Hibryd System Presentacion

16

HYSCOMIEEE CSS Technical Committee on Hybrid Systems

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fo senilpicsid gninibmoc yB .cigol lortnoc,yroeht lortnoc dna smetsys dna ecneics retupmoc

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.(srehto ynam dna ,seirtsudni

www.ist-hycon.orgwww.unisi.it

1 HYCON PhD School on Hybrid Systemsst

Siena, July 19-22, 2005 - Rectorate of the University of Siena

Hybrid Systems in Automotive Engine Control

Andrea BalluchiPARADES – Rome, Italy

[email protected]

Hybrid Systems in Hybrid Systems in Automotive Engine ControlAutomotive Engine Control

Andrea BalluchiAndrea Balluchi

in collaboration within collaboration withLuca Luca BenvenutiBenvenuti, Antonio , Antonio BicchiBicchi,, Claudio Lemma, Claudio Lemma, Emanuele MazziEmanuele Mazzi,,

Alberto L. SangiovanniAlberto L. Sangiovanni--VincentelliVincentelli,, Gabriele Gabriele SerraSerra

11stst HYCON PhD School on Hybrid Systems HYCON PhD School on Hybrid Systems Siena, 19Siena, 19--22 July 200522 July 2005

PARADESPARADES Università degli Studi di Pisa

1st HYCON PhD School on Hybrid Systems 1st HYCON PhD School on Hybrid Systems A. Balluchi A. Balluchi -- PARADES Siena, 19PARADES Siena, 19--22 July 2005 p.22 July 2005 p.2

OutlineOutline

Automotive: a promising domain for hybrid systemsAutomotive: a promising domain for hybrid systemsModelModel--based designbased designDerivative designDerivative designDesign flowDesign flow

Two automotive engine control applications of hybrid systemsTwo automotive engine control applications of hybrid systemsActual Engaged Gear Identification: a Hybrid Observer ApproachActual Engaged Gear Identification: a Hybrid Observer ApproachHybrid Modeling and Control of the Common Rail Hybrid Modeling and Control of the Common Rail


Automotive networked control systemAutomotive networked control system

Car manufacturers have to redesign their products more and more Car manufacturers have to redesign their products more and more frequently to meet customers’ demands on innovationfrequently to meet customers’ demands on innovationThe pressure of competitiveness is even higher for control systeThe pressure of competitiveness is even higher for control system m development, since more than 80% of innovation is in electronicsdevelopment, since more than 80% of innovation is in electronicsIn today cars, the electronic control system is a networked systIn today cars, the electronic control system is a networked systemem

with more than 80 interconnected with more than 80 interconnected ECUsECUs, some of them safety critical, some of them safety critical


Automotive control systems architectureAutomotive control systems architecture

Today, a “oneToday, a “one--subsystem onesubsystem one--ECU” networked control systemECU” networked control systemThis rigid partition between subsystems and electronicsThis rigid partition between subsystems and electronics

results in a higher cost of electronicsresults in a higher cost of electronicsprevents the design of efficiently integrated functionalitiesprevents the design of efficiently integrated functionalitiesit is often not efficient in terms of communication and synchronit is often not efficient in terms of communication and synchronizationization


Future scenario for automotive control systems Future scenario for automotive control systems

The new trend isThe new trend isBreak the “oneBreak the “one--subsystem onesubsystem one--ECU” paradigmECU” paradigmDistribute functionalities over several nodes to optimize numberDistribute functionalities over several nodes to optimize number and cost of and cost of ECUsECUs

AdvantagesAdvantagesflexibility, cost reduction, redundancy (faultflexibility, cost reduction, redundancy (fault--tolerance)tolerance)more sophisticated control enabled by more powerful hardwaremore sophisticated control enabled by more powerful hardware


