optimizing gaseous and particle emissions of a gdi engine ... · model of a t-gdi engine predicting...

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


Challenges of todays Powertrain Calibration

2

Stricter emissions legislation (RDE) Hybridization/Electrification

Complexity of combustion engines Increasing Cost and Time Pressure

$


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


Model of a T-GDI Engine predicting the influence of all inputs on all relevant outputs

4

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


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


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


But: Steady State Model is not sufficient to optimize Dynamic Effects

10

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


Solution: Explicit modeling of Dynamic Behavior

11

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


Dynamic Modeling: Transient DoE

12

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


Dynamic Modeling: Transient DoE

13

Resulting Traces for all 8 Inputs considering various constraints


Dynamic Modeling: Modeling Process

14

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


Dynamic Modeling: Validation of Modeling Results

16

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


Dynamic Modeling: Validation of Modeling Results

17

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 ?


Optimization requires Coupling of ECU Strategy with Dynamic Model

18

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


Tool Framework MOCA for the Optimization of Parameter in ECU Models

19

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


Joint Optimization: ECU Injection Strategy with Dynamic Model on Real Cycles

20

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



21

Workflow in MOCA: 2nd step

Import the ASCMO-

DYNAMIC model

Connect/match channels



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



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



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



25

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)



26

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


Summary

27

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

optimizing gaseous and particle emissions of a gdi engine ... · model of a t-gdi engine predicting...

Documents