optimizing gaseous and particle emissions of a gdi engine ... · model of a t-gdi engine predicting...
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Future Powertrain Conference 2020
Optimizing gaseous and particle emissions of a GDI engine by coupling
a dynamic data based engine model with ECU injection structures
Thomas KruseThorsten HuberHolger KleinegraeberNicola Deflorio
Future Powertrain Conference 2020
Challenges of todays Powertrain Calibration
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Stricter emissions legislation (RDE) Hybridization/Electrification
Complexity of combustion engines Increasing Cost and Time Pressure
$
Future Powertrain Conference 2020
Example: How to reach a RDE optimal Base Calibration of a modern T-GDI Engine
3
Engine Operating Range: Speed Load (Torque)Engine Parameter to Calibrate: Fuel pressure Main injection timing (SOI) Factor 1st split injection Timing 1st split injection Factor 2nd split injection Timing 2nd split injection Inlet Cam timing Exhaust Cam timing
Inputs:
Emissions:
CO2/Fuel-Consumption
Particle
NOx
HC
CO
Other Boundaries:
Comb. Stability (CoV)
Drivability (smooth maps)
Targets:Challenge:
Optimize all 8 base
ECU maps in the
whole operating
range with respect
to different RDE
cycles
4-Zylinder, 1.3l, T-GDI, VVT
Replace the real engine with a data driven model
*DoE – Design of Experiments
Run a space filling DoE* on a steady state engine Dyno 1 Automatic build of a static engine model2 Global RDE optimization for all base maps3
First Step:
Model based Calibration
with ETAS ASCMO Static
Future Powertrain Conference 2020
Model of a T-GDI Engine predicting the influence of all inputs on all relevant outputs
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Automatic build of a high fidelity data
model using latest AI/ML* algorithms
Based on ~1400 DoE measurements
from a steady state engine dyno
Model covers the whole operation
range with 10 inputs in total
* AI = Artificial Intelligence; ML = Machine Learning
Future Powertrain Conference 20205
Model of a T-GDI Engine predicting the influence of all inputs on all relevant outputs
Automatic build of a high fidelity data
model using latest AI/ML* algorithms
Based on ~1400 DoE measurements
from a steady state engine dyno
Model covers the whole operation
range with 10 inputs in total
Future Powertrain Conference 20206
Model of a T-GDI Engine predicting the influence of all inputs on all relevant outputs
Automatic build of a high fidelity data
model using latest AI/ML* algorithms
Based on ~1400 DoE measurements
from a steady state engine dyno
Model covers the whole operation
range with 10 inputs in total
Future Powertrain Conference 20207
Model of a T-GDI Engine predicting the influence of all inputs on all relevant outputs
Automatic build of a high fidelity data
model using latest AI/ML* algorithms
Based on ~1400 DoE measurements
from a steady state engine dyno
Model covers the whole operation
range with 10 inputs in total
Future Powertrain Conference 20208
Particle emission (PN) for typical
RDE cycles are far too high with the
manual pre-calibration already in
steady state operation
Calibration maps are not sufficiently
smooth for operation in the vehicle
Set of real and simulated cyclesimported for global optimization
Predicted Cumulated Emissions on a set of RDE Cycles for manual pre-calibration
Model based Optimization of all 8 Maps with respect to RDE conditions
Future Powertrain Conference 20209
Set of real and simulated cyclesimported for global optimization
Predicted Cumulated Emissions on a set of RDE Cycles for optimized calibration Optimization leads to a significant
reduction of particle emission (PN)
and fuel-consumption compared
to the manual pre-calibration
Map smoothness is considered in the
optimization process
Results could be validated on a
steady-state dyno
Model based Optimization of all 8 Maps with respect to RDE conditions
Future Powertrain Conference 2020
But: Steady State Model is not sufficient to optimize Dynamic Effects