ModelModel--based designbased design

ModelModel--based design is becoming widely based design is becoming widely used in automotive industryused in automotive industry

algorithms are designed and analyzed using algorithms are designed and analyzed using block diagramblock diagram--based modeling toolsbased modeling toolscorrectness of the algorithms is validated correctness of the algorithms is validated against models of the plantagainst models of the plantmodels form the basis for all subsequent models form the basis for all subsequent development stagesdevelopment stages

executable specification (instead of docs)executable specification (instead of docs)automatic code generation automatic code generation

AdvantagesAdvantagesTimeTime--saving and costsaving and cost--effectiveeffectiveDesign choices can be explored and Design choices can be explored and evaluated quickly and reliablyevaluated quickly and reliablyIdeally, an optimized and fully tested system Ideally, an optimized and fully tested system is obtainedis obtained


ModelModel--based designbased design

However, today in the automotive industry However, today in the automotive industry modelmodel--based design is often limited to control algorithm descriptionbased design is often limited to control algorithm descriptionnot complete plant modeling prevents accurate validation of algonot complete plant modeling prevents accurate validation of algorithmsrithms

Experimental validation is still extensively used, but Experimental validation is still extensively used, but very expensive, timevery expensive, time--consuming, bounded coverageconsuming, bounded coveragedue to the high cost, OEM will provide less support to experimendue to the high cost, OEM will provide less support to experimentation in Tiertation in Tier--1 companies1 companies

The partial implementation of modelThe partial implementation of model--based design is due tobased design is due toinsufficient investments in design process innovationinsufficient investments in design process innovationlack of methodologies and tools suitable to address critical stelack of methodologies and tools suitable to address critical steps in the design ps in the design flow, which are currently handled relying on the experience of tflow, which are currently handled relying on the experience of the designershe designers


Derivative designDerivative design

The derivative design approach:The derivative design approach:Every twoEvery two--three years a new generation of products is designedthree years a new generation of products is designed

Product generations are conceived to accommodate the specificatiProduct generations are conceived to accommodate the specification of all on of all customers for the next yearscustomers for the next years

For each commitment, the electronic control unit is obtained by For each commitment, the electronic control unit is obtained by derivation derivation from the current generationfrom the current generation

In the derivative design approach, reuse is extensively employedIn the derivative design approach, reuse is extensively employed to to minimize cost and development timeminimize cost and development time

for each class of applications, products are variants of a same for each class of applications, products are variants of a same originating originating designdesign

PPC

OEMTechnology

DB

ABSACCSteer C.

Brake C.

Control Algorithm

HC11

Supplier Technology

DB

SW


Automotive industry design processAutomotive industry design process

system system specificationspecification

functional functional deploymentdeployment

control control designdesign

hw/sw hw/sw designdesign

components components implementationimplementation

hw/sw hw/sw testingtesting

control control validationvalidation

functional functional integrationintegration

system system testingtesting

designdesign

integ

ration

& tes

ting

integ

ration

& tes

tinghybrid hybrid

systemssystems


Plant modeling Plant modeling -- model developmentmodel development

44--stroke internal combustion enginestroke internal combustion engine44--stroke engine cycle (FSM + DES + CT)stroke engine cycle (FSM + DES + CT)

in: in: spark ignitionspark ignition; ; injected fuelinjected fuel; ; air chargeair charge; ; EGR conc.EGR conc.;;engine speedengine speed;;out: out: engine torque, temperatureengine torque, temperature; ; dc eventsdc events; ; A/FA/F; ; exhaust gasexhaust gas;;

fuel injection (FSM + CT)fuel injection (FSM + CT)inputs: inputs: fuel injection signalfuel injection signal ((rail pressure regulator commandrail pressure regulator command-- DI);DI);outputs: outputs: injected fuelinjected fuel ((rail pressurerail pressure;;fuel temperaturefuel temperature -- DI);DI);

spark ignition (FSM)spark ignition (FSM)inputs: inputs: ignition coil commandignition coil command; ; spark commandspark command;;outputs: outputs: spark ignitionspark ignition;;

air dynamics (CT)air dynamics (CT)inputs: inputs: throttle valve commandthrottle valve command; ; EGR commandEGR command; ; VGT VGT commandcommand;;outputs: outputs: throttle valve anglethrottle valve angle; ; temperature; pressuretemperature; pressure; ; air flow air flow raterate; ; air chargeair charge; ; EGR concentrationEGR concentration;;

