engine virtual calibration platform using physical real ...€¦ · 2019, 28 - 29 october 35th...
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![Page 1: Engine Virtual Calibration Platform Using Physical Real ...€¦ · 2019, 28 - 29 October 35th INTERNATIONAL CAE CONFERENCE AND EXHIBITION G. Boccardo - Engine Virtual Calibration](https://reader036.vdocument.in/reader036/viewer/2022081614/5fc5bcc612dd631bfe463ad8/html5/thumbnails/1.jpg)
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Engine Virtual Calibration Platform Using
Physical Real-Time Integrated Models
Dr. Giulio Boccardo
Guido Giardino
Diego Zanella
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www.caeconference.com 235th INTERNATIONAL CAE CONFERENCE AND EXHIBITION 2019, 28 - 29 October G. Boccardo - Engine Virtual Calibration Platform
Introduction
▪ Control system development within the automotive industry is evolving rapidly due to
three main drivers (in addition to the evergreen cost-reduction):
Regulatory Pressure
• Pollutants (EU6d w/ RDE)
• CO2 emissions (CAFE)
Shorter Time-to-Market
• Shorter lifecycle
• More differentiation
Control System Complexity
• Vehicle Electrification
• More DoFs
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CAE in the Control Development
▪ Virtualization of the product development process (V-Cycle) becomes mandatory,
simulation is used throughout to replace the plant (prototypes):
Functions
Specification
Controller
Design
SW Calibration, ValidationV-Cycle
HiL
SiL
MiL
• Reduced need for
(costly) experimental
tests
• Shorter lead time for
DoEs, optimizations
(scalability)
V-Cycle
Testing
Matrix
• Control system
development moved
earlier in the process
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Hardware-in-the-Loop Simulations
Functions
Specification
Controller
Design
SW Calibration, ValidationV-Cycle
HiL
SiL
MiL
Vehicle Input Signals
Actuator Signals
ECU
HiL HW
Actuators
RT Simulation
FRMs
▪ HiL is a fundamental enabler for control Virtual
Calibration and Validation
→ SHORTER TIME-TO-MARKET
• HiL requires Real-Time (RT) Engine/Vehicle
plant models
→ FAST-RUNNING MODELS
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Fast-Running Fully-Physical Engine ModelDetailed model: 191 volumes 0D FRM: 18 volumes
▪ GT-SUITE 1D fluid-dynamics engine models a de-facto standard in automotive industry▪ Detailed models ~50x slower than RT but can be
simplified into fully physical 0.5xRT FRMs
Tra
de-O
ff be
twee
n ac
cura
cy
and
com
puta
tiona
l effi
cien
cy
▪ RT-Capable GT-POWER FRMs still based on the very same templates and fluid-dynamic solution
→ HIGHLY PREDICTIVE
Detailed Model already
present within OEMs!
Efficient Re-Use of
Available Information
“Development and assessment of a Fully-physical 0D Fast Running Model of an E6 passenger car Diesel engine for ECU testing on a Hardware-in-the-loop system” SIA Conference, 2015, PWT - FCA
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▪ Albeit very computational efficient,
FRMs are still very accurate
▪ Predictive and highly flexible, no need
to rebuild them when changes occur
▪ Can be run in steady-state and
transient conditions
Fast-Running Fully-Physical Engine Model
10
10
10
10
10
10
10
10
101010
20
20
20
30
30
4040
2020
10
10
10
10
50
10
10
10
20
Engine Speed [RPM]
Lo
ad
[%
]
1500 2000 2500 3000 3500 4000 45000
10
20
30
40
50
60
70
80
90
100
0
5
10
15
20
25
30
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40
45
50
Exhaust Manifold Temp
Absolute error [°C]
4
4
4
4
4
444
4
4444
8
8
8
4
4
8
812
8
4
88
Engine Speed [RPM]
Lo
