model based development and calibration - home - avl.com
Post on 16-Oct-2021
7 Views
Preview:
TRANSCRIPT
1
Model based development and calibrationInnovative ways to increase calibration quality within the limits of acceptable development effort!
2
MODEL BASED DEVELOPMENTChallanges
3
Next steps EC/ JRC:• Definition of PEMS boundaries (temperature, idling, altitude, inclination,…)• Beginning of 2013: Screening of small fleet• PEMS most likely to come, methodology to be defined until mid 2013• Definition how EC will survey ISC (in addition to OEM tests)
EUROPEAN EMISSION LEGISLATIONPASSENGER CARS
4
ENGINE SPEED / LOAD DISTRIBUTION EXAMPLES OF REAL WORLD DRIVING VS. TEST
Rela
tive L
oad
, %
Engine Speed, rpm500 1000 1500 2000 2500 3000 3500 4000
0
10
20
30
40
50
60
70
80
90
100
RDEPEMS
<100 km/hmoderate
Driver
Rela
tive L
oad
, %
Engine Speed, rpm500 1000 1500 2000 2500 3000 3500 4000
0
10
20
30
40
50
60
70
80
90
100
NEDC
Rela
tive L
oad
, %
Engine Speed, rpm500 1000 1500 2000 2500 3000 3500 4000
0
10
20
30
40
50
60
70
80
90
100
WLTP
<100 km/hdynamic
Driver
5
Load Collective NEDC vs. RDE
N - upm
Md -
Nm
Random Test
RandomTest
PEMS: all modes possible
Options for RDE:
• Random test cycle:Chassis DynoSimulation
• PEMS:Measurement in customer driving with PEMS
���� Decision openNEDC
6
MODEL BASED DEVELOPMENTIntention
7
Challenges in the Powertrain Developmentand AVLs Solutions
Model-Based Development
CO2 / Fuel Consumption
Real Driving Emissions
Broad Vehicle Portfolio
AVL Solutions
Reduction of
development costs€
Reduction of
development time
Keep quality standards1
Clustering of hardware
and engineering activities
Increased system
complexity (EAS, OBD, Hybridization)
Multi-variant simulation with the
same engine family (RDE, OBD, EAS)
Reduced test facilities/variant
through front-loading
Improved quality and robustness
through additional virtual validation1
Independence of environmental testing
from seasonal conditions and vehicles
availability
8
Model Based DevelopmentWhat is it?
• Model based development using a real time capable engine model
• Starting from concept phase until SOP calibration
• Engine model based on semi-physical modeling approach
���� empirical model components derived from AVL experience and test bed data
���� physical components increase the range of application due to better extrapolation
• Easy usability due to the use of suitable simulation environments
Increasing system
robustness within given
development duration
and budget by transferring
development from
real to virtual testing
9
Definitions - Model Accuracy Levels
Maturity Level Description Use Cases
Level 1 Only the main geometrical
data of the engine are used
as input for model set-up
• Concept study and decision
• ECU algorithm design
• Exhaust gas aftertreatment (EAS)
concept
Level 2 Measurement data is used
to make a refinement of the
model to increase accuracy.
• Pre-Calibration: the possible
calibration tasks depends on focus
of the model parameterization
• Used for specific calibration tasks
Level 3 Model is adapted to steady
state and transient data,
measured at AVL. Highest
accuracy which is needed
for model based calibration.
• Variant calibration support
• Ambient correction calibration
(altitude/hot/cold)
• EAS calibration strategy
• OBD calibration support
• Robustness investigations
• ECU algorithm verification
10
Virtual
Real
Te
stb
ed
re
su
lts
EAS System (DOC,
Semi-physical
Thermodynamic
NOx-Emission
EAS System (DOC, DPF, SCR, NLT)
Empirical
static global
HC, CO, Soot, SPL,…
DoE Measurements
number of engine
Combined-model
Increased number of engine specific outputs
HiL Setup
MiLSetup
Baseengine testbed development
Emission validation
Environmental validation
Field data
Modelrefinement
Pre
-ca
libra
tio
n
Do
E T
est
Re
su
lts
Fie
ld d
ata
Robustness analysis
Pre
-ca
libra
tio
n
First engine run
MoBEO basic model
setup
MoBEO refined model
setup
Model-based calibration of various variants
Variant specific hardware change(e.g. intake piping, …)
(No combustion HW change)Semi-physical
Basic Model without
measurement data
Model based DevelopmentModelling Process
11
Zyk
lus
Erg
eb
nis
NO
x [
mg
/km
]
400
420
440
460
480
500
520
540
560
580
600
Kra
fts
toff
verb
rau
ch
[L
/km
]
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
0 100 200 300 400 500 600 700 800 900 1000 1100
time [s]
To
urq
ue [
%]
0
20
40
60
80
100
En
gin
e s
peed
[%
]
0
20
40
60
80
100
Te
mp
. tu
rb.