CVCV

DVDV

CTCTDTDT

Maserati Spider - V8


Hybrid model of a 4Hybrid model of a 4--stroke engine stroke engine

cont / contload torque load torque TTL L

disc / discclutch clutch clutchclutch

Time / ValueTime / ValueDisturbancesDisturbances

cont / contthrottle α

disc / discignition spark

Time / ValueTime / ValueControlsControls

T(t)

TL(t)

n(t)m(t)α (t)

spark

clutch

ThrottleValveAngle

POWER-TRAIN

INTAKEMANIFOLD

CYLINDERSMass of Air

SparkIgnition

EngineTorque

EngineSpeed

LoadTorque

ClutchEngagement

+-

intake manifold

powertrain (idle gear)


Single cylinder FSM: engine cycleSingle cylinder FSM: engine cycle

disc / contspark advance angle ϕ

disc / contgenerated torque T

disc / contmass of air m

Time / ValueTime / ValueStatesStates

dc

dcdc

dc

spk

spk

spk&dc

dcI

H

BS

ASPA

NA

C E

negative spark negative spark advanceadvance

positive spark positive spark advanceadvance

II →→ BSBSBSBS →→ PAPAPAPA →→ ASAS

NANA →→ ASAS

ASAS →→ HH

t

T (t)torque

piece-wise profilespark

k - 4 k - 3 k - 2 k - 1 k k+1

aH I BS PA AS H

fuel injection air intake


44--Cylinder Engine Hybrid AutomatonCylinder Engine Hybrid Automaton


Control synthesis Control synthesis -- algorithm developmentalgorithm development

Characteristics of the overall electronic control systemCharacteristics of the overall electronic control systemMultiMulti--rate control system composed of nested control loops that interarate control system composed of nested control loops that interact with ct with other embedded controllersother embedded controllers

frequency and phase drifts between sampling frequenciesfrequency and phase drifts between sampling frequenciesevent driven actionsevent driven actionsasynchronous communication on the networkasynchronous communication on the network

Implements both continuous and discrete functionalitiesImplements both continuous and discrete functionalitiesmore discrete than continuous more discrete than continuous control algorithms may have many operation modescontrol algorithms may have many operation modes

nominal operation modessafety, protection and recovery modes

computations performed at transition time are very importantcomputations performed at transition time are very importantswitching conditions controller initializations

A large part of algorithms devoted to diagnosis, fault toleranceA large part of algorithms devoted to diagnosis, fault tolerance and safetyand safetyComplexity: more than 150 I/O and 200 algorithms in engine contrComplexity: more than 150 I/O and 200 algorithms in engine control unitsol units


Architecture of cruise control algorithmsArchitecture of cruise control algorithms

Driver console and driveline systems interfaceDriver console and driveline systems interface

Cruise control supervisorCruise control supervisor

Vehicle velocity regulation feedbackVehicle velocity regulation feedback

VEHICLE

Cruise Control Commands

ECU Inputs: Brake, Clutch, Gear

Accelerator Pedal

VEHICLE VELOCITY REGULATION FEEDBACK

CRUISE CONTROL

SUPERVISOR

DRIVER CONSOLE AND

DRIVELINE SYSTEMS INTERFACE

TORQUE CONTROL

Commands

CC Torque Feedback

Driver/Cruise

Velocity Set Point

Cruise Control Mode

Display

VehicleVelocity

DRIVER

Traction Force

Engine Torque


Cruise control supervisorCruise control supervisorkey_on_endKEYON LOCK

OFF

ONAWAIT

REG_UP

lamp = on;vref = 0;

lamp = on;

lamp = on;vref = 0;

lamp = on;vref = 0;

lock_ immediately

lock_smoothly

cruise_on

cruise_off

cruise_on &NOT cruise_release &NOT cruise_stop

cruise_on &NOT cruise_release &NOT cruise_stop cruise_off

( set_plus l set_minus )/ vref = vehicle_speed;

vehicle_speed >> vref

VCONTROL TRACKING

set_plus /vref = vref + v

set_minus /vref = vref - v

vehicle_speed << vref

vehicle_speed >= vref vehicle_speed <= vref REG_DOWN

ENABLEDlamp = on;

stand_by

OVERRUNSTANDBY

aux_sys_req

resume &NOT stand_by &NOT aux_sys_req

( set_plus l set_minus ) &NOT stand_by &NOT aux_sys_req/ vref = vehicle_speed;

