Habilitation à Diriger des Recherches Guillaume Colin 5 décembre 2013

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Optimisation du contrôle de la chaînede traction des véhicules automobiles

Optimization of powertrain management for automotive vehicles

Habilitation à Diriger des Recherches

Guillaume Colin5 décembre 2013


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2

Outline

Extended curriculum Vitae

General context

Research Activities Supervisory control : Optimal approach Control : Robust or Predictive approach Observer : Polytopic approach

Conclusion & future prospects


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Curriculum vitae

3

ESSTIN engineer

Master in

Automatic contro

l

20032006

2011PhD in Energetics

Scientific E

xcellence

bonus (PES)

20132007

Habilitation defense

Monitor

ATER Assistant Professor

A. Ivanco

M. Debert

C. Deng

J. El Hadef

P. Michel

T. Miro-Padovani

A. Lamara

PhD Students33 years old

Married

2 children


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Supervision 7 PhD students (340%)

3 already defended (160%) and 1 in january (60%)! 6 Masters (450 %)

Production 17 international papers and 31 international conferences 1 book chapter 3 patents

Academic Partnership Supelec Paris (from 2012) CRAN (from 2003) IMS (from 2006) LAMIH (2004-2007) ETH Zurich (from 2008)

Supervision, production, partnerships

4


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Signal treatment22%

Automatic control10%

Informatic11%

Internal Combustion Engine

9%

Powertrain control30%

Projects11%

Tutoring7%

Main teaching activities (L2, M1, M2)

Automatic control, Informatics, Signal treatment, Powertrain Control and internal combustion engine

Around 250 h/year from License 2 to Master 2

Applied control to automotive (M2)

Teaching Activities

5


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Teaching Elected to the council of Polytech Orléans Co-responsible of the international Master Automotive

Engineering for Sustainable Mobility (AESM) Co-responsible of the speciality VSE of Polytech Orléans Correspondent for Polytech Orléans of Master VTD (IFP School,

ENS Cachan, Supelec, Centrale) Research

Research group on Automatic control and automotive (GTAA) Correspondent for University of Orléans of the Society of

Automotive Engineers (SIA) 7 Industrial Contracts from 2006 (418k€)

Administrative responsabilities

6


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Global view of my researches

7

ENERGETICS (62)

Physical models

My activities

AUTOMATIC (61)

Control oriented models

Optimal Control

Robust Control

Observers

Hybrid Vehicle

Internal Combustion

engine

Predictive Control

Battery

Pow

ertr

ain

Model Based


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General context : why my work?

8

Energy demand increases

Big part of oil used for transport

Number of vehicles increases

Reduce fuel consumption and pollutant emissions with drivability is a challenge! India

China

Thailand

BrazilRussia

IsraelSlovakia

UK QatarPolandBelgium SwitzerlandGermany

France CanadaFinland

Italy

Luxembourg

USA

0

100

200

300

400

500

600

700

800

900

0 20 000 40 000 60 000 80 000 100 000 120 000

Vehi

cles

per

100

0 pe

ople

GDP per capita Source : FMI & world bank data

Source : IAE, 2013


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Global view of energy conversion for vehicles well-to-tank tank-to-vehicle vehicle-to-kms

An efficient energy managementis important to obtainmaximum efficiency!

How to reduce energy demand for mobility ?

9

Primary energy sources

Energy conversion efficiency « well-to-tank »

On-board energy storage

Vehicle kinetic and potential energy

« tank-to-vehicle »

« vehicle-to-kilometers »

Energy conversion efficiency

Vehicle efficiency

Driving and altitude profile

(Guzzella, Sciarretta, Vehicule Propulsion Systems)


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SupervisorycontrolEnergy

Management

Dynamic control

Powertrain control

Accel.pedal

Brakepedal

Torquesource 1

Torquesource 2

actuators

sensors

ObserversEstimators

states

constraints

Driver Powertrain

General powertrain control scheme Supervisory control Dynamic control Observer

Where is my work?

