Model -Based Powertrain and Engine Control · Model -Based Powertrain and Engine Control Guoming...

ModelModel--Based Powertrain and Engine ControlBased Powertrain and Engine ControlGuoming (George) ZhuGuoming (George) Zhu

Mechanical Engineering, Electrical and Computer EngineeringMechanical Engineering, Electrical and Computer Engineering

Michigan State UniversityMichigan State University

(7(7--44--2013)2013)

07/04/2013 Zhu

� Background

� Model-based control frame work

� Modeling

� Crank-resolved engine/powertrain model

� Control oriented charge-mixing model

� Control Applications

� Optimal control: HCCI model transition control

� LPV (Gain-scheduling) control of cam phaser

� Adaptive control of LNT regeneration

� Conclusions

OutlineOutline

07/04/2013 Zhu

BackgroundBackground

VGT/Super charger Cam phasing

(camless)

Wastegate

ETC

PlantPID

Traditional SISO Control

MIMO Control System

PlantMBC

Engine system has the following characteristics:

� Highly nonlinear

� Parameter (time) varying

� System parameters changes as engine aging

� Time and event based control

� Complicated flow and thermal dynamics

� Robustness (fuel, altitude, etc.)

Model-Based Control (MBC) is

a necessity

PFI/DI

ignition

Oxygen/Nox sensors

Pressure/temperature

sensors

Cylinder pressure

and combustionsensors

07/04/2013 Zhu

ModelModel--Based Control FrameworkBased Control Framework

Dynamic

System

Disturbance

System Outputs

Disturbance

Estimation

and/or

Model Adaptation

System

Model

Model-Based

Controller or

Optimizer

Reference Signal

Updated Model and Disturbance

MeasurementsControl

Generic form of the model-based control

Two key components of model-based control:

�control oriented model capable of real-time simulation, and

�model based controller and optimizer

07/04/2013 Zhu

ModelModel--Based Control Based Control (development roadmap)(development roadmap)

Model-based

control strategy

Crank-Based

Combustion

Model�EGR

Πlift

θINTM

θEXTM

θST

Scheduled

Open-loop

Control

PI Control

(Feedback)

ILC Control

LQ Control

Memory

+

+

IETC

FDIλ(i+1) IMEP(i+1)

IMEP(i)

λ(i)

FFF(i)

FILC

FFB

MAP

�TPS

Time-Based

Air Handling

Model

FFF(i+1)

HIL Engine

SimulatorPrototype Engine Controller

IMEP

Control-oriented engine models

developed with GT-Power

simulations

HIL simulation to

validate the

control strategy

Single cylinder optical engine

Multi-cylinder metal engine

dSPACE based

real-time

simulator

Harness

break-out box

Opal-RT

based engine

prototype

controller

dSPACE host computer

Opal-RT host computer

Simulated signal display oscilloscope

��

07/04/2013 Zhu

ControlControl--Oriented ModelingOriented Modeling

Dynamic System

Fundamental(first principle

model)

Data-Driven(empirical model)

Nonlinear Diff. Equations.

(ODEs & PDEs)

Reduced Order Models

Discrete-Time State Space

Models

Continuous-Time State

Space Models

Frequency Domain

Transfer Funs

Linearized Models

Linearized Empirical Models

(e.g., ANN)

Linearization

07/04/2013 Zhu

CrankCrank--Resolved Engine ModelResolved Engine Model

� Crank-based in-cylinder pressure and temperature;

� Simple in-cylinder charge mixing model;

� Event-based fuel, ignition, and torque calculation with mean manifold dynamics.

� Real-time simulation up to 5000RPM

07/04/2013 Zhu

PhysicsPhysics--based modeling: based modeling: charge mixing (1)charge mixing (1)

BackgroundBackground

140° after exhaust TDC 180° after exhaust TDC 120° before comb TDC 60° before comb TDC

� HCCI combustion assumes Homogeneous Charge before compression

ignition, while in practice it is Heterogeneous, especially with high EGR.

