Mean Value Model Y.He 2006-11-14 - gtisoft.com · IAT (K) EGR cooler out temp (K) MAF (kg/s) MAP...

$: Mean Value Model Y.He 2006-11-14 - gtisoft.com · IAT (K) EGR cooler out temp (K) MAF (kg/s) MAP (bar) Pexh (bar) Exh tmp (K) Intake O2 frac Exh O2 frac Turbo spd (rpm) Trq (Nm) BMEP$
1Mean Value Engine Model for Integrated Engine & Control System Simulation

10th GT-Suite Users Conference, Dearborn, MIY. He11/14/06

Development and Validation of a Mean Development and Validation of a Mean Value Engine Model for Integrated Value Engine Model for Integrated

Engine and Control System Simulation Engine and Control System Simulation

Yongsheng He and Chan-Chiao LinGeneral Motors Corporation

10th GT-Suite Users Conference

November 14, 2006



OutlineOutlineIntroductionIntroduction

Integrated Engine & Control System SimulationIntegrated Engine & Control System SimulationDetailed 1D engine modelMean value engine model

Results and DiscussionResults and DiscussionStep changeStep transientsFTP cycle

SummarySummary



IntroductionIntroductionMathMath--based control development in automotive industrybased control development in automotive industry

Much of control design and development process could be done off-line using computer simulationsDramatically reduce development time and risk

Integrated engine and control system model valuable Integrated engine and control system model valuable Accurately evaluate control algorithmsExplore different control strategies & study parameter sensitivity

Before experiments conductedBefore hardware selected and built



Integrated Engine & Control System SimulationIntegrated Engine & Control System Simulation

fuel temp (C)

coolant temp (C)

mph

Intake_oxy_frac_out

Exh_tmp_K_out

Pexh_bar_out

MAP_eng_out

MAF_eng_out

fqc_q_desired_out

pos_itv_target_out

power_eng_out

BMEP_eng_out

trq_eng_out

turbo_speed_out

Exh_oxy_frac_out

pos_vnt_target_out

pos_egr_target_out

PPS (%)

Intake tmp (K)

rpm

EGR cooler tmp (K)

EGR lift (%open)

VNT pos (%close)

fuel (mm^3/st)

eng spd (rpm)

baro (bar)

IAT (K)

EGR cooler out temp (K)

MAF (kg/s)

MAP (bar)

Pexh (bar)

Exh tmp (K)

Intake O2 frac

Exh O2 frac

Turbo spd (rpm)

Trq (Nm)

BMEP (bar)

Power (kW)

Diesel Engine

MAF (kg/s)

MAP (bar)

PPS (%)

rpm_eng

mph_veh

Baro (bar)

fuel_temp (C)

tmp_air_intake (K)

coolant_temp (C)

egr_target_lift (%)

vnt_position (%)

itv_position (%)

fuel (mm^3/st)

Controller (ECM)

Baro (bar)

(SAE Paper 2006-01-0439)



FTP Cycle: FTP Cycle: Simulation Simulation ResultsResults

BlowBlow--up of the up of the FTP results to FTP results to compare compare simulations and simulations and experiments experiments (200(200--300 s)300 s)

200 210 220 230 240 250 260 270 280 290 3000

0.05

0.1

MA

F (k

g/s)

200 210 220 230 240 250 260 270 280 290 300

1

1.2

1.4

MA

P(b

ar)

200 210 220 230 240 250 260 270 280 290 300

1

1.5

2

2.5

Pex

h (bar

)

200 210 220 230 240 250 260 270 280 290 300400

600

800

1000

T exh (K

)

200 210 220 230 240 250 260 270 280 290 3000

0.1

0.2

0.3In

t O2

200 210 220 230 240 250 260 270 280 290 3000

0.1

0.2

0.3

Exh

O2

200 210 220 230 240 250 260 270 280 290 3000

2

4

6

8x 104

Turb

o S

peed

(rpm

)

Time (sec)

(a)

(b)

(c)

(d)

(e)

(f)

(g)

SimulationExperiment

Simulated Thermocouple Temp

ExperimentSimulation

(SAE Paper 2006-01-0439)



Model Accuracy Model Accuracy vsvs Model SpeedModel SpeedFastFast--running engine model with sufficient accuracyrunning engine model with sufficient accuracy

Efficient evaluation of control algorithms and control strategiesExploration of the classical trade-off in the modeling process

