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TechnicalPapers
－29－
Keihin Technical Review Vol.6 (2017) Technical Paper
Key Words: Dynamic modelling, DoE, Calibration, Optimization, Gasoline direct injection engine
Taro SHISHIDO＊１ Jing HE＊１ Masataka KAIHATSU＊１
Carsten HAUKAP＊２ Thomas DREHER＊２ Michael HEGMANN＊２
1. Introduction
In recent years, much effort has been expended
to improve the model based calibration (MBC)
m e t h o d o l o g i e s t o c o p e w i t h t h e i n c r e a s i n g
requirements of the emissions legislation. Due
to this development and in combination with the
introduction of efficient calibration processes, the
workload of the calibration engineer has been
reduced. Even more importantly, the required test
bench time was also very much reduced.
Furthermore, it became more and more important
to model the transient behaviour of the engine to
build up an environment to simulate the entire
vehicle. Here, a simulation environment for vehicle
and cycle simulations for the optimization of
emissions and operation and aftertreatment strategies
will be introduced.
Even though the field of dynamic modelling is
still a focus topic of methodology development,
this paper introduces the dynamic MBC process
for the daily calibration work: a process based
on the measurement of time resolved data and
the introduction of a sophisticated model building
process for dynamic engine models as well as a
process for global map optimization. The benefits
of this development is versatile, a massive gain
of quality in terms of optimization results, and
traceability and efficiency, in combination with a
reduction of cost and test bench time.
2. Calibration Process Using Dynamic DoE Models
The calibration process using dynamic DoE
models consists of seven steps: the first step is the
task definition to define the overall target of the
calibration task and known limitations and conditions.
A short test bench phase follows to retrieve engine
constra ints and boundar ies . During tes t p lan
phase the entire experimental designs necessary
for the given task are created. The actual engine
identification is done in a second short test bench
phase to collect the data followed by the dynamic
modelling phase. The introduced transient MBC
process allows a very flexible handling. Whereas
the global map optimization or cycle simulation is
the main focus, the steady state information and
models are automatically retrieved. Thus the process
is flexible for both approaches, steady state and
dynamic calibration work, refer to Figure 1(1).
2.1. Task Definition
The overall task is the base calibration of a
Dynamic Modelling for Gasoline Direct
Injection Engines※
＊１ System Development Department, R&D Operations ＊２ IAV GmbH
※ Received 20 July 2017, Reprinted with permission from IAV GmbH, original publication in, Automotive Data Analytics, Methods and Design of Experiments (DoE), proceedings of the International Calibration Conference, May 11-12, 2017, Berlin. Copyright © 2017 IAV GmbH.
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Dynamic Modelling for Gasoline Direct Injection Engines
throttle position are the result of the boost pressure
controller. This constraint is important as the final
models are dependant on the controller calibration.
Finally, the results of the global optimization
using dynamic models will be compared with
the conventional steady state MBC process. This
comparison is easily possible, because the steady
state DoE model can be easily retrieved form the
dynamic DoE models.
2.2. Test Bench Set-up
Figure 3 shows the engine test bench set-up.
The test bench system iTest operates the engine
and dynamometer, auxiliary systems as well as
all measurement devices. INCA is used to operate
the ECU and is hooked up to the engine via an
direct injected 4 cylinder gasoline engine. Whereas
the derived dynamic models can be used for the
majority of base test bench calibration tasks,
such as air charge, torque and temperature model
calibration, this paper describes the example of a
camshaft optimization. The optimization task is a
global closed loop optimization of a WLTC cycle
for a given vehicle and engine combination. To meet
realistic engine operation, the dynamic models are
combined with a model of the ECU in a Simulink
environment. The given WLTC also defines the
operating range of the dynamic models. Additionally,
the WLTC cycle, replicated on the engine test
bench, is used to retrieve frequency and gradient
information for the later test design and for the
validation of the final DoE models.
The control parameters of the DoE models are
chosen to be engine speed and air charge, intake
and exhaust valve timing, rail pressure and start
of injection, lambda and finally spark timing. For
this examination, the boost pressure controller
identification has decided not to be an explicit
task. Therefore the turbo charger wastegate and the
Fig. 1 Flowchart of transient calibration process
Fig. 2 Input and output parameters for engine model
Task Definition Constraints measurement Experimental Design• Static Constraints test(Base Map, Boundary Finder)
• Dynamic Constraints test(Mode cycle test: NEDC, WLTC ...)