� One-zone control-oriented HCCI combustion model, developed earlier,

assumes that the thermodynamic characteristics is uniformly distributed in the

cylinder, leading large prediction error of the start of combustion (SOC).

� To accurately predict the SOC, it is proposed to use a two-zone HCCI

combustion model for predicting SOC, which involves two-zone charge mixing

and HCCI modeling.

* M. Shen, “Simulation of in-cylinder flow and composition distribution of a gasoline HCCI engine with variable valve actuation,” MS Thesis, Tianjin University, July, 2006.

07/04/2013 Zhu

Charge mixing modeling - three phases:

� Backflow phase: when in-cylinder pressure

is higher than manifold pressure;

� Backflow returning: when in-cylinder

pressure is lower than manifold pressure;

� Fresh charge phase.

Diffusion

• Molecular diffusion

• Laminar diffusion

• Turbulent

diffusion

Mass transfer between fresh charge

and residual is assumed to be mainly

due to

350 400 450 500 550 600 650 700-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Crank position (deg)

Mass

(g)

Mixed-zone mass

Unmixed-zone mass

Total trapped mass

SOCIVCIVO

stage 1 stage 2 stage 3


* S. Zhang, G. Zhu, and Z. Sun, “A control-oriented charge mixing and two-zone HCCI combustion model," Submitted to IEEE Transactions on Vehicular Technology (Feb., 2013).

07/04/2013 Zhu

Model

FUE

L

(MG)

SOC IMEP

GT-Power 13.2 2 4.2

Two-zone model 13.2 2 4.22

One-zone model (w/ flow dynamics) 13.2 4 4.23

One-zone model (w/o flow dynamics) 13.2 8 4.36

Pre

ssu

reT

em

pera

ture

-100 0 100 200 300 400 5000

5

10

15

20

25

30

35

40


In-c

ylin

der

pre

ssure

(bar)

GT-Power modelTwo-zone model

-100 0 100 200 300 400 5000

500

1000

1500

2000


In-c

ylin

der

tem

pera

ture

(K

)

GT-Power modelTwo-zone model

200 250 300 350 400 450 5000

2

4

6

8

10


In-c

ylin

der

pre

ssure

(bar) GT-Power model

Two-zone model

One-zone model

200 250 300 350 400 450 500

400

600

800

1000

1200

1400


In-c

ylin

der

tem

pera

ture

(K

) GT-Power model

Two-zone model

One-zone model

In-Cylinder Mass Flow Rate (GT-Power and Two-Zone Models)

100 150 200 250 300 350 400 450 500-0.05

0

0.05

0.1

-0.05

0

0.05

0.1

-0.05

0


Ma

ss fl

ow

rate

(kg/s

)

GT-Power model

Two-zone model


07/04/2013 Zhu

Hybrid powertrain Hybrid powertrain –– HIL simulationsHIL simulations

Hybrid powertrain supervisory

control for the best fuel economy

using real-time equivalent fuel

consumption optimization

Real-time hybrid powertrain modeling for the

hardware-in-the-loop (HIL) simulation applications

CAN

Mototron based

supervisory controller

Opal-RT based

real-time simulator

TruckSim + Driver Model

gal

total fuel used (gal)

drive cycle

Vehicle

Speed

Power bus MPG

Gal

Engine_on

key_on

time

Fuel converter

Gal

Distance

Energy storage

EMB

EMA

Driver

Divide

Controller

1

Constant1

1

Constant

ClockAHS1

TruckSim + Driver Model

07/04/2013 Zhu

Optimal Control: Optimal Control: HCCI Mode TransitionHCCI Mode Transition

Two cycle throttle pre-opening

to prepare for valve lift switch

from high to low

LQ Optimal throttle control

to maintain desired AFR

Valve lift switch

from high to low

Hybrid combustion to match

increased recompression due

to electric cam phasing

Hybrid combustion that starts with SI

and ends with HCCI combustion

DI fuel control to ensure smooth mode transition

-80 -60 -40 -20 0 20 40 60 80

0

0.2

0.4

0.6

0.8

1

Crank angle (deg)