Detailed 1D engine modelDetailed 1D engine modelPredict gas dynamics and engine performance within 3-5%Run speed on the order of 100~1000 times slower than real time

Mean value engine modelMean value engine modelCapture dynamics over one or more engine cyclesRun speed close to or faster than real time

Model AccuracyModel Accuracy Model SpeedModel Speed



Mean Value Engine Modeling Mean Value Engine Modeling -- ApproachApproachReduce the complexity of a detailed 1D engine model to a Reduce the complexity of a detailed 1D engine model to a mean value modelmean value model

Cylinder processes simplified using empirical correlations Flow components lumped together to use a larger time step

For GTFor GT--Power, model reduction process proposed by GTI Power, model reduction process proposed by GTI (SAE 2005(SAE 2005--0101--0072, GT0072, GT--Suite User Manual v6.1)Suite User Manual v6.1)

Identify input variables and design simulations using DOEConduct simulations using the detailed model, and train neural networks (NN) with simulation results Build a mean value cylinder model using trained neural networks,and replace the original cylinder model in the detailed model



Mean Value Engine Modeling Mean Value Engine Modeling -- ImprovementsImprovements

Improvements are applied based on the application Improvements are applied based on the application and practice in this study:and practice in this study:

Constrained Latin Hypercube used for DOEConsider the physical constraints of engine operations

Radial Basis Functions (RBF) used in addition to NNImprove the accuracy to approximate simulation results

Intake and exhaust system completely simplifiedIn addition to building the mean value cylinder model



Input Variables and DOEInput Variables and DOETurbocharged Turbocharged V6 diesel V6 diesel engine with engine with external EGRexternal EGR

Focus on the Focus on the control of control of fueling, EGR, fueling, EGR, and VNT and VNT

500 1000 1500 2000 2500 30000

20

40

60

Fuel

(mg/

cycl

e)

500 1000 1500 2000 2500 30000

0.2

0.4

0.6

0.8

1

Engine Speed (rpm)

EG

R L

ift F

ract

ion

500 1000 1500 2000 2500 30001

1.1

1.2

1.3

1.4

Boo

st P

ress

ure

(bar

)500 1000 1500 2000 2500 30001

1.5

2

2.5

Engine Speed (rpm)B

ack

Pre

ssur

e (b

ar)

[1 2.4]Back Pressure (bar)[1 1.4]Boost Pressure (bar)[0 1]EGR Valve Lift Fraction

[0 55]Total Fueling (mg/cycle) [530 3000]Engine Speed (rpm)



Neural Networks Neural Networks vsvs Radial Basis FunctionRadial Basis FunctionNeural NetworksNeural Networks

Self-organizing Local Linear NN (Automatic tool in GT-Power)

Radial Basis FunctionsRadial Basis FunctionsHybrid RBF (Model-Based Calibration Toolbox, MATLAB)

Improvement on indicated efficiency approximationImprovement on indicated efficiency approximation

2R

0.9790.979Exhaust Energy Fraction

0.9670.937Indicated Efficiency

0.9990.999Volumetric Efficiency

Hybrid RBFNeural Networks



Mean Value Engine Modeling Mean Value Engine Modeling –– Final ModelFinal Model



OutlineOutlineIntroductionIntroduction

Integrated Engine & Control System SimulationIntegrated Engine & Control System SimulationDetailed 1D engine modelMean value engine model

Results and DiscussionResults and DiscussionStep changeStep transientsFTP cycle

SummarySummary



StepStep--Change Test: Simulation Results (1/3)Change Test: Simulation Results (1/3)

0 5 10 15 201500

2000

2500

3000

Eng

ine

Spe

ed (r

pm) (a)

0 5 10 15 200

20

40

60

80

Ped

al (%

)

(b)

0 5 10 15 200

20

40

60

80

100

120

EG

R L

ift (%

)

Time (sec)

(c)

0 5 10 15 200

20

40

60

80

Fuel

(mm

3 /st)

(d)

0 5 10 15 2020

40

60

80

100

120

VN

T P

ositi

on (%

) (e)

0 5 10 15 200

0.05

0.1

0.15

MA

F (k

g/s)

Time (sec)

(f)

Mean Value (RBF)Mean Value (NN)Detailed Model




0 5 10 15 200.8

1

1.2

1.4

1.6

Inta

ke P

ress

ure

(bar

)

(a)

0 5 10 15 200.8

1

1.2

1.4

1.6

1.8

Exh

aust

Pre

ssur

e (b

ar)