( set_plus l set_minus ) &NOT aux_sys_req/ vref = vehicle_speed;

driver_req laux_sys_req

NOT driver_req &NOT aux_sys_req

REG_UP

disenable_smoothly

disenable_immediately

Actual Engaged Gear Identification:Actual Engaged Gear Identification:a Hybrid Observer Approacha Hybrid Observer Approach

Andrea BalluchiAndrea Balluchi((1,21,2)), , LucaLuca BenvenutiBenvenuti((1,31,3)),, Claudio LemmaClaudio Lemma((11) )

Alberto L. SangiovanniAlberto L. Sangiovanni--VincentelliVincentelli((1,41,4) ) ,, Gabriele Gabriele SerraSerra((55) )

(1)(1) PARADES, Rome, IPARADES, Rome, I(2)(2) Centro Centro InterdipInterdip. “E.. “E. PiaggioPiaggio”, University of Pisa, Pisa, I”, University of Pisa, Pisa, I

(3)(3) Dip.Dip. di Informaticadi Informatica ee SistemisticaSistemistica, University of Roma “La, University of Roma “La SapienzaSapienza”, Rome, I”, Rome, I(4)(4) Dept. of EECS, University of California at Berkeley, CA Dept. of EECS, University of California at Berkeley, CA

(5)(5) MagnetiMagneti MarelliMarelli PowertrainPowertrain, Bologna, I, Bologna, I

1616thth IFAC World CongressIFAC World CongressPrague, July 4Prague, July 4--8, 20058, 2005 CC “Control and Computation”CC “Control and Computation”

Applications Paper Prize


MotivationMotivation

Actual engaged gear identification is relevant to engine controlActual engaged gear identification is relevant to engine control for for cars equipped with manual gearcars equipped with manual gearThe gear and clutch states are used inThe gear and clutch states are used in

Engine torque controlEngine torque controlto improve drivability by compensating the equivalent inertia ofto improve drivability by compensating the equivalent inertia of the vehicle on the the vehicle on the crankshaftcrankshaftto prevent engine stall by acting promptly when the transmissionto prevent engine stall by acting promptly when the transmission is openedis opened

Tailpipe emissions controlTailpipe emissions controlparticulate emissions for Diesel engines are particularly criticparticulate emissions for Diesel engines are particularly critical to control with first al to control with first gear engagedgear engaged


OutlineOutline

Automotive driveline modelingAutomotive driveline modelingDetailed hybrid model used for analysis and validationDetailed hybrid model used for analysis and validation

discontinuities and nonlinear dynamicsdiscontinuities and nonlinear dynamics

Simplified hybrid model used for synthesis Simplified hybrid model used for synthesis obtained by abstraction and reductionobtained by abstraction and reduction

Hybrid design of the actual engaged gear identification algorithHybrid design of the actual engaged gear identification algorithmm

Validation Validation Against the detailed hybrid nonlinear modelAgainst the detailed hybrid nonlinear model

robustness analysisrobustness analysis

Using experimental data provided byUsing experimental data provided by Magneti Marelli PowertrainMagneti Marelli Powertrain


Automotive drivelineAutomotive driveline1) engine suspension

2) cylinder block

3) crankshaft

4) connecting rod

5) piston

6) clutch

7) gear

8) differential

9) semi-axle

10) flexible joint

11) gear box

12) hub

13) body suspension

14) wheel

15) tire


Driveline detailed hybrid modelDriveline detailed hybrid model

6048 discrete states12 continuous states


Validation of the model with experimental dataValidation of the model with experimental data

proposed modelexperimental data

engine clutch

shunt

shuffle


Driveline simplified hybrid modelDriveline simplified hybrid model7 discrete states- gear and clutch

