abstracted
Problem: Simulation results of
>> Shift_Gear_Delay
detailed and abstracted model
do not match
Solution: Use Simulink Design
Optimization to tune abstracted
model parameters
>> Ratio_Time_Const
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Agenda

System Level Simulation – An Overview

Modeling System Level Simulation of Vehicle

Estimation of Fuel Economy from Measured Data

Shift Schedule Optimization

Summary/Q&A
14
Use Measured Data To
Estimate Fuel Economy
Model:
Control
Problem: Use simulation to
calculate a realistic estimate of
fuel economy
Solution: Use Curve Fitting
Technique to import fuel economy
data and generate a lookup table
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Agenda

System Level Simulation – An Overview

Modeling System Level Simulation of Vehicle

Estimation of Fuel Economy from Measured Data

Shift Schedule and Drive Axle Ratio Optimization

Summary/Q&A
16
Optimize Gear Shift Schedule & Drive Axle Ratio
To Minimize Fuel Use
Final Drive Axle Gear
Ratio (sweep)
Fuel Consumed
On FTP75
Drive-Cycle
Up Shift
0-100KPh
Acceleration
Time
Gear Shift
Schedules
(32 params)
Down Shift
Global Optimization Toolbox
patternsearch
Trade Off Acceleration Performance vs. Fuel Consumption Cost By
Optimizing Gear Shift Schedules as Final Drive Axle Ratio is Swept
17
Global Optimization Solver : Pattern Search
 Use Pattern Search Optimization for Robustness to Local Minima
Search Systematically
Steps Through The
Search Parameter Space
18
Optimization Process
Initialize Optimization Parameters
Generate 64 Shift Parameter Variations With
Pattern Search
Run 64 FTP75 Drive-Cycle Simulations
Size Pattern Search Mesh Smaller Than
Tolerance?
No
Yes
Report Results
(~15,400 FTP cycle simulations)
19
Speeding Up Optimization Process
 Use Rapid Accelerator for Stand
Alone Executables for Parallel
Computing.
for
Parameter Set 1
Parameter Set 2
…
 Use Parallel Computing To Make
Execution Speed Scalable and
Controllable
Multi
Core
Parameter Set N
i = 1:numSims
out{i} = sim(mdl, SimSettings{i});
end
parfor
i = 1:numSims
out{i} = sim(mdl, SimSettings{i});
end
 Use Distributed Computing Server
to execute optimization on a
computer cluster
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Use Nonlinear Numerical
Optimization Tools On The Model

Run ‘Axle Sweep’ To Find Performance vs. Fuel Economy
Tradeoff
Best Axle Ratio: 3.0 at
Performance Time <10s
Pattern Search Optimizer Re-Optimizes Performance and Normal Shift Schedules at
Each Axle Ratio Setting (This Process Takes ~33 Hours for 7 Axle Settings on 64
21
workers).
Optimal Shift Schedule Surfaces
Optimized
Baseline
Axle Ratio 3.0
3.8
Shift
Schedule
Axle Ratio
Base Opt 3.8 Opt 3.0
3.8
MPG
31.85
Performance
Time
8.03
3.8
3.0
5.8%↑ 12.5%↑
8.03
9.54
0~100KPH[s]
22
Agenda

System Level Simulation – An Overview

Modeling System Level Simulation of Vehicle

Estimation of Fuel Economy from Measured Data

Shift Schedule Optimization

Summary/Q&A
23
Challenges for the Powertrain Engineer

Building a System Level Simulation Model of Vehicle

Designing and Verifying the Controllers along with the
Vehicle Model

Optimizing the System Parameters

Speeding up the Optimization Process
24
Key Message
System level simulation addresses the challenges
involved in the design and optimization
Control
Algorithm
Transmission
and Vehicle
Calculate
Fuel Use
Parameter
Values
Optimization
Algorithm
25
Thank You
26