Mass Fraction Burned

ST

SOHCCI

-80 -60 -40 -20 0 20 40 60 80

0

0.2

0.4

0.6

0.8

1

Crank angle (deg)


ST

SOHCCI

-80 -60 -40 -20 0 20 40 60 80

0

0.2

0.4

0.6

0.8

1

Crank angle (deg)


SOHCCI

* X. Yang and G. Zhu, “SI and HCCI combustion mode transition control of a multi-cylinder HCCI capable SI engine,” IEEE Transaction on Control System Technology (Accepted in May, 2012, DOI: 10.1109/TCST.2012.2201719)

07/04/2013 Zhu

LPV (GainLPV (Gain--Scheduling) Control Scheduling) Control

� Traditionally, the PID control gains are tuned by calibration engineers in test cell

or field.

� Control design based upon a linear system model whose parameters are a

function of measurable parameters; and the resulting controller parameters are

also a function of these measurable parameters.

� Closed loop system stability and performance are guaranteed

* A. White, Z. Ren, G. Zhu, and J. Choi, “Mixed H∞

and H2 LPV control of an IC engine hydraulic cam phase system,” IEEE Transaction on Control System Technology, Vol. 21, Issue. 1, 2013, pp. 229-238 (DOI 10.1109/TCST.2011.2177464)..

VVT example VVT example ( 1) ( ) ( ) ( ) ( )

( ) ( ) ( )

c c c

c

x k A x k L z k

u k K x k

θ θ

θ

+ = +

=

07/04/2013 Zhu

Adaptive Control: Adaptive Control: AFR during LNT regenerationAFR during LNT regeneration

� Biofuel content is estimated online with guaranteed convergence

� Optimal air-to-fuel ratio (AFR) tracking control as a function of biofuel content

� Guaranteed closed loop system stability under any fuel content

• X. Chen, Y. Wang, I. Haskara, and G. Zhu, “Optimal air-to-fuel ratio tracking control with adaptive biofuel content estimation for the LNT regeneration,” IEEE Transaction on Control System Technology (Accepted in March, 2013, DOI: 10.1109/TCST.2013.2252350).

EngineExhaust

Manifold

Oxygen

Sensor

fuelm Φˆ

ˆair

DS

m

ασ

DS

airm

ασ

Plant

3x2

x1xu

System Model0.6

0.8

1

1.2

Fu

el G

ain

100 200 300 400 500 600 700 800

1

1.2

1.4

λ

350 400 450 500

1

1.2

1.4

Time(s)

λ

Fuel Gain

Reference λ

Measured λ

Reference λ

Measured λ

Plant

Adaptive Fuel

Gain Estimation

LQ

Contoller

State

Estimation

r

x̂

3x

α̂

Estimate

Fuel Mass

fuelmu

07/04/2013 Zhu

� Model-based powertrain/engine control becomes a necessity due to the

significant increment of number of sensors/actuator and high system

nonlinearity.

� Control-oriented powertrain and engine modeling is moving towards

first-principle based with reduced complexity (e.g., engine charge-

mixing model)

� Powertrain and engine models used for the HIL (hardware-in-the-loop)

simulations will be capable of simulating the physical systems at

different detail level. The improved computing technology enables more

and more first-principle based simulations.

� Model-based control, such as adaptive control, model predictive control,

linear parameter varying (gain-scheduling) control, will be the future

powertrain and control technologies

ConclusionsConclusions

07/04/2013 Zhu

� Closed loop combustion control of internal combustion engines (SI,

HCCI, and CI)

� Adaptive and model reference control of hydraulic and electric valve

actuation

� Closed loop system identification and control of automotive systems

� Hybrid powertrain system control and optimization

� Automotive system modeling for hardware-in-the-loop (HIL) simulations

� Combustion control and optimization for ethanol engines

� Variable displacement engines

� Ionization based combustion diagnostics and control

� TEG (thermo-electric generator) system management

� Application of the smart material to automotive systems

� LPV (linear parameter varying) optimal control with hard constraints

Other Research ActivitiesOther Research Activities

Model -Based Powertrain and Engine Control · Model -Based Powertrain and Engine Control Guoming...

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