4 continuous statesωe - crankshaft speedωc - clutch speedωw - wheel speedα - torsion angle

1 discrete input- gear lever ∈{1, 2,..5, RG, N }

3 continuous inputsTe - engine torque (m.v. known)Pc - clutch plate pressureTw - wheel torque

2 continuous outputsωe - crankshaft speedωw - wheel speed

with gear engaged and clutch closed:

crankshaft clutch plates

gear

road

ωeωc

ωwTe

Pc

Tw

α

vehicleinertia


Abstraction of the discrete behaviorAbstraction of the discrete behavior

clutch closedand

1st gear

OPENSLIPPINGCLOSED

idle gear or backlash or clutch open or clutch slipping


Wheel speed and engine speed comparisonWheel speed and engine speed comparison

LimitationsLimitationsLarge time delays in the engaged gear identification Large time delays in the engaged gear identification

due to oscillations of the transmission shafts during transientsdue to oscillations of the transmission shafts during transientsparticularly critical for idle speed and first gear identificatiparticularly critical for idle speed and first gear identificationon

Frequent identification errorsFrequent identification errors

SpecificationSpecificationIdentification within a delay of 250 Identification within a delay of 250 msecmsec., sampling period of 12 ., sampling period of 12 msecmsec..

crankshaft clutch plates

gear

road

ωeωc

ωwTe

Pc

Tw

α

vehicleinertia

ωwωe

1st gear

2nd gear

3rd gearτ3

τ2

τ1


Hybrid observer approach for actual engaged gear Hybrid observer approach for actual engaged gear identificationidentification

Driveline Hybrid Driveline Hybrid Model Model

qq((kk), ), xx((tt))

Hybrid ObserverHybrid Observer

ContinuousContinuousObserverObserver

q(k)~

x(t)~

LocationLocationObserverObserver

Discrete input σσ ((kk)) Discrete output ψψ ((kk))

Continuous input uu((tt)) Continuous output yy ((tt))

inputs- gear lever- clutch pedal- engine torque- wheel torque

outputs- crankshaft speed- wheel speed

X


Location observerLocation observer

Location ObserverLocation Observer

FSMFSMObserverObserver

ψψ((kk))q(k)~

LocationLocationIdentificationIdentification

Logic Logic --

DESDESObserverObserverResidualResidual

GeneratorGenerator...

DecisionDecisionFunctionFunction

...

r1

rM

r1(t)

rM(t)

~

~

Discrete input σσ ((kk)) Discrete output ψψ ((kk))

Continuous input uu((tt)) Continuous output yy ((tt))

uu((tt))

yy ((tt))

X

Driveline Hybrid Driveline Hybrid Model Model

qq((kk), ), xx((tt))


Continuous observerContinuous observer

A switched A switched LuenbergerLuenberger observer with resets and switching controlled by the observer with resets and switching controlled by the identified plant locationidentified plant location

Stability can be achieved by using dwell time approachStability can be achieved by using dwell time approacha possible delay in location identification has to be taken intoa possible delay in location identification has to be taken into accountaccount

qq33

qq22qq11

q=q2 /~

x=(A3-G3C3)x+B3u+G3y~ ~.

x:=R113x+R0

13~ ~

x=(A1-G1C1)x+B1u+G1y~ ~.

x:=R132x+R0

32~ ~

x=(A2-G2C2)x+B2u+G2y~ ~.

q=q3 /~

x:=R121x+R0

21~ ~

q=q1 /~


Engaged gear identification algorithmEngaged gear identification algorithm

The location The location qqNN cannot be detected using a residual generator due to lack of feecannot be detected using a residual generator due to lack of feedbacksdbacks

The corresponding signature is obtained by negation of the otherThe corresponding signature is obtained by negation of the otherss

Location Identification Logic


Residual generator and decision functionResidual generator and decision function

Residual generatorsResidual generatorsLuenberger obsLuenberger obs..Unknown input Unknown input obsobs..Kalman Kalman filterfilterWalkcottWalkcott--Zak obsZak obs..Sliding mode Sliding mode obsobs..