Dynamic Modeling
• Engine constraints
• Input Parameters
• Model Ranges ...
• Sinusoidal Chirp Test
• Ramp Type A/B Test
• Dynamic Boundaries
Steady State Optimization
Mode Cycle Optimization
• Export steady state model
• Constraints definition
• Optimization (EasyDoE)
• Export dynamic model
• Create ECU model
• Constraints definition
• Optimization
Identification Test
• Sinusoidal Chirp
• Ramp Type A/B
• Validation test
• Delay times
• Validity check
• Model Definition
Calibration parameters• Engine speed (Speed)• Cylinder suction air mass (air-mass)• Intake valve timing (IVT)• Exhaust valve timing (EVT)• Ignition timing (IG)• Excess air ratio (Lambda = λ)• Start of injection (SOI)• Fuel pressure (Pf)
Outputs• BSFC• Fuel mass flow• COV• Soot• PN• THC• NOx• CO
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Keihin Technical Review Vol.6 (2017)
ES910. The tes t bench system ORION is the
main component to control the engine. ORION
is connected to iTest us ing the ASAP3 High
Speed Server and is connected to INCA using the
Direct INCA Interface. This set-up allows parallel
control of the dynamometer as well as the access
to all engine maps within a time frame of 10Hz.
Furthermore, this set-up in combination with the
sampling rate is fundamentally able to react on limit
violations, especially knocking. Here, the knock
detection is realized using the measurement device
KIS4.
2.3. Constraints and Boundary Detection
To ensure optimal test design for dynamic DoE
model building, information of the engine’s design
space and frequency is mandatory. Here, to detect
the steady state parameter ranges, the ORION
Boundary Finder function is used to automatically
screen the multidimensional operation range of the
engine(2), (3). Figure 4 shows as example of the results
of the engine dependencies of the engine parameter
as three dimensional convex hulls.
The information of the engine and parameter
operation range is extremely important to avoid limit
violations within the test design. Using the Boundary
Finder results, the risk of exceeding an engine limit
while exciting the engine dynamically may not be
completely avoided, because of the steady state
nature of the methodology, but very much limited.
For the definitions of the static and dynamic test
plan boundaries, the later purpose of the calibration
task is essential. For an unknown engine, it may
be important to define full parameter ranges for the
test design to be able to cover the complete engine
operation range for a base calibration or to evaluate
engine potentials.
If the calibration task is more specific or to
improve the model quality, it may be advantageous
to limit the parameter range towards a reasonable
offset of the input domain. Figure 5 shows two
examples of a design space for a full parameter
range and a limited range using an offset for given
engine maps.
Finally, for dynamic engine excitation the setting
speed of the parameters must be defined. Here, the
dynamic modelling toolbox (DMT) automatically
estimates the maximum gradients using the power
spectral density (PSD) for each defined input
parameter of a given measurement. Typically any
valid engine cycle such as the WLTC or FTP has
been proved to provide sufficient information of
dynamic DoE models. Figure 6 plots the PSD and
frequency domain of the input parameters.
Fig. 3 Engine test bench layout
DMT/EasyDoE
ORIONOffline
Direct INCA Interface
Direct INCA InterfaceMeasurement: 100HzParameter: 50HzMap: 20Hz
exhaust gas analyzer
combustion analyzer
Soot analyzer
BEX-5700G-E (Direct)BEX-650G (CVS)
KIS4
AVL483
MEXA-2100SPCSPN counter
ASAP3-HS ServeriTest
INCA
ES910 ECU+ETKDynamo
Fig. 4 3-D convex hull of the engine design space
CA50 (deg ATDC)
Pf (
kPa)
IVT
(de
g)
IVT
(de
g)E
VT
(de
g)
EVT (deg)
KCA50 (deg ATDC)
CA50 (deg ATDC)
CA50: Timing of mass combustion ratio 50%K: Excess fuel ratio = 1/λ
K
CA50 (deg ATDC)K
40
30
20
10
0
4030
20
5060
1.11.2
1.31.4
1.5
80
60
40
20
0
4030
20
5060
010
2030
40
80
60
40
20
0
4030
20
5060
2.5
× 104
2
1.5
1
0.5
0
4030
20
5060
1.11.2
1.31.4
1.5
1.11.2
1.31.4
1.5
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Dynamic Modelling for Gasoline Direct Injection Engines
2.4. Experimental Design
The DMT is used for the design of the test plans.