10


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Management

Dynamic control

Powertrain control

Accel.pedal

Brakepedal

Torquesource 1

Torquesource 2

actuators

sensors

ObserversEstimators

states

constraints

Driver Powertrain11

Outline


General context


Conclusion & future prospects


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12 1216

20

24

28

32

Engine speed [rpm]

En

gin

e t

orq

ue [

Nm

]

Conventional efficiency W/m*PCI*100[%]

Cmax

1000 2000 3000 4000 5000 6000

20

40

60

80

100

120

140

Internal Combustion Engine Maximal efficiency: just close to full load

¾ of vehicle use at low/part load Downsizing: smaller engine + supercharging (turbo)

Non-reversible thermodynamic cycleKinetic energy is lost during braking Hybridizing: storage + reuse

Hybrid Vehicle Fuel economy (Stop&Start, downsizing, recuperation …) Cost, weight, recycling

Energy Management : Optimal approachWhy hybrid cars?

12


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Evaluation of the strategies Driving cycles : standard, Artemis and creation with Markov chains!

Objective : find u* that minimizes fuel consumption over the cycle

w.r.t

Energy Management : Optimal approach

13

Initial conditionState evolution (often state of charge)

Fuel consumptionFinal constraint

0 500 1000 1500 2000 2500 3000 3500 40000

50

100

150Vehicle speed [kmph]

Artificial cycle #11

0 500 1000 1500 2000 2500 3000 3500 40000

50

100

150


0 500 1000 1500 2000 2500 3000 3500 40000

50

100

150

Time (s)


0 200 400 600 800 1000 12000

50

100

Vehicle Speed [kmph]

NEDC

0 500 1000 1500 2000 2500 3000 3500 40000

50

100

150

Time (s)

Traffic jam

Urban

Road

Highway

0 200 400 600 800 1000 1200 1400 1600 18000

20

40

60

80

100

120

140

Time (s)

Veh

icle

spe

ed [

kmph

]

WLTC


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Which approaches? Optimal strategies : often offline, but necessary to benchmark!

Dynamic programming (DP) based on Bellman’s principle of optimality

Pontryagin Maximum Principle (PMP) based on dual problem


x

Backward reasoning

timetf t r

Difficult to find the optimal co-state!

14


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Which approaches and why? Sub-optimal strategies : compatible and relevant for the application

Heuristic Equivalent Consumption Minimization Strategy (ECMS) is based on the

Hamiltonian and the PMP

Adaptive-ECMS Driving Pattern Recognition Variable Penalty coefficient s(x)

Model Predictive control (MPC) Telemetric-ECMS : use of GPS to adapt s(t) On-line Dynamic Programming

Black Box : learning the off-line strategy and apply it on-line


15

Fuel Power Electrochemical Power


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Hybrid Pneumatic Engine (HPE)1. Combustion chamber2. Air tank3. Charging valve

Energy Management : Optimal approachHybrid Pneumatic Engine

16

2

3

1

Configuration HEV HPE

First energy source Combustion engine Combustion engine

Second energy source Additional electrical machine

Integrated pneumatic motor

Energy distribution Power electronics Charging valve

Energy storage Battery Air tank

PhD A. Ivanco


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Discrete time backward vehicle model Vehicle cycle speed Acceleration Desired Engine Torque Fuel

and Air consumption

Four driving modes Two propulsive modes umode: pneumatic µp and conventional µc

One recuperative: pneumatic pump One alternative: engine stop

17

Transmission

Driving cycle speed

Gear box

Acceleration

Control : chosen mode

Enginespeed

DesiredEngineTorque

Traction force

tank pressure

Fuel consumption

Air consumption mF)(k)(kvd

)(kgb

)(kumode

)(kmtank

)(km fuel

)(kTe

)(ke

)(kptank

)1( kptankVolume

dynamicsderivate

PhD A. Ivanco


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Comparison of the fuel consumption w.r.t. Dynamic Programming