inle
t [°
C]
100
200
300
400
500
600
Inta
ke a
ir m
ass f
low
[%
]
0
20
40
60
80
100
NO
x C
co
ncen
trati
on
[p
pm
]
0
100
200
300
400
500
Measurement
Simulation
Cycle
re
su
lts
NO
x [m
g/k
m]
Cycle
fu
el c
on
su
mp
tio
n [m
g/k
m]
Cycle results - NEDC
Model AccuracyHigh model accuracy as base for model based calibration
Typical deviations of the cycle emissions and fuel consumption as well as achievable temperature accuracy:
• Fuel Consumption < 3%
• NOx Emission < 5%
• Insoluble Particulate Emission < 10%
• Temperature Intake Side < 10°C
• Temperature Exhaust Side < 20°C
12
Model AccuracyHigh model accuracy as base for model based calibration
Typical deviations of the cycle emissions and fuel consumption as well as achievable temperature accuracy:
• Fuel Consumption < 3%
• NOx Emission < 5%
• Insoluble Particulate Emission < 10%
• Temperature Intake Side < 10°C
• Temperature Exhaust Side < 20°C400 450 500 550 600 650 700 750 800
Time [s]
T.
Tu
rbin
e-I
nle
t [°
C]
300
350
400
450500
550
NO
x C
on
ce
ntr
ati
on
[p
pm
]
0
200400
600
800
1000
Op
ac
ity
[%
]
010
20
3040
50
En
gin
e S
pe
ed
[%
]
050
100
To
rqu
e [
%]
0
20
4060
80
100
Inta
ke
Air
Ma
ss
flo
w [
%]
0
20
4060
80
100
Measurement
Simulation
13
MODEL BASED DEVELOPMENTApplication Environment
14
Model Based Development Virtual test beds – Comparison Simulation Environment
Model in the Loop (MiL)
Advantages
+ Simulation faster than real time (app. 5 times)
+ No hardware parts needed
+ Simulation on normal PC possible
Disadvantages
- Setup of software ECU time consuming
- typically not all ECU functionalities available
- Hard to achieve equal control behaviour as real engine
Hardware in the Loop (HiL)
Advantages
+ All ECU functions available
+ Pre-Calibration of all ECU functions possible
+ Possibility of ECU software and dataset validation
Disadvantages
- Only real time simulation possible
- Need of hardware in the loop test bed
- Need of hardware parts
���� Both environments can be used for pre-calibration of specific tasks
15
Standard Hardware-in-the-Loop test bed
� HiL operation system
� HiL automation system
� Calibration software (INCA,..)
Suitable Application Environment – As Prerequisite to Integrated a Model Based Calibration Methodology in an exciting Application Team
Advanced AVL Hardware-in-the-Loop test bed extended with
� Advanced semi-physical powertrain model
� PUMA & CAMEO
Same interface for the calibration engineer as real test bed
� PUMA Host Connection
Simulation results stored in same format as real test data
� Post-Processing
Same CONCERTO Layouts can be used for data analyzing
� Calibration Software (INCA,…)
16
Application – From Virtual Test Bed to SOP Virtual Test Beds as Extension of Real Test Facilities
Test Bed Virtual Test Bed Vehicle Validation
Calibration
Load Profiles
Environment
Production Tolerances Aging Effects
17
MODEL BASED DEVELOPMENTUse - Cases
18
Model Based Development Concept Investigations
Model based concept investigations
� Assessment of technology route
� Simulation of transient behaviour of engine in early concept phase on MiL
environment
� Definition of possible concepts considering the interaction between
� engine
� exhaust aftertreatment system
� software and calibration
� Sensors and actuators
� environmental conditions
Vehicle & drivetrain simulation
19
DPF Loading Limit
active
Regenera
tion
passiv
eR
egenera
tion
HDDE 2ltr/cyl. class
EGR + DPF
Boundary conditions:Tier4i engineNOX/soot ratio < 10 [g/g]
If NOX/soot ratio increasesDPF soot load in balance pointwill be lowered.
Low engine out total soot emissionsallows also for conditions were no CRT effect is observable a long operationwithout DPF regeneration.Soot Model accuracy can be reduced to a worst case scenario.