Decision functionDecision functionPassive Passive hysteresishysteresis relay relay DebouncingDebouncing algorithmalgorithm

EXIT thresholdEXIT threshold

ENTER thresholdENTER threshold

Thresholds function of engine torqueThresholds function of engine torqueDecision function output disenabled Decision function output disenabled during residual transientsduring residual transients


Validation against the detailed hybrid model of the Validation against the detailed hybrid model of the drivelinedriveline

Unknown inputsUnknown inputswheel torque (road slope)wheel torque (road slope)clutch plate pressureclutch plate pressureengine torque pulsationengine torque pulsationgear levergear lever

SensorsSensorsengine speed quantization (1 RPM)engine speed quantization (1 RPM)vehicle speed quantization (1 Km/h) vehicle speed quantization (1 Km/h) and dead zone (5 Km/h)and dead zone (5 Km/h)

UnmodelUnmodel dynamicsdynamicsboth discrete and continuousboth discrete and continuous

drivelinedrivelinehybrid modelhybrid modelqq((kk),), xx((tt))

engine torque pulsationunknown

unknown

gear leverunknown

quantization

quantization

dead zone

ωe

ω wTwPc

Teunknown

engaged gearengaged gearhybrid observerhybrid observer


Validation against the detailed hybrid model of the Validation against the detailed hybrid model of the driveline driveline


Experimental resultsExperimental results

Obtained inObtained in Magneti Marelli Powertrain Magneti Marelli Powertrain using anusing an Opel AstraOpel Astra equipped withequipped witha Diesel engine and a robotized gearboxa Diesel engine and a robotized gearbox SeleSpeedSeleSpeed

The estimated engaged gear compared to the signal from the gearbThe estimated engaged gear compared to the signal from the gearbox control unitox control unit

The proposed algorithm was tested on several maneuvers of differThe proposed algorithm was tested on several maneuvers of different types, for a ent types, for a total of 250 gear engagementstotal of 250 gear engagements

The actual engaged gear was successfully identified within a delThe actual engaged gear was successfully identified within a delay of 250 ay of 250 msecmsec. . in 90% of casesin 90% of cases

The unsuccessful cases have been obtained in very critical maneuThe unsuccessful cases have been obtained in very critical maneuvers such as vers such as gear engagements during sharp braking gear engagements during sharp braking clutch abrupt releasesclutch abrupt releases

In these cases, the residuals exhibit large oscillationsIn these cases, the residuals exhibit large oscillations


Experimental resultsExperimental results


ConclusionsConclusions

A detailed hybrid model of the driveline has been developedA detailed hybrid model of the driveline has been developed

The model has been analyzed to obtained a reduced model used forThe model has been analyzed to obtained a reduced model used for synthesissynthesis

An algorithm for actual engaged gear identification based on hybAn algorithm for actual engaged gear identification based on hybrid observer rid observer theory has been devised theory has been devised

The proposed algorithm exhibits remarkable robustness with respeThe proposed algorithm exhibits remarkable robustness with respect to ct to unmodel unmodel dynamics, disturbances and uncertain parametersdynamics, disturbances and uncertain parameters

The proposed algorithm has been validated by bothThe proposed algorithm has been validated by bothextensive simulation with the detailed hybrid model of the driveextensive simulation with the detailed hybrid model of the drivelinelineexperimental data obtained with an experimental data obtained with an OpelOpel AstraAstra equipped with equipped with SeleSpeedSeleSpeed

Efficient drivability control allows car manufactures to design Efficient drivability control allows car manufactures to design lighter transmission lighter transmission systems characterized by higher elasticity, which will require tsystems characterized by higher elasticity, which will require the use of he use of dynamical algorithms for actual engaged gear identification dynamical algorithms for actual engaged gear identification