(b)

0 5 10 15 20

200

400

600

800

1000

Exh

aust

Tem

pera

ture

(K)

Time (sec)

(c)

0 5 10 15 200.05

0.1

0.15

0.2

0.25

0.3

Inta

ke O

xyge

n Fr

actio

n

(d)

0 5 10 15 20-0.1

0

0.1

0.2

0.3

Exh

aust

Oxy

gen

Frac

tion

(e)

0 5 10 15 200

2

4

6

x 104

Turb

ine

Spe

ed (r

pm)

Time (sec)

(f)





0 5 10 15 20-50

0

50

100

150

Eng

ine

Pow

er (k

W)

(a)

0 5 10 15 20

0

200

400

600

Eng

ine

Torq

ue (N

m)

(b)

0 5 10 15 200

20

40

60

80

100

EG

R R

ate

(%)

Time (sec)

(c)

0 5 10 15 200

50

100

Vol

umet

ric E

ffici

ency

(%)

(d)

0 5 10 15 200

20

40

60

80

100

Indi

cate

d E

ffici

ency

(%)

(e)

0 5 10 15 200

20

40

60

80

100

Exh

aust

Fra

ctio

n (%

)

Time (sec)

(f)




Model Validation: Vehicle TestingModel Validation: Vehicle TestingSeries of different cruising and acceleration conditionsSeries of different cruising and acceleration conditions

Selected for validation: 3 step transients (ST)

0 100 200 300 400 500 600 700 800 900 1000 11000

20

40

60

80

Ped

al (%

)

0 100 200 300 400 500 600 700 800 900 1000 1100500

1000

1500

2000

2500

3000

Eng

ine

spee

d (rp

m)

0 100 200 300 400 500 600 700 800 900 1000 11000

20

40

60

80

Veh

icle

spe

ed (m

ph)

Time (sec)

(a)

(b)

(c)

PPS

RPM

MPH

ST3 ST1 ST2



Step Transient: Simulation Results (1/3)Step Transient: Simulation Results (1/3)

0 5 10 15 201500

2000

2500

3000

Eng

ine

Spe

ed (r

pm) (a)

0 5 10 15 200

20

40

60

80

Ped

al (%

)

(b)

0 5 10 15 200

20

40

60

80

100

120

EG

R L

ift (%

)

Time (sec)

(c)

0 5 10 15 200

20

40

60

80

Fuel

(mm

3 /st)

(d)

0 5 10 15 2020

40

60

80

100

120

VN

T P

ositi

on (%

) (e)

0 5 10 15 200

0.05

0.1

0.15

MA

F (k

g/s)

Time (sec)

(f)

Mean Value (RBF)Detailed Model

ST2




0 5 10 15 200.8

1

1.2

1.4

Inta

ke P

ress

ure

(bar

)

(a)

0 5 10 15 20

1

1.5

2

Exh

aust

Pre

ssur

e (b

ar)

(b)

0 5 10 15 20

200

400

600

800

1000

Exh

aust

Tem

pera

ture

(K)

Time (sec)

(c)

0 5 10 15 200.05

0.1

0.15

0.2

0.25

0.3

Inta

ke O

xyge

n Fr

actio

n

(d)

0 5 10 15 20-0.1

0

0.1

0.2

0.3

Exh

aust

Oxy

gen

Frac

tion

(e)

0 5 10 15 200

2

4

6x 104

Turb

ine

Spe

ed (r

pm)

Time (sec)

(f)


ST2




0 5 10 15 20-50

0

50

100

150

Eng

ine

Pow

er (k

W)

(a)

0 5 10 15 20

-100

0

100

200

300

400

500

Eng

ine

Torq

ue (N

m)

(b)

0 5 10 15 200

20

40

60

80

100

EG

R R

ate

(%)

Time (sec)

(c)

0 5 10 15 200

20

40

60

80

100

120

Vol

umet

ric E

ffici

ency

(%)

(d)

0 5 10 15 200

20

40

60

80

100

Indi

cate

d E

ffici

ency

(%)

(e)

0 5 10 15 200

20

40

60

80

100

Exh

aust

Fra

ctio

n (%

)

Time (sec)

(f)


ST2



Model Validation: FTP CycleModel Validation: FTP Cycle

0 200 400 600 800 1000 1200 14000

20

40

60

80P

edal

(%)

0 200 400 600 800 1000 1200 14000

1000

2000

3000

Eng

ine

spee

d (rp

m)