Model Based DevelopmentConcept Investigations – DPF Soot Loading
Simulation of specific duty cycles for different applications with respect to DPF soot loading behavior on HiL system � Calibration validation
20
700
800
900
1000
1100
1200
1300
1400
1500
1600
1700
1800
1900
2000
Engine Speed [1/min]
Tem
p. u
pstr
.Tu
rbin
e [
°C]
0
200
400
600
800
Pre
ssu
re u
pstr
. T
urb
ine [
kP
a]
0
125
250
375
500
LP
TC
Sp
eed
[rp
m]
0
50000
100000
HP
TC
Sp
eed
[rp
m]
50000
75000
100000
125000
150000
Tem
p. d
s. C
om
pre
sso
r [°
C]
0
100
200
BM
EP
[kP
a]
800
1600
2400
Model Based DevelopmentCalibration of Ambient Corrections
Simulation of full load altitude operation for validation of ambient correction and engine protection functions
970mbar = 350m (Graz)750mbar = 2500m660mbar = 3500m540mbar = 5000m
Limits for component protection
Limit temperature upstream turbine
Limit temperature downstream compressor
Limit pressure upstream turbine
Limit LP turbochargerspeed
Limit HP turbochargerspeed
No derating up to 2500 m
21
700
800
900
1000
1100
1200
1300
1400
1500
1600
1700
1800
1900
2000
Engine Speed [1/min]
Tem
p. u
ps
tr.T
urb
ine [
°C]
0
200
400
600
800
Pre
ss
ure
up
str
. T
urb
ine [
kP
a]
0
125
250
375
500
LP
TC
Sp
eed
[rp
m]
0
50000
100000
HP
TC
Sp
eed
[rp
m]
50000
75000
100000
125000
150000
Tem
p. d
s. C
om
pre
sso
r [°
C]
0
100
200
BM
EP
[kP
a]
800
1600
2400
Model Based Development Calibration of Component Protection Functions
Simulation of engine failure at full load for validation of engine protection functions
Limit temperature upstream turbine
Limit temperature downstream compressor
Limit pressure upstream turbine
Limit LP turbochargerspeed
Limit HP turbochargerspeed
Limits for component protection
22
Pre-calibration
� Pre-calibration of thresholds, enable/releaseconditions, debouncing
� Simulation of fault-parts (e.g. EGR-orifice, broken DPF, etc.)
� Multi-Variant calibration (e.g. adjustment fromlead-calibration to a follow-up variant)
� …
Ideas for Application of Model-Based-Development in OBD Calibration
Validation of calibration
� Functional check of calibration (Tested-Flag, P-Codes, etc.)
� Check of IUMPRs at different driving profiles (e.g. using same calibration for a commercial truck and a city bus)
� Robustness investigation and tolerances� …
Evaluation and R&D projects currently ongoing
23
Calibration on Hardware-in-the-Loop test beds Virtual Test Beds as Extension of Real Test Facilities
Calibration
Driving Cycle
Environment
Production Tolerances Aging Effects
Powertrain calibration tasks for HiL test bed
� Pre-calibration of different calibration work packages
� Calibration for non-standard ambient conditions
� Calibration of component protection
� Vehicle/Engine derivate calibration
� RDE – Real Driving Emission evaluation
� Real world fuel consumption optimization
� Sensitivity studies taking into account system interactions
� Software and dataset validation
24
Front-LoadingExample Facilities
HiL
Engine Testbed
Chassy Dyno
Road
Ideal Lead Variant Calibration Project (i.e. no
relevant H/W changes)
25
FacilitiesHiL
Engine Testbed
Chassy Dyno
Road
Ideal Lead Variant Calibration Project (i.e. no
relevant H/W changes)
Multi-variant projects can be addressed by: an extension of the test environment through HiL (MiL/SiL) Testing• Keep calibration quality through additional HiL testing, though high number of variants• Multi-variant simulation (calibration clustering, RDE, EAS, OBD)• Keep test facilities usage by a feasible level• Make environmental testing more flexible and efficient
Front-LoadingExample
26
AV
L D
iesel-C
alib
ration
Qualit
y G
ate
s
Time
Cal. QualityS
Ot
-12 -9-15-18 -6-21-24
Month before SOP
G1 - Runnable
G2 - Refined
G3 – Target Achievement
G5 – Validated
G4 – Robust
conventional Calibration process
-27
model based Calibration process
Calibration Process and Dataset Management
Validation by:
1. Dataset Management (Dataset Merging, Clustering, Tracing)
2. Automated HiL Dataset validation und consideration of
different production tolerances in advance to dataset freeze
3. A extensive model based fleet validation with active search for
critical events.
-30
27
Da
tas
et
-Q
ua
lity
model based
Concept Vehicle developmentTest bed development Fleet validationVehicle developmentTest bed development Fleet validation
� Shifting of development tasks in earlier phases and well proven concept decisions
� Reduction of project duration due to additional virtual test facilities
• Independent from environmental conditions
• Independent from vehicle availability
• Higher efficiency for real testing
�High dataset quality due to good
monitoring and solid validation
Concept Vehicle developmentTest bed development Fleet validation
Vehicle developmentTest bed development Fleet validation
Conclusion - Innovative ways to increase calibration quality within the limits of acceptable development effort!
28
AVL LIST GMBHYour Independent Engineering Partner
Thank you for your attention
top related