The objective of the test design is to stimulate the
system engine in a way to be able to identify a time
dependant, empirical data driven model. For this, the
test design must follow basic boundary conditions.
The system must be s t imulated in the ent i re
frequency domain of interest. Also, the excitation
must be twice as fas t as the reques ted t ime
resolution of the later model (Shannon theorem). To
obtain the required time dependant system response
of the engine, a sinusoidal pattern is used as test
design. Here, the pattern is an amplitude modulated
chirp signal, where the amplitude represents the
parameter range and the chirp frequencies represent
the later frequency resolution of the time dependant
model (parameter gradients), refer Figure 7(a).
To improve the steady state prediction capabilities
of the model, it is recommended to introduce a test
design with a steady state excitation, as shown in
Figure 7(b), because a sinusoidal excitation does
not cover the very low frequency regime for w ω
→ 0. Finally to cope with large time scales of the
system reaction, a stepwise excitation as shown in
Figure 7(c) may also be used as an option. Overall
temperature changes for dynamic cyclic excitation
are understood as system responses with large
time scales. Further typical examples are the boost
pressure rise (delay) for a fast engine acceleration.
The latter design types: the ramp and hold excitation
as well as the step excitation, are special cases of
the known APRBS signal (amplitude modulated
pseudo random signal). Here, the DMT offers the
advantage to adopt the test design more easily and
efficient to the desired model task.
The final test plans are calculated to provide
a space filling or optimal design space coverage.
Main influencing parameter to control the design
space coverage in is the duration of the excitation
sequence, especially for the sinusoidal test plans or
the number of ramp and hold points.
The introduction of individual zones for the test
design bears additional advantages: Main advantages
are a better design space coverage, flexibility in case
of different engine control modes (varying injection
patterns, boost pressure control modes, etc.). Finally,
the test design should not exceed a time limit to
Fig. 6 The spectral analysis of input parameters
Fig. 7 Test design types of the DMT
Pow
er s
pect
ral d
ensi
ty (
dB)
Frequency (Hz)0.9 1.0
Speedair-massIVTEVTIGλPfSOI
0.80.70.60.50.40.30.20.10.0
151050
20
-5-10-15-20-25-30
(a) Sinusoidal chirp excitation (b) Ramp and hold excitation
10s >30s
(c) Step excitation
maximal Parameter Variation
minimal Parameter Variation
Excitation Range
Speed (rpm)air-mass (g)
40
30
20
10
50
0
0
40003000
20001000
50000.8
0.20.4
0.6
0.0
EV
T (
deg)
Boundary Finder Variation
EV
T (
deg)
maximal Parameter Variation
minimal Parameter Variation
Excitation Range
Speed (rpm)air-mass (g)
optimal Base Map Setting40
30
20
10
35
25
15
45
05
40003000
20001000
0.80.20.40.6
0.0
Fig. 5 Example of input parameter ranges for the test design(Left: Full parameter range, Right: Limited range using an offset to a given map)
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Keihin Technical Review Vol.6 (2017)
avoid large result files and to allow emission bench
purging and calibration and engine conditioning.
Figure 8 shows an example the zoning introduced
for this investigation.
2.5. Measurements & Limit Violations and Limit
Reaction for Dynamic Testing
The highest priority while operating the engine
on the test bench is to prevent the engine from
damages due to restricted engine setting. Further, the
later model quality strongly depends on the quality
of the collected data. Therefore, it is mandatory
to install a test bench automation which is able
to operate and excite the engine dynamically and
being able to watch and react on limit violations.
Especially, to prevent a gasoline engine from
knocking, over temperature and misfire is extremely
challenging.
Two methodologies for the reaction of limit
violations will be introduced below.