Hybrid Modeling and Control Hybrid Modeling and Control of the Common Rail of the Common Rail

Andrea BalluchiAndrea Balluchi((11)), , Antonio Antonio BicchiBicchi((22)),, Emanuele MazziEmanuele Mazzi((2,42,4))

Alberto L.Alberto L. SangiovanniSangiovanni--VincentelliVincentelli((1,31,3) ) ,, GabrieleGabriele SerraSerra((44))

(1)(1) PARADES GEIE, Rome, IPARADES GEIE, Rome, I(2)(2) Centro Centro InterdipInterdip. “E. . “E. PiaggioPiaggio”, University of Pisa, Pisa, I”, University of Pisa, Pisa, I(3)(3) Dept. of EECS., University of California at Berkeley, CADept. of EECS., University of California at Berkeley, CA

(4)(4) MagnetiMagneti MarelliMarelli PowertrainPowertrain, Bologna, I, Bologna, I

First HYCON Workshop on First HYCON Workshop on Automotive Applications of Hybrid SystemsAutomotive Applications of Hybrid Systems

Rome, 26Rome, 26--27 May 200427 May 2004


OutlineOutline

Common rail injection systemCommon rail injection system

Detailed hybrid model of the fuel injection system Detailed hybrid model of the fuel injection system

Rail pressure controller designRail pressure controller design

Simulation results Simulation results

Conclusions and future workConclusions and future work


New common rail injection system developed by New common rail injection system developed by MagnetiMagneti MarelliMarelli PowertrainPowertrain


Hybrid model of the common rail injection systemHybrid model of the common rail injection system

PumpPump

DRVDRV

mduty

Vbatt

mPropmProp

InjectorsInjectors

Qpump

Qinj

Qleak

Qinj-SERV

QDRV

Tcoil

PrailRailRail

enginespeed

Tfuel

QmProp


High pressure pump regulation valve (High pressure pump regulation valve (mPropmProp))

mduty

Vbatt PWM

0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.040

0.2

0.4

0.6

0.8

1

time (sec)

0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.040

5

10

15

time (sec)

tens

ione

(V

)

sequenza ripetutamduty

V

( )LVI

LTRI mprop

coilmprop +−=&V Tcoil

Flow rate - Current

Tfuel QmProp

0 50 100 150 200 2500

20

40

60

80

100

time (sec)

T coil (°

C)

Improp


High pressure pumpHigh pressure pump

Intake

Compression

QmProp ∫

Pump angle

Amout of flow rate entering each raw

x

Geometric volumeFuel charge

1 = compression

Flow rate

Compressed volumeFuel charge

Qpump

+

Qpiston_2

Qpiston_3

Qpiston_1

Pump angle

deg

mm3

mm3 /

sec

mm3


InjectorsInjectors),,(),(),,( giriETPQTPQgiriETPQQ railSERVinjfuelrailleakrailinjout −++=Prail

Tfuel

ET

giri

Qout

0200

400600

8001000

12001400

−40

−20

0

20

40

60

80

1000

200

400

600

800

1000

1200

←−30

←−10

←10

←20

←40

←60

←80

←100

Prail

(bar)T

fuel (°C)

QL

ea

k (

mm

3/s

ec)

engin

e ang

le (d

eg)

Injec

tor co

mman

d


Common railCommon rail

( )

( ))(),()(

)()()()(

1)(

tTtPKVV

tC

tQtQtQtC

tP

fuelrailbulk

piperailrail

DRVoutpomparail

rail

+=

−−=&

Fuel comprimibility modelled using Bulk parameter

Prail(0)

Tfuel

Qpump

Qout

QDRV

Prail(t)


Controller designController design

Common rail pressure controlCommon rail pressure controlDesign a tracking controller for the rail pressure that achievesDesign a tracking controller for the rail pressure that achieves tracking of a tracking of a reference pressure signal generated onreference pressure signal generated on--line by an outer loop controllerline by an outer loop controller