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Validation with RDE cycles on a
transient dyno shows much higher
emission in dynamic operation
especially for particle
Local peaks in particle emissions
are up to 5-times higher than
steady state model prediction
Cumulated particle emission
improvement “only” ~50% instead
of >90% as to be expected by
steady state results
Future Powertrain Conference 2020
Solution: Explicit modeling of Dynamic Behavior
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Outputy(t)
t
Inputx(t)
t
Dynamic data-basedEngine Model
Main transient effect from injection parameter:
‒ Main Injection Timing (SOI)
‒ Fuel Pressure
‒ Split Factor of 1st split injection
‒ Timing of 1st split injection
‒ Split Factor 2nd split injection
‒ Timing of 2nd split injection
Coverage of the whole operation range:
‒ Speed
‒ Torque
Total of 8 inputs for the required dynamic model
Second step:
Dynamic Modeling with
ETAS ASCMO Dynamic
DoE including dynamics measured on a transient dyno1 Automatic build of a dynamic engine model2 Prediction of transient drive cycle results3
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Dynamic Modeling: Transient DoE
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Space Filling Variation (Sobol) of
input amplitudes and gradients
for optimal system identification
Consideration of steady state
pre-calibration by various
input constraints (amplitude &
gradients) as maps or curves
Inclusion of steady state points and
“real cycle snippets” possible
Export in various formats
considering specific test bed
automation requirements
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Dynamic Modeling: Transient DoE
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Resulting Traces for all 8 Inputs considering various constraints
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Dynamic Modeling: Modeling Process
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Feedback model structure (NARX*) to learn time dependent behaviour
x1(t)
Structure for Dynamic Modelling
x(t)
x(t-1)
x(t-2)
y(t-1)
y(t-2)
y(t-3)
t
t-1 y(t)
t-1
t-1
t-1
t-1
x2(t)
x3(t)
Regression with SparseGaussian Process
*NARX: Nonlinear Auto Regression with External inputs
Internal Regression model with Gaussian Process (GP)
Automatic Model building, good generalization capability and extrapolation behavior Modified Sparse GP with reduced no. of base functions to cope with high no. of inputs and data points
Consideration of time effects by feedback of past input and output values up to a certain time horizon
Reduction of feedback structure complexity by “automatic feature selection”: only relevant inputs are used
Future Powertrain Conference 2020
Dynamic Modeling: Validation of Modeling Results
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Transient peaks in particle emissions
caused by sharp changes on inputs
can now be modeled more precise
than with former steady state model
Magnitude and integral of peaks can
be predicted with an accuracy of
approx. 30% (similar to the
repetition measurement error)
Particle Mass (Micro Soot)─ Measured─ Steady State Prediction─ Dynamic Model Prediction
Particle Mass (Micro Soot)─ Measured─ Steady State Prediction─ Dynamic Model Prediction
Future Powertrain Conference 2020
Dynamic Modeling: Validation of Modeling Results
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Comparison of model prediction with real cycle measurements (e.g. WLTC) shows good quality
Dynamic Model can now be
used to predicted effects of
changes in injection-calibration
parameter for any cycle
But:
How to optimize calibration
maps on a dynamic model
for transient cycles ?
Future Powertrain Conference 2020
Optimization requires Coupling of ECU Strategy with Dynamic Model
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Vehicle: Mass, Cw, gear, …
RDE Trips: Veh. speed, slope, …
Optimizer
Speed
Torque
Cycle Prediction
Output Change
Fuel_cum[l/100km]
-2 %
Soot_cum[mg/km]
-30 %
Dynamic Engine Model
S
Eng. SpeedTorq
ue
Injection Pattern:
• Single-Inj.
• Double-Inj.
• Triple-Inj.