2.5.1. Safe Track Methodology
A straight forward solution for a system reaction
in the case of a limit violation named ‘Safe Trace’
is shown in Figure 9. The safe trace corresponds to
a parameter combination, which is known to be safe,
for example the base settings form the maps for the
given speed and load combination.
In case of a limit violation, the automation
system steps all parameters towards the safe trace
within a definable step range until the limit violation
is removed. After a short stabilization time, the
automation system ramps back all parameter towards
the normal demand trace.
The disadvantage of this methodology is, that
typically all base maps are optimized for steady
state engine operation. Hence, the risk of violating a
limit while moving and operating the engine on the
safe track may not be completely excluded.
2.5.2. Limit Reaction Using Offsets
For a gasoline engine operated at high speed and
load, the ‘Safe Trace’ methodology turned out not
to be flexible and fast enough to react on engine
knocking, over temperature and misfire in parallel.
The limiting factor is, that the methodology does not
allow an evaluation of the root cause of the violation
and the reaction always follows the same procedure.
In order to cope with an individual handling of
different violation cases, ORION offers up to three
limit groups, which can be mapped individually
Fig. 8 Splitting of the design space into zones
1000 4000 Speed
air-massZone II Zone I
Zone III to VI
Zone VII (Idle)
Zone II (λ<1)Zone V
Zone IIIVII
Zone VI
Zone IV
Table 1 Test designs and test plan duration planned for this investigation
IIIIIIIVVVIVII
IIIIIIIVVVIVII
IIIIIIIVVVIVII
Zone4 × 45min4 × 45min4 × 45min4 × 45min4 × 45min4 × 45min1 × 45min2 × 45min2 × 45min2 × 45min2 × 45min2 × 45min2 × 45min
--
1 × 45min1 × 45min1 × 45min1 × 45min1 × 45min
-
Duration3.03.03.03.03.03.00.751.51.51.51.51.51.5--
0.750.750.750.750.75
-
Total Time [h]Model Purpose
Cycle Simulation
No
1
2
3
Excitation Type
Sinusoidal
Ramp and Hold
Step Excitation
31.5Sum
Fig. 9 Limit reaction using a ‘Safety Trace’
modified demand values
Ramp back to Normal Trace withModified demand valuesFeedback
Hold Time
Ramp Tim
e
Normal Demand Trace
Step Width(Normal Trace - Safe Trace)/Steps(Bsp.: 2 Steps)
Safe Trace
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Dynamic Modelling for Gasoline Direct Injection Engines
steady state model can easily be retrieved to be
available in the IAV EasyDoE environment.
2.7. Model Validation
For the verification of the models a vehicle cycle,
reproduced on the engine test bench, was used. Here,
the cycle was the same as for the determination of
the parameter gradients for the test design.
The advantage of a replicated vehicle cycle is
that any influences of the measurement devices can
be neglected. To rate the model quality, various
statistical characteristics are taken into account, such
as the absolute and relative RMSE (root mean square
error), MSE (maximal square error) R² (coefficient
of determinat ion) , and MAD (mean absolute
deviation). For a visual validation, Figure 11 shows
on the left hand side a time based comparison of the
measurement and on the right hand side a predicted
over observed plot. The plot allows a fast evaluation
of the overall robustness, outliers and limitations.
In addition, but not shown in this paper, the design
space of the model is checked against the parameter
ranges of the cycle to avoid or determine model
extrapolation.
2.8. Model Results
In the following, the model results will be
briefly introduced and discussed on the base of a
comparison of a cycle measurement and a cycle
simulation of the models. From this, three cycles
have been replicated on the test bench: FTP, NEDC
to a given violation. This allows a reaction to a
violation without provoking another limit violation.
Furthermore, the limit reaction of the systems allows
a reaction within the time frame of one sample, here
~10Hz of the used ASAP3-HS interface. This turned
out to be fast enough to operate the engine safely.
2.6. Data Post Processing and Model Building
Two model types are available in the DMT, the
parametric Volterra series polynomial model and the
Gaussian process model. Further, the model building
process is very much simplified, the latest version
even allows to find optimal model parameter settings
automatically. The advanced user is, of course, still
able to tune the models individually varying various
settings, such as output transformation, model
orders, feedback, filter types or model limits and
restrictions.
