¶ Gerry Clark – Technical Specialist GM Powertrain

¶ Chao Daniels – Technical Specialist GM Powertrain

Combustion Modeling using Neural Networks Trained to In-Cylinder Physically Predicted Quantities

Outline ¶ Indirect Engine Model Support ¶ Combustion Model Scope and Concept ¶ Process ¶ Application ¶ Next Steps

Indirect Support and Model Sharing: --Is GT Power model required? ¶ Would a look up table work better and simpler?

¶ What systems require representation?

¶ Is a pressure based varying torque signal needed?

¶ Are time sensitive control interactions needed? – Spark/Injection (this cylinder event) – Cam Phase (next cylinder event/1 cycle) – Throttle (1-3 cycles) – Turbo (3 or more cycles) – EGR (>10 cycles)

Indirect Support and Model Sharing Cylinder Pressure Datasets for other specialties

Encrypted Models (For Suppliers)

Multi Dimensional DOE’s for Calibration

Fast Running Models For HIL and SIL

Detailed Engine Model for GT Suite system models

Detailed or FRM For Control Algorithm Development

GT Power Model

Boundary Conditions for 3D Projects

Models for 3D coupling

Data Files = .trn .xls .txt

Model Files = .gtm

Data Files

Model Files

Gas Side Boundary for Thermal/Structural

Response Data For Vehicle Simulations

Models for Driveline and Vehicle Modeling

Need models with spark as input!

¶ Spark responsive models required. – Vehicle and Driveline – SIL and HIL – Controls development – Etc

¶ Leverage existing models with low effort. ¶ Robust combustion models are key to efficient indirect

support. – Spark Driven – Not just a knock model.

Model Robustness –Where does the model need to work?

¶ Vehicle and Driveline – Spark responsive and representative from best torque to zero

torque.

¶ Controls development – Cam position representative over the full phasing range. (at any

RPM and load) – EGR system responsive from zero external EGR to misfire.

¶ Basically everywhere – like an engine on the dyno!

Combustion Model Scope:

-0.2

0

0.2

0.4

0.6

0.8

1

-40 -30 -20 -10 0 10 20 30 40 50 60

Nor

malized

TQ

<<Retard Spark >>Advance

Torque vs Spark

Combustion Model Scope

10

15

15

15

20

20

20

25

25

25

30

3035

3540

45

ICAM

ECAM

Residual Percentage vs Cam Position

-35 -30 -25 -20 -15 -10 -5 00

5

10

15

20

25

¶ Use dyno DOE data to train combustion parameter Neural Network (Not a new concept)

¶ Innovation: Run GT Power @ DOE conditions and generate physics based combustion model inputs instead of operating condition based.

Combustion Model Concept

What do we mean by physics based

Operating Condition based combustion model

inputs (engine controller parameters):

¶ ICAM (Intake cam timing)

¶ ECAM (Exh cam Timing)

¶ TPS (Throttle Position)

¶ MAP (Manifold Pressure)

¶ (Spark)

Physics based combustion model inputs

(in-cylinder quantities):

¶ RPM

¶ VE

¶ Residual

¶ T @ 55

¶ P @ 55

¶ Spark

Combustion Parameter

Outputs:

-CA50

-Burn Duration

-COV

Temperature and Pressure at 55 degrees before TDC Firing

Operating Condition based NNW Combustion Model

Engine DOE with combustion data and

control data

Train NNW to predict combustion

parameters from control inputs

Implement Trained Combustion NNW into GT

Power model

Prediction OK

Prediction NOK

Model robust for one

engine specification.

Physics Based NNW Combustion Model Engine DOE with combustion data and

control data

Train NNW to predict combustion

parameters from Physical parameter

inputs

Implement Trained Combustion NNW into GT

Power model

Prediction OK

Prediction NOK

Model robust for any

similar engine.

Run GT Power model at DOE points using

control and combustion parameters. Generate

Physical parameters

Using the Operating Condition based NNW Combustion Model

GT Power Engine Model GT Power dynamic

outputs

Torque

FFR

Trained Combustion NNW

Control Params:

RPM

Spark

Throttle

ICAM

ECAM

EGR valve

CA50 Burn Duration

COV of IMEP

Using the Physics based NNW Combustion Model

GT Power Engine Model GT Power dynamic

outputs

Torque

FFR

Physical Parameter

Trained Combustion NNW

Control Params:

RPM

Spark

Throttle

ICAM

ECAM

EGR valve

CA50 Burn Duration

COV of IMEP

Physical Params: RPM

VE

Residual

T @ 55

P @ 55

Spark

Predicted Torque vs Spark Results

-20

0

20

40

60

80

100

120

-40 -30 -20 -10 0 10 20 30 40 50

TQ (N

M)

<<Retard Spark Advance>>

TQ vs Spark at varying cylinder air load

Physical Parameter Development ¶ Initial List: RPM VE P@55 T@55 Resid Spark MdotV* Lambda* SOI*

¶ Current List: RPM VE P@55 T@55 Resid Spark

¶ Study List: RPM VE SL TKE

*Model still comprehends the non combustion effects of these parameters

Applied successfully to controls development models for future technology combinations, both regular GT Power and FRM’s Applied successfully to development models for SIL and HIL process. Driveline applications under development

Application Summary

¶ Optimize DOE data required for robust model.

¶ Automate limit functions for misfire etc. ¶ Continue parameter development allowing

application to more diverse configurations ¶ Develop fuel swap transfer functions

(E10>E85)

Next Steps

Acknowledgements:

Dave White, Bhaskar Dongare for models and DOE runs.

Gary Cygan for input parameter study and supporting broader application of this technique.

Live Demo