Comparison of the fuel consumption w.r.t Conventional

Energy Management : Optimal approachHybrid Pneumatic EnginePhD A. Ivanco

-6

-5

-4

-3

-2

-1

0Traffic-Jam Urban Road Highway NEDC Cycle 11 Cycle 12 Cycle 13

A-ECMS NN ECMS Rule-based

Sub

optim

ality

[%

]

-45,00

-40,00

-35,00

-30,00

-25,00

-20,00

-15,00

-10,00

-5,00

0,00Traffic-Jam Urban Road Highway NEDC Cycle 11 Cycle 12 Cycle 13

DP A-ECMS NN Rule-based ECMS

Fue

l con

sum

ptio

n ga

in [

%]


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Real-time sub-optimal generic approaches with minimal sub-optimality for Hybrid pneumatic vehicle

Pneumatic is a credible alternative to electric this project continue in our lab, at ETH, with industrial partnerships

And for Hybrid Electric Vehicle?


19

Strategy Advantages Disadvantages

Causal Simple to use and adapt tuning, performance

ECMS Similar results to causal pressure variations, global tuning

A-ECMS (Driving Pattern)

global tuning, charge sustainingLocal optimization, add. gain

More tuning parametersIdentification delay

Neural Single network over different cycles Training effortNeed to observe some variables

PhD A. Ivanco


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Powertrain example Series-parallel hybrid High efficiency transmission 2 manipulated variables u

ICE torque (Ti) Electrical power (Pe)

Quasi-static modeling

Consider only one dynamic = State of Charge

Energy Management : Optimal approachHybrid Electric Vehicle

20

PhD M. Debert

Cycle Vehicle

v (m/s)

a (m/s²)

Trans-mission

T0(Nm)

ω0(rad/s)

u= [Ti, Pe] InternalCombustion

Engine

Electricalmachine(s)

CO2

Pbat

Ti(Nm)

Te(Nm)

ωi(rad/s)

ωe(rad/s)

Batteryx=SOC


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Control problem = optimal control

Off-line optimization (DP) knowing the whole cycle


21

subject to

CO2 emission

State of Charge

constraint on SOC

PhD M. Debert

0 200 400 600 800 100030

35

40

45

50

55

60

0

50

100

150

200

250

Temps (s)

0 200 400 600 800 100030

40

50

60

-4

-2

0

2

x 10421

Ti [Nm]

Pe [W]Charge

Discharge

SO

C [

%]

020406080

100120

NEDC Traffic jam Urban Road Highway

Loss

es (M

J/10

0km

)

thermal electrical

Losses comes from ICE part


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Possibility to consider only thermal path for a given demanded power Best operating line

System more constrained Remove one control Computation time is reduced

Suboptimality (DP)


22

PhD M. Debert

-60%

-50%

-40%

-30%

-20%

-10%

0%

-22%

-39%

-51%

-21%

-1%

-34%

-20%

-35%

-51%

-20%

-0,50%

-32%

Optimal strategy Sub-optimal strategy

NEDC Traffic-Jam Urban Road Highway FTPEngine speed [rpm]

Tor

que

[N.m

]

1000 1500 2000 2500 3000 3500 40000

50

100

150

200

250

300

250

300

350

400

450

500

iso power

BSFC (g/kWh)

Maximal torque

Best efficiency


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Energy Management : Optimal approachPredictive EMS for HEV

23

PhD M. Debert

Knowing the future improves the efficiency Driving conditions influences CO2 emissions Available information about future driving conditions (LIDAR, GPS…)

Sub-optimal DP is suitable for real-time on finite horizon Results

Influence of possible prediction horizon Influence of prediction uncertainty : prediction is not perfect

Representative (in vehicle acceleration)

Predictive Energy Management In real-time with dynamic programming Fuel consumption decreases exponentially with prediction horizon A certain robustness is observed Relevant energy management to improve fuel consumption

Reduce fuel consumption is not sufficient: constraints!