Certainly, the quality of the modes strongly
depends on a ca re fu l da t a pos t p roces s ing .
Especially, the alignment of the channels is very
important. For this, the DMT offers two approaches.
At fi rs t , the gas t ravel or dead t imes of the
measurement is determined automatically. Using this
information, the models are trained. In the second
step the models are optimized to handle rise times.
Using this technique the later models turn out to
be optimal to cope with complex system behaviour,
such as thermal inertia of the engine and probes,
analyser characteristics, etc. Figure 10 shows an
example of a system response with a large dead and
rising time.
The models are available as common Matlab
m-files as well as Simulink models. Further, a static
Fig. 10 Delay time Fig. 11 Validity check
Time Shift dead time rising time
0 30 70605040 802500200015001000500 3000
60
55
50
45
40
35
30
65
25
time (s)
60
55
50
45
40
35
30
65
25
actu
al f
uelin
g (m
m3 /
strk
)
actu
al f
uelin
g (m
m3 /
strk
)
∆fueling (t - 0.1) (mm3/strk)
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Keihin Technical Review Vol.6 (2017)
and WLTC, refer Figures 12 - 16.
While evaluating the models, one has to take
one issue into account: the models are trained for
fired engine operation, only. Fuel cut conditions
are not taken into account. Therefore all results
show large deviations for this engine operating
mode. Additionally, the shown model results are
extrapolated. All calculated errors (RMSE) include a
model extrapolation.
2.8.1. Fuel Mass Flow Model
The overall normalized RMSE of the fuel mass
flow model is up to 1.3% for the set of all three
vehicle cycles, showing very small deviations against
the measurement on a time resolved scale. The
steady state prediction capability is very good.
2.8.2. THC Model
The overall normalized RMSE of the THC
model is up to 3.5% for the set of all three vehicle
cycles, showing acceptable deviations against the
measurement on a time resolved scale. The steady
state prediction capability is acceptable.
2.8.3. NOx Model
The overall normalized RMSE of the NOx model
is up to 3.5% or 195ppm absolute for the set of all
three vehicle cycles, taking the fuel cut conditions
into account, the results would be much better. The
steady state prediction capability is very good.
2.8.4. Soot and PN Models
The overall normalized RMSE of the soot and
PN models are up to 3.5 and 4% for the set of all
Fig. 12 Fuel mass flow model
12
10
8
6
4
2
0
-2
14
0 2.521.510.5
× 104
Validationmeasurementmodel
FTP NEDC WLTC
Fuel
mas
s Fl
ow (
g/s)
3500
3000
2500
2000
1500
1000
500
0
4000
0 2.521.510.5× 104
Validation
FTP NEDC WLTC
NO
x (p
pm)
Fig. 14 NOx model
0 2.521.510.5
3.5
3
2.5
2
1.5
1
0.5
0
4× 104
measurementmodel
FTP NEDC WLTC
Validation
TH
C (
ppm
C)
Fig. 13 THC model
2.5
2
1.5
1
0.5
0
3
0 2.521.510.5
× 104
Validation
FTP NEDC WLTC
PN (
#/cc
)
× 104
Fig. 16 Particle number model
1
0.8
0.6
0.4
0.2
0
-0.2
1.2
0 2.521.510.5× 104
Validationmeasurementmodel
FTP NEDC WLTC
Soot
(m
g/cm
3 )
Fig. 15 Soot model
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Dynamic Modelling for Gasoline Direct Injection Engines
three vehicle cycles. Here, the overall error assumes
an acceptable model quality, whereas an evaluation
on second-by-second base shows large deviations,
especially for the acceleration phases of the vehicle.
Although the trends come out correctly, the second-
by-second accuracy is not acceptable. (R² = 0.85
to 088). Low reproducibility of the soot emission
measurement devices may be a reason.
3. ECU Map Optimization and Verification
Two optimization approaches are discussed
in this paper. At first, a global steady state MAP
optimization. These results will be compared with a
global and dynamic optimization of a given CYCLE.