ad
[%
]
1500 2000 2500 3000 3500 4000 45000
10
20
30
40
50
60
70
80
90
100
0
5
10
15
2
2
2
2
2
2
2
2
2
2
2
2
2
22 2
2
2
4
4
2
22
4
44
46
4
4
2
6
8 6
Engine Speed [RPM]
Lo
ad
[%
]
1500 2000 2500 3000 3500 4000 45000
10
20
30
40
50
60
70
80
90
100
0
5
10
15
4
4
4
4
812
4
Engine Speed [RPM]
Lo
ad
[%
]
1500 2000 2500 3000 3500 4000 45000
10
20
30
40
50
60
70
80
90
100
0
5
10
15
Turbine Inlet Pressure
Relative percentage error [%]
Air flow
Relative percentage error [%]
BSFC
Relative percentage error [%]
Average error:
2.0 %Average error:
2.1 %
Average error:
4.9 %
Average error:
10.8 °C
“Development and assessment of a Fully-physical 0D Fast Running Model of an E6 passenger car Diesel engine for ECU testing on a Hardware-in-the-loop system” SIA Conference, 2015, PWT - FCA
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Simulink 3
Aftertreatment GT-RT
▪ GT-SUITE FRMs are encapsulated into Simulink I/O masks, compiled and deployed onto HiL
Hardware (e.g. dSpace). Multi-Model and Multi-Core simulations are supported:
FRMs Deployment on HiL Machines
Simulink 1
Engine GT-RT
Engine Model to Core 2
EATS Model to Core 4
HiL System Services
plus Vehicle model to
Core 1
Simulink 2
Cooling GT-RT
www.dspace.com
“Multi-Core HiL Simulation of an Integrated Engine & Cooling Model” GT-SUITE European User Conference, 2015, PWT - FCA
ECU
Actuator Signals
Sensor Signals
HiL HW
Vendors
Cooling Model
to Core 3
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The problem of Engine CalibrationDiesel Engine
Injection Strategy:
▪ 2 → 4 Auxiliary injection events
▪ 2x Parameters Ɐ Injection event
▪ Pattern Positioning (SOI)
▪ Injection pressure
→ 10 Parameters only for injection pattern
Air Management Strategy
▪ Boost Pressure
▪ EGR level and split (Dual Loop Circuit)
▪ Swirl Flap
→ 4 Parameters
TOT: 14 Parameters → 3 levels Ɐ params → ~5 millions Combinations!
TARGET:
Optimization of
emissions and fuel
consumption
VIRTUAL TEST BENCH
huge saving potentials
Engine Performance
Engine Out Emissions
Tailpipe Emissions
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Online Approach (HiL)0.8x FRM: TimeStep: ~0.30 ms; Flow Volumes: ~15
▪ Coarse gas dynamic
▪ Mapped Wall Temperatures
▪ Mapped Combustion
▪ Mapped or SP Emissions
EXP Data Needed: DoE (103)
Development Time: 3-6 Months
Virtual Calibration in a Nutshell
Offline Approach (SiL)5x FRM: TimeStep: ~0.05 ms, Flow Volumes: ~100
▪ Accurate gas dynamic
▪ Wall Temperature Solvers
▪ Predictive Combustion Models
▪ Predictive Emissions Models
EXP Data Needed: Engine Maps (102)
Development Time: 1-2 Months
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Online Approach (HiL)0.8x FRM: TimeStep: ~0.30 ms; Flow Volumes: ~15
▪ Coarse gas dynamic
▪ Mapped Wall Temperatures
▪ Mapped Combustion
▪ Mapped or SP Emissions
EXP Data Needed: DoE (103)
Development Time: 3-6 Months
Virtual Calibration in a Nutshell
Offline Approach (SiL)5x FRM: TimeStep: ~0.05 ms, Flow Volumes: ~100
▪ Accurate gas dynamic
▪ Wall Temperature Solvers
▪ Predictive Combustion Models
▪ Predictive Emissions Models
EXP Data Needed: Engine Maps (102)
Development Time: 1-2 Months
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▪ Semi Predictive Emission Models
Light-duty Truck Diesel (Euro VI)
▪ Engine Map Results
Semi-Predictive Diesel Emissions
𝑁𝑂𝑥, 𝐶𝑂, 𝐻𝐶, 𝑆𝑂𝑂𝑇 = 𝑎0 ∗ෑ
𝑘=1
𝑝
𝑥𝑘𝑎𝑘
*Percentage error bands limited to 1% absolute error
▪ 𝑥𝑘 are the independent variables
▪ 𝑎𝑘 are the coefficients, result of the
calibration process
▪ 𝑝 is the number of regressors, index
of the model complexity