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Principle Global objective

Dual problem with Hamiltonian :

Pollution constraint into EMS ?

24

PhD P. Michel

Fuel consumption Pollutant emissions

Additional dynamics!


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Simulation on Worldwide harmonized Light vehicles Test Cycle

A good trade-off between pollutant and fuel consumption permits to be under the standards

3WCC model under validation & strategy will be implemented

Pollution constraint into EMS ?

25

Veh

icle

Spe

ed (

km/h

)

Time (s)

200 400 600 800 1000 1200 1400 1600 1800

20

40

60

80

100

120

140WLTC

SO

C (

%)

Time (s)200 400 600 800 1000 1200 1400 1600 1800

35

40

45

50

55

3WC

C t

empe

ratu

re (

°C)

Time (s)200 400 600 800 1000 1200 1400 1600 1800

0

200

400

600

800

Electric mode Hybrid modeFull-engine mode

PhD P. Michel

-14

-12

-10

-8

-6

-4

-2

00 0,05 0,1 0,15 0,2 0,25 0,3

Pollu

tant

red

ucti

on [

%]

Over Fuel Consumption (%)

CO

HC

Nox


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26

Battery : key stone of electrified vehicles Ageing is a technological lock due to

Use (depth of Charge/Decharge) Time State of Charge High Temperature : negative impact of first order

Battery ageing into EMS ?

anode (-) cathode (+)

Active material(graphite C6)

Active material(graphite C6)

Collector(copper)Collector(copper)

charge carrier (lithium)charge carrier (lithium)

electrolyte(lithium salt LiPF6 & solvent)

electrolyte(lithium salt LiPF6 & solvent) SéparatorSéparator

Collector(aluminium)

Collector(aluminium)

Active material(oxyde "LMO

blend" LiMn2O4, LiCoO2, LiNiO2)

Active material(oxyde "LMO

blend" LiMn2O4, LiCoO2, LiNiO2)


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27

Battery constraint into EMS ?PhD T. Miro-Padovani

Principle Idea : take into account ageing through thermal management

Dual problem with Hamiltonian

Existing tradeoff between fuel consumption and final cell temperature

Propose to estimatecell temperature!


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Drivability constraint into EMS ?

28

PhD T. Miro-Padovani

Weight on kinematic modes changes

with k=0

Uncomfort Hamiltonian Influence of k on comfort

Trade-off between fuel consumption and uncomfortwithout this constraint in the EMS, the vehicle is undriveable

Prototype vehicle under validation

Optimal control is erratic uncomfortable


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29

Main contributions Real-time sub-optimal energy management strategies for hybrid

vehicles with minimal sub-optimality Demonstration of potential of Hybrid vehicles (pneumatic or electric)

and maximization of this potential by using prediction/recognition Strategies that takes into account in the Energy Management

constraints such as pollutant emissions, comfort or battery And also …

Implementation of sub-optimal strategies on real vehicles and on high fidelity models

State of Charge has been redefined into State of Energy

Strategies for Plug-in hybrid Realistic Dynamic Programming

(non-erratic control)

Energy Management : Optimal approachConclusion


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Management

Dynamic control

Powertrain control

Accel.pedal

Brakepedal

Torquesource 1

Torquesource 2

actuators

sensors

ObserversEstimators

states

constraints

Driver Powertrain30

Outline


General Context


Conclusion & future prospects30


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Diesel or Gasoline : quite the same control problematic

General Torque Control Scheme

Control : Robust or Predictive approach

31

Diesel engine air path

Qair

VWG

Wastegate valve

IntercoolerThrottle Valve

VTH

Intercooler

EGR ValveVEGR

pboost

pman

pexh

Qegr

pman

VTH

lambda sensor

turbine

compressor

Throttle valvemanifold

pboost

pamb

VWGwastegate

Gasoline engine air path

Air Path

Air Set point generation

Supervisor

Air Path control

Air mass

set pointairQ

boostp

WG

EGRspboostp _

Torque

Set point

spairQ _ Th

spmanp _

manpFuel mass

Set point

DieselGasoline


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Engine control research meets industrial constraints Need quasi-systematic approaches with tools