3.1. Global Steady State MAP Optimization
The global s teady state MAP optimization
methods follows a two step procedure. Start point
for the global optimizations are the calculated local
optima for a given distribution of speed and load
on the base of steady state models. In order to
take smoothness of input as well as output model
parameters into account, the gradient for each node
is calculated upon its surroundings and is used as
constraint for the second optimization loop. Main
advantage of this procedure is the process robustness
and calculation speed. Here, the steady state models
are retrieved from the dynamic models. Not used for
this evaluation, the method would allow to define
a cumulated output values as additional constraint
or map dependant weights. The following Table 2
summarizes the constraints used for the optimization.
3.1.1. Verification 1: Comparison of Steady State
Maps
Figure 17 compares the original measured base
maps of the engine (left) with the optimized maps
that have been optimized with respect to BSFC.
In general, both calibrations, the original base
maps and the optimized maps, show the same
tendency of the emissions and fuel consumption
levels. Further, the results allow a qualitative
Table 2 Constraint condition of calibration
Setting UnitCondition Parameter name ValueTarget Output gMin fuel-mass -
-Max HULL 1-Constant lambda 1.00-Gradient Input dx 10
Constraint -Gradient Input dy 10%Max COV 3.0
mg/m3Max Soot 1.0#/ccMax PN 1.00E+06
--
- start value 3Optimize Setting
- Global opt √
Fig. 17 Comparison of measured base maps (left) and optimized maps (right)
BSFC [g/kw·h] (ECU base map)D
yno_
Trq
[N
m]
Dyn
o_T
rq [
Nm
]BSFC [g/kw·h] (Optimize map)
COV_Cyl2 [%] (ECU base map) COV_Cyl2 [%] (Optimize map)
Dyn
o_T
rq [
Nm
]D
yno_
Trq
[N
m]
Soot [mg/cm^3] (ECU base map) Soot [mg/cm^3] (Optimize map)
PN [#/cm^3] (ECU base map) PN [#/cm^3] (Optimize map)
Dyno_Speed [rpm] Dyno_Speed [rpm]
Dyn
o_T
rq [
Nm
]D
yno_
Trq
[N
m]
THC [ppmC] (ECU base map) THC [ppmC] (Optimize map)
NOx [ppm] (ECU base map) NOx [ppm] (Optimize map)
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Keihin Technical Review Vol.6 (2017)
comparison only, because the cost function for
the optimization was defined as optimal in terms
of BSFC, but not set up in a way to al low a
quantitative comparison.
The main deviations are close to full load for
BSFC and COV and maximum speed and load for
the NOx emissions. The root cause for the described
deterioration is a different calibration strategy of
enrichment.
Further, large deviations can be explored in
the very low load area. Here, the root cause is a
different calibration of the fuel pressure and start of
injection, refer Figure 18.
Due to the large uncertainty of the PN/PM
models, the particles will not be taken into account
to evaluate the optimization procedure.
However, the results show the efficiency and
the transferability of the optimization process for
base calibration or the engine. Especially, the very
important task of camshaft can be done very fast.
3.1.2. Verification 2: Comparison of Cumulated
CYCLE Results
Figure 19 shows the cumulated emissions and fuel
consumption for the given WLTC validation cycle.
For a constant fuel consumption (fuel-mass) the
optimized maps show significant higher levels for
the THC and NOx emissions of the optimization
compared to the base maps. The soot emission
are almost constant whereas the particle number
is doubled. As already explained previously, the
particle models are not reliable and thus they will
not further be discussed.
Parts of the significantly higher cumulated
THC and NOx emissions can be explained by the
different operating strategies of the cycle replication
and the cycle simulation. For the cycle simulation,
the fuel cut has not taken into account, here, the
models are always over predicting the emissions.
Further, the model quality for idle and low load are
not satisfactory, and this may also explain additional
deviation. Whereas the engine operating strategy as
well as sufficient models can be provided for idle
etc., additional deviations are the result of dynamic
engine operation. And this can not be explained by
the steady state models.
3.2. Global Dynamic CYCLE Optimization
In order to meet optimal results for real engine
or vehicle behaviour the optimization has to be done
on the base of a given cycle. Thus the target is to
optimize the static engine maps of the ECU towards
optimal results for dynamic engine driving. For this,
dynamic engine models are mandatory.