SOI
Air_Charge
P_Fuel
Split_1
Frac_1
Split_2
Frac_2
Extract of ECU Injection Strategy
Engine Speed/Torque Trajectories
Future Powertrain Conference 2020
Tool Framework MOCA for the Optimization of Parameter in ECU Models
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Today widely used to calibrate physically based virtual sensor on ECU or XiL models
Speed
Air-Mass
Ignition
SystemInputs:
Data
From Engine / Vehicle with real sensors
Covering all input combinations
Steady state data from test bed
or Transient data from
vehicle test trip or model
Measured Engine Torque (System Output Yi, measured )
-
Modelled
Engine Torque
(Yi, predicted)
SystemOuput:
Extract of Gasoline Torque Structure
Calibration Task:− Find parameter values p minimizing the deviation
between measured- and modelled output− Additional constraints: Smooth maps, …
Ideal framework to couple ECU strategies with any plant model for optimization
Future Powertrain Conference 2020
Joint Optimization: ECU Injection Strategy with Dynamic Model on Real Cycles
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Workflow in MOCA: 1st step
Import a set of relevant
RDE driving cycles as
input for optimization:
Here: WLTC + RTS95 cycles
Available as Speed/Torque
trajectories (MDF-files)
from vehicle measurements
Future Powertrain Conference 2020
Joint Optimization: ECU Injection Strategy with Dynamic Model on Real Cycles
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Workflow in MOCA: 2nd step
Import the ASCMO-
DYNAMIC model
Connect/match channels
Future Powertrain Conference 2020
Joint Optimization: ECU Injection Strategy with Dynamic Model on Real Cycles
22
Workflow in MOCA: 3rd step
Make the relevant part of the
ECU-Function available:
here the injection strategy
Direct connection to Simulink-,
ASCET- or FMU-models
Other option chosen here:
Replication of ECU strategy
by a formula with an
easy to use calculator
Advantageous in terms of
calculation time and flexibility:
Changes in ECU strategy can
quickly be realized and tested
Future Powertrain Conference 2020
Joint Optimization: ECU Injection Strategy with Dynamic Model on Real Cycles
23
Workflow in MOCA: 3rd step
Make the relevant part of the
ECU-Function available:
here the injection strategy
Direct connection to Simulink-,
ASCET- or FMU-models
Other option chosen here:
Replication of ECU strategy
by a formula with an
easy to use calculator
Advantageous in terms of
calculation time and flexibility:
Changes in ECU strategy can
quickly be realized and tested
Future Powertrain Conference 2020
Joint Optimization: ECU Injection Strategy with Dynamic Model on Real Cycles
24
Workflow in MOCA: 4th step
Import all calibration
parameter used in the
ECU function
All standard calibration
parameter types and
formats supported
Here: Calibration maps from
steady state optimization
imported as reference and
start for optimization
Future Powertrain Conference 2020
Joint Optimization: ECU Injection Strategy with Dynamic Model on Real Cycles
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Workflow in MOCA: 5th step
The Optimization:
Define optimization criteria:
- Cumulated emissions
- Local constraints
- Map smoothing/gradients
Start optimization:
- Duration approx. 1 hour
Optimizer proposes significant
changes for some calibration
maps compared to the steady
state calibration (Reference)
Future Powertrain Conference 2020
Joint Optimization: ECU Injection Strategy with Dynamic Model on Real Cycles
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Results of Optimization compared to steady state calibration
Cumulates particle mass
over WLTC + RTS95 cycle
reduced by ~35%
New calibration eliminates
high particle emissions peaks
Cumulated fuel mass
reduced by ~1.5%
Other gaseous emissions
(NOx, CO, HC) remains
nearly unchanged
Results could be validated
precisely for fuel-mass and
for particle mass with some
repetition variation
- Actual Particle Mass Reference- Actual Particle Mass Optimized
- Cumulated Particle Mass Reference- Cumulated Particle Mass Optimized
- Cumulated Fuel Mass Reference- Cumulated Fuel Mass Optimized
- Engine Speed- Torque
- Actual Particle Mass Reference- Actual Particle Mass Optimized
Elimination of high particle
emission peaks
35% PM reduction
1.5% Fuelreduction
Future Powertrain Conference 2020
Summary
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Steady State DoE methods are an important first step for calibration. But especially particle
emissions are highly sensitive to transient effects which has to be considered for RDE calibration
Dynamic data based models combined with a suitable DoE allow to predict transient peaks sufficiently
well including effects of calibration changes
Coupling the relevant ECU strategy part with a dynamic model in a tool environment for ECU model
calibration allows a systematic optimization of the calibration parameter
Methodology is independent from combustion engine and can also be used e.g. for (H)EV optimization
Outlook: Fully virtual calibration including whole ECU and a complete vehicle model running on a
cloud environment to support validation and optimization of RDE calibration