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Semi-Predictive Diesel Emissions▪ Semi Predictive Emission Models
▪ Medium-size passenger car Diesel (Euro VI)
▪ Emission maps tested on different Driving Cycles
EXPERIMENTAL: Driving cycle simulation with
experimental emission map
PREDICTED: Driving cycle simulation with emission
map evaluated via SP emission model
NOx - WLTC HC - WLTC- 0.31 %
0.89 %
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Online Approach (HiL)0.8x FRM: TimeStep: ~0.30 ms; Flow Volumes: ~15
▪ Coarse gas dynamic
▪ Mapped Wall Temperatures
▪ Mapped Combustion
▪ Mapped or SP Emissions
EXP Data Needed: DoE (103)
Development Time: 3-6 Months
Virtual Calibration in a Nutshell
Offline Approach (SiL)5x FRM: TimeStep: ~0.05 ms, Flow Volumes: ~100
▪ Accurate gas dynamic
▪ Wall Temperature Solvers
▪ Predictive Combustion Models
▪ Predictive Emissions Models
EXP Data Needed: Engine Maps (102)
Development Time: 1-2 Months
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▪ Predictive Diesel Combustion Model: DI-Pulse
▪ Phenomenological combustion model developed by GT.
▪ Designed to handle modern multi-pulse injection
▪ Multi-zone approach (3 zones per pulse)
▪ Improved thermal description of in-cylinder phenomena
▪ Improved results of emissions models
▪ Combustion rate is predicted based on injection rate &
in-cylinder conditions:
▪ Injection timings and profiles
▪ Pressure and temperature
▪ Mixture composition (fresh air, fuel, EGR/residuals)
Physical Diesel Emissions Modeling
Courtesy of Gamma Technologies
ሶ𝑚𝑎ሶ%𝐸𝐺𝑅
𝑅𝑃𝑀
𝑝𝑐𝑦𝑙𝐼𝑀𝐸𝑃𝑇𝑜𝑟𝑞𝑢𝑒…
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Physical Diesel Emissions Modeling▪ Predictive Diesel Combustion Model: DI-Pulse
Medium-size passenger car Diesel Engine (Euro VI) with predictive combustion.
“Set-up and Validation of an Integrated Engine Thermal Model in GT-SUITE for Heat Rejection Prediction” SAE Paper 2019-24-0078, PWT – Jaguar Land Rover
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91%81%
73%
20% 10% 5%
% o
f P
oin
ts
Tolerance*
N. of OP within error bands:
*Percentage error bands limited to 10ppm absolute error
Physical Diesel Emissions Modeling▪ Predictive Diesel Emission Model: NOx
Medium-size passenger car
Diesel Engine (Euro VI) with
predictive combustion.
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Aftertreatment Modeling▪ Physical Catalyst modeling:
▪ Requires dedicate testing of a Lab-Scale reactor sample on a synthetic flow bench.
▪ Real time and fully predictive
▪ Ex: Physical Model of a SCR on Filter
Millo F., Rafigh M., Fino D., Miceli P. , Application of a global kinetic model on an SCR coated on Filter (SCR-F) catalyst for automotive applications, Fuel Volume 198
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Conclusions▪ Control system
development within the automotive industry is evolving rapidly towards an ever-increasing complexity.
▪ A process was identifiedwhich consents the virtualization of the engine control development andcalibration reducing the need for testing and making an extensive re-use of available resources.
▪ This process relies heavily on state-of-the-art CAE simulation of theengine gas-exchange, combustion and pollutant emission formation torealize a digital-twin of an engine test bench which can be used foronline and offline control calibration and optimization.
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THANK YOU FOR YOUR ATTENTION
Any Question?POWERTECH ENGINEERING S.R.L.
Via C. Invernizio 6, 10127 Torino, IT
www.pwt-eng.com
Giulio Boccardo
+39 011 3036481