The Optimization Frame Work (OFW) is an
optimization toolbox that allows complex dynamic,
cycle based map optimization. In the example shown
Fig. 18 Fuel pressure and λ
Dyn
o_T
rq [
Nm
]D
yno_
Trq
[N
m]
Pf (ECU base map) Pf (Optimize map)
ECU base map Rambda
Speed [rpm] Speed [rpm]
Optimize map Rambda
Fig. 19 Comparison of base calibrated and the global steady state optimization for a cumulated cycle
2
1.5
1
0.5
2.5
0Total fuel mass Total Soot Total PN Total THC Total NOx
ECU Base Map
Optimize Results
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Dynamic Modelling for Gasoline Direct Injection Engines
Fig. 20 Model overview
Fig. 21 Verification of the ECU model accuracy
4. Conclusion
This paper introduced and explained the process
for dynamic model building for gasoline engines.
Starting from the task definitions and test design
and model building, requirements for the automation
system have been discussed. Two options for a limit
reaction in case of a limit violation were shown.
Further, the results of a global steady state
map optimization and a global dynamic cycle
optimization have been shown and discussed.
As an outlook for the future, the dynamic models
in combination with the introduced calibration
processes proved to be ready for base calibration
work for the daily mass production.
Reference
(1) Kaihatsu, M.; He, J.; Shishido, T.; Haukap, C.;
Dreher, T.; Hegman, M.: Dynamic Modelling
Calibration for Gasoline Direct Injection Engine,
Presentation in Powertrain Calibration Conference
2016 (in Japanese), Akihabara, Tokyo
(2) He, J.; Kakimoto, F.; Sato, F.; Haukap, C.;
Dreher, T.: Steady State Calibration for Catalyst
Heat -up Opt imiza t ion on Gasol ine Direc t
Injection Engine, DoE in Engine Development,
Berlin, 2015
in this paper, the target cycle of the WLTC shall
be the base for the map optimization. In order to
increase the accuracy of the results, the dynamic
models of the engine are combined with a Simulink
model of the ECU. Figure 20 shows the set-up of
the model structure to perform the global dynamic
map optimization for a given vehicle cycle.
To meet real engine behaviour the accuracy of
the ECU model is mandatory. To evaluate the ECU,
Figure 21 shows for the given WLTC, the fuel
pressure (Pf), camshaft positions (IVT, EVT) and the
spark (IG) of a bench measurement and of the ECU
model used. The overall accuracy is very good, only
the deviation may be found due to not supported
control modes.
Finally, Figure 22 shows the optimized maps for
Simulink Model of optimization
Mode C
ycle In
put
(WLTC)
ECU Control M
odel
Dynamic E
ngine Model
Pf
IVT
EVT
IG
Bench MeasurementSimulation
Fig. 22 ECU map generated by OFW
IVT
(de
g)
EV
T (
deg)
Speed (rpm)Speed (rpm)VT_MAP_Y
Optimize results (IVT)
40
20
60
0
0 0
80006000
40002000
250200
150100
50
20
40
0
0 0
80006000
40002000
250200
150100
50
Optimize results (EVT)
VT_MAP_Y
a camshaft optimization for the given WLTC. As
constraints, the same conditions have been applied as
stipulated in Table 2 of the steady state optimization.
TechnicalPapers
－39－
Keihin Technical Review Vol.6 (2017)
Authors
T. SHISHIDO M. KAIHATSU
Although to create simulation models containing
engine transient characteristics is always a big
challenge, Keihin/IAV’s collaboration show the
possibility by actual case that engine behavior is
reproduced with high accuracy. Many research topics
still remain, but we believe that our job contribute
to a reworking-effort reduction of calibration from
actual vehicle test. I would like to thank all the co-
authors, meaningful technical discussion during
collaboration and paper organization has been a
good experience for me. (SHISHIDO)
(3) He, J.; Kakimoto, F.; Sato, F.; Haukap, C.;
Dreher, T.: Steady State Calibration for Catalyst
Heat -up Opt imiza t ion on Gasol ine Direc t
Injection Engine, Keihin Technical Review, Vol. 4,
pp. 17-27, 201
(http://www.keihincorp.co.jp/technology/tec_
report_201512.html)
C. HAUKAP
J. HE
T. DREHER
M. HEGMANN