Main challengesMain challengesTimeTime--varying delay between valve control command and rail pressure varying delay between valve control command and rail pressure measurementmeasurementTimeTime--varying uncertainty of the actuator electrical resistancevarying uncertainty of the actuator electrical resistance

Design approachDesign approachSmith predictor controller Smith predictor controller

loop delay compensation loop delay compensation Adaptive algorithmAdaptive algorithm

onon--line identification of the electrical resistanceline identification of the electrical resistanceFirst attempt: controller synthesis based on a plant meanFirst attempt: controller synthesis based on a plant mean--value modelvalue model


MeanMean--value model for controller synthesisvalue model for controller synthesis

nonlinear CT modelnonlinear CT model

piecewise polynomial CT modelpiecewise polynomial CT model


Rail pressure controllerRail pressure controller

Sampling time 5msecSampling time 5msec

+

-

Feed Forward

++

-

Injection system

dsTe−

Smith predictor

Delay-free model dTse ˆ−

PIDanti-windup

mduty Prail

+

Delay-free Plant

-

+Prailref


Hybrid closedHybrid closed--loop system simulation results loop system simulation results

Smith controlled pressureReference pressure


Smith Predictor and adaptive controlSmith Predictor and adaptive control

Adaptive Algorithm

Improp-est 1° order Sliding mode observer

Rest

current estimator

+

-

Feed Forward

++

-

Smith predictor

Delay-free model

PIDanti-windupPrailref

mduty Prail

+

Common Rail Hybrid Model

-

+

dsTe−

Low Pass Filter


ClosedClosed--loop hybrid model simulation resultsloop hybrid model simulation results

Smith controlled pressureReference pressureMagneti Marelli controlled pressure

Smith controlled pressureReference pressure


Limits of the controller designed using the meanLimits of the controller designed using the mean--value model approachvalue model approach

3 4 5 6 7 8 9 10

200

250

300

350

400

450

time (sec)

Pra

il (b

ar)


Hybrid multiHybrid multi--rate controllerrate controller

QHP(t) p(t)COMMON RAIL

FLOW RATEVALVE

HP PUMPvalve fuelflow rate

HP fuelflow rate

CM pressure

injectorsfuel flow rate

desiredHP fuel

flow rate

flow ratevalve

command

FLOW RATEVALVE

CONTROLLERm(l)

flow ratefeedback

+-

QINJ(t)

+

pref(l)pressure reference

-

pressuresensor~QHP(k)

CM PRESSURECONTROLLER

QFRV(t)

perr(k)

pressureerror

valveactuator

5ms fuel delivery 5ms

interpolator decimator


Tracking behavior of the hybrid multiTracking behavior of the hybrid multi--rate rate controllercontroller

2 3 4 5 6 7 8200

220

240

260

280

300

320

340

360

380

time (sec)

Pra

il (b

ar)

PrailPrailREF


ConclusionsConclusions

A detailed hybrid model of the common rail fuel injection systemA detailed hybrid model of the common rail fuel injection system has been has been presentedpresented

The hybrid model describes the pulsating evolution of the rail pThe hybrid model describes the pulsating evolution of the rail pressure due to HP ressure due to HP pump supply and multiple fuel injectionspump supply and multiple fuel injections

The proposed switching controller has been designed using a meanThe proposed switching controller has been designed using a mean--value model value model of the plant and employs of the plant and employs

a Smith Predictor to compensate the timea Smith Predictor to compensate the time--varying loop delayvarying loop delayan adaptive algorithm to adjust the static gainan adaptive algorithm to adjust the static gain

Simulation results obtained with the hybrid closedSimulation results obtained with the hybrid closed--loop model show that the loop model show that the controller perform satisfactorily if the reference pressure is ncontroller perform satisfactorily if the reference pressure is not too fastot too fast

Controller design based on hybrid methodologies achieves better Controller design based on hybrid methodologies achieves better performances performances and ensures tracking of fast pressure referencesand ensures tracking of fast pressure references


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