0 200 400 600 800 1000 1200 14000

20

40

60

Veh

icle

spe

ed (m

ph)

Time (sec)

(a)

(b)

(c)

PPS

RPM

MPH



FTP Cycle: Simulation Results (1/2)FTP Cycle: Simulation Results (1/2)

0 200 400 600 800 1000 1200 14000

20

40

60

80

Fuel

(mm

3 /st) (a)

0 200 400 600 800 1000 1200 1400

60

80

100

VN

T P

ositi

on (%

)

(b)

0 200 400 600 800 1000 1200 14000

50

100

EG

R L

ift (%

)

Time (sec)

(c)




FTP Cycle: FTP Cycle: Simulation Simulation Results (2/2)Results (2/2)

0 200 400 600 800 1000 1200 14000

0.1

0.2

MA

F (k

g/s) (a)

0 200 400 600 800 1000 1200 14001

1.5

MA

P (b

ar) (b)

0 200 400 600 800 1000 1200 14001

1.5

2

Pex

h (b

ar)

(c)

0 200 400 600 800 1000 1200 14000

500

1000

Texh

(K)

(d)

0 200 400 600 800 1000 1200 14000.1

0.2

0.3In

t O2

(e)

0 200 400 600 800 1000 1200 1400

0

0.1

0.2

Exh

O2

(f)

0 200 400 600 800 1000 1200 14000

5

x 104

Turb

ine

Spe

ed (r

pm)

Time (sec)

(g)




FTP Cycle: Simulation Results BlowFTP Cycle: Simulation Results Blow--up (1/2)up (1/2)

200 210 220 230 240 250 260 270 280 290 3000

20

40

60

80

Fuel

(mm

3 /st)

200 210 220 230 240 250 260 270 280 290 300

60

80

100

VN

T P

ositi

on (%

)

200 210 220 230 240 250 260 270 280 290 3000

50

100

EG

R L

ift (%

)

Time (sec)


DetailedMean Value

BlowBlow--up of the FTP results for comparison (200up of the FTP results for comparison (200--300 s)300 s)



FTP Cycle: FTP Cycle: Simulation Simulation Results Results BlowBlow--up (2/2)up (2/2)

200 210 220 230 240 250 260 270 280 290 3000

0.1

0.2

MAF

(kg/

s)

200 210 220 230 240 250 260 270 280 290 3001

1.5

MAP (b

ar)

200 210 220 230 240 250 260 270 280 290 3001

1.5

2

Pex

h (b

ar)

200 210 220 230 240 250 260 270 280 290 3000

500

1000

Texh

(K)

200 210 220 230 240 250 260 270 280 290 3000.1

0.2

0.3In

t O2

200 210 220 230 240 250 260 270 280 290 300

0

0.1

0.2

Exh

O2

200 210 220 230 240 250 260 270 280 290 3000

5

x 104

Turb

ine

Spe

ed (r

pm)

Time (sec)


BlowBlow--up of the up of the FTP results for FTP results for comparison comparison (200(200--300 s)300 s)

DetailedMean Value



Model Accuracy Model Accuracy vsvs Model Speed (Summary)Model Speed (Summary)Mean value engine model developed in this studyMean value engine model developed in this study

Accuracy slightly compromised (cylinder quantities)About 40 times faster than the detailed model

0

20

40

60

80

100

Model Run Time (x Real Time)

Detailed 1D Model [15-16] Mean Value Model (RBF)

Model Error (%)



SummarySummaryA fastA fast--running mean value engine model with sufficient running mean value engine model with sufficient accuracy developed for control applications accuracy developed for control applications

Reduced from a detailed engine model in GT-PowerConstrained Latin Hypercube to consider physical constraints Hybrid RBF to approximate cylinder quantities for better accuracyCompletely simplified (cylinders, intake & exhaust system)

The developed mean value model integrated with a The developed mean value model integrated with a comprehensive controller model for control analysiscomprehensive controller model for control analysis

The integrated engine and control system model extensively validated with satisfactory accuracy achieved

1 Step change3 Step transients1 FTP cycle

Mean Value Model Y.He 2006-11-14 - gtisoft.com · IAT (K) EGR cooler out temp (K) MAF (kg/s) MAP...

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Transcript of Mean Value Model Y.He 2006-11-14 - gtisoft.com · IAT (K) EGR cooler out temp (K) MAF (kg/s) MAP...