Two model based control approaches Robust control : classical and Crone frequency domain Non linear Predictive control physical or generic model in time

domain Complete methodology from specifications to real

implementation for robust control1. Choice of excitation signals (sinusoid-based)2. Frequency response computation (FFT)3. System analysis4. Robust Control design (classical or crone)5. Experimental validation

Control : Robust or Predictive approach

32

identification


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1 & 2. Identification

3. System analysis Condition Number Relative Gain Array Column Diagonal Dominant Degree

Robust control of the air path

33

73 operating point

1000 1500 2000 2500 3000 3500 40000

20

40

60

80

100

120

140

160

180

200Operating points of speed and torque

Engine speed/rpm

Eng

ine

torq

ue/N

.m

pboost

VEGR

VWG

Qair

Ne T

VTH

G11

G21

0

0

0

0+

VEGR

VWG

Qair

pboost

Ne T

VTH

0

0

G12

G22

0

0+

VEGR

VWG

Qair

pboost

Ne T

VTH

0

0

0

0

G13

G23

73 operating point

Qair

pboost

EGR WGTh

Qair

pboostTh

EGRQair

WGwastegate

intercoolerThrottle valve

Th

intercooler

EGR valveEGR

pboost

pman

WG

PhD C. Deng and A. Lamara

73 operating point

Qair

pboost

EGR WGTh

100

-1

-0.5

0

0.5

1

1.5

2

Frequency(rad/s)

Magnitude

RGA elements of G11

100

-0.6

-0.4

-0.2

0

0.2

Frequency(rad/s)

Magnitude

RGA elements of G12

100

-0.5

0

0.5

1

1.5

2

Frequency(rad/s)

Magnitude

RGA elements of G13

100

-1

-0.5

0

0.5

1

1.5

Frequency(rad/s)M

agnitude

RGA elements of G21

100

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Frequency(rad/s)

Magnitude

RGA elements of G22

100

-1

-0.5

0

0.5

1

1.5

Frequency(rad/s)

Magnitude

RGA elements of G23

RGA elements of G11

RGA elements of G12

RGA elements of G13

RGA elements of G21

RGA elements of G22

RGA elements of

G23


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Non-square

Square

Torque set point [Nm]

Engine speed [rpm]

Time [s]0 50 100 150 200 250 300 350

0

20

40

60

80

100

120

Time(s)

Spee

d(km/

h)

High band of vehicle speed

Set point of vehicle speed

Low band of vehicle speedSquare control

Non-square control

0 50 100 150 200 250 300 3500

20

40

60

80

100

120

Time(s)

Spee

d(km/

h)




Non-square control

0 50 100 150 200 250 300 3500

20

40

60

80

100

120

Time(s)

Spee

d(km/

h)




Non-square control

4. Robust control synthesis Analysis decentralized

MIMO

Here, crone synthesis (collaboration IMS) Open loop optimization Resonance peak optimization w.r.t. sensibility functions constraints Take into account every processes

5. Experimental validation Comparison square/

non square 4% NOx reduction

Throttle (more EGR)

Robust control of the air path

34

PhD C. Deng and A. Lamara

Set point

Measure

Set point

Measure

Boost pressure [bar]

Actuators [%]

Air flow [kg/h]


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35

Application to gasoline engine air path Step 1: Physics-based model

Quasi-systematic predictive control design

Exhaustmanifold

௩௧ ǡߠ��௩௧

Turbine

� �

Wastegate

�௪

Cylinders

�

Intakemanifold

ǡߠ��

Compressormanifold

� Heatexchanger

ǡߠ��

Throttle�௧ �௧

� �௨Flowcomponent

Control volume

Legend

Physics-based model

Nonlinear

MPC

Explicit solution

PhD J. El Hadef

500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 60000.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Engine speed Ne [rpm]

Inle

t man

ifol

d pr

essu

re p

man

[ba

r]

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.090

0.02

0.04

0.06

0.08

0.1

Test bench measurements

Con

trol

-ori

ente

d m

odel

Engine mass flow rate Qeng

[kg/s] | +/- 5%

600 700 800 900 1000 1100 1200 1300600

800

1000

1200

1400

Test bench measurements

Con

trol

-ori

ente

d m

odel

Outlet manifold temperature avt

[K] | +/- 10%

0 5 10 15 20 25 30 35

1

1.5

2

Time [s]

Compressor outlet pressure papc

[bar]

Experimental measurementsControl-oriented model

0 5 10 15 20 25 30 350

0.5

1

1.5

2

Time [s]

Inlet manifold pressure pman

[bar]

0 5 10 15 20 25 30 351

1.5

2

2.5

3

Time [s]

Outlet manifold pressure pavt

[bar]


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36

Step 2: Non linear model predictive Control Find u that minimizes a “thermodynamics” criteria


Physics-based model

Nonlinear

MPC

Explicit solution

PhD J. El Hadef

Cylin

der

pre

ssure

Cylinder volume

+_

pman

pexh

0 0.5 1 1.5 2 2.5 3 3.5 4

0.5

1

1.5

2

Time [s]

Inlet manifold pressure [bar]

Set pointSimulation+/- 100 mbar

0 0.5 1 1.5 2 2.5 3 3.5 4

1

1.5

2

Time [s]

Compressor downstream pressure [bar]

0 0.5 1 1.5 2 2.5 3 3.5 40

50

100

Time [s]

Throttle opening [%]

0 0.5 1 1.5 2 2.5 3 3.5 40

50

100

Time [s]

Wastegate opening [%]

Implicit NMPC

Implicit NMPC


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Step 3: Explicit solution (collaboration Supelec) Approximate the control law by a piecewise affine function Store the control into binary search tree Add a slow integrator to ensure zero-steady state error


Physics-based model

Nonlinear

MPC

Explicit solution

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u=A3x+B3

x(1)

x(2)

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PhD J. El Hadef

Turbochargedgasolineengine

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++0 5 10 15 20 25 30 35

0

0.5

1

1.5

2

Time [s]

Inlet manifold pressure pman [bar]

Set pointSimulation+/- 100 mbar

0 5 10 15 20 25 30 351

1.5

2

Time [s]

Compressor downstream pressure papc [bar]

0 5 10 15 20 25 30 350

50

100

Time [s]

Throttle opening uthr [%]

0 5 10 15 20 25 30 350

50

100

Time [s]

Wastegate opening uwg [%]

Explicit NMPCIntegrator

Explicit NMPCIntegrator


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Main contributions Two quasi-systematic methodologies of control synthesis from

specifications to real application for non linear multivariable systems (square or not) function of desired performances

Maximizing engine efficiency and minimizing pollutant emissions using a good knowledge of the engine

And also … Implementation of robust and predictive engine torque control on

real engine test benches and on high fidelity models

Control : Robust or predictive approachConclusion


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Management

Dynamic control

Powertrain control

Accel.pedal

Brakepedal

Torquesource 1

Torquesource 2

actuators

sensors

ObserversEstimators

states

constraints

Driver Powertrain39

Outline

Curriculum Vitae

General context


Conclusion & future prospects39


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Why estimate « on-line » of non-measured states ? Need of variable estimation but no sensor (or too costly) e.g. In-cylinder air mass, cell temperature, turbo-charger speed, …

How? Model Measurement feedback

Which model? Discrete time Linear Parameter Varying (LPV) model

Estimation and observation : why?

Measured outputInput Unknown statesx yu

Estimated stateObserver

x

with and

Measurement noise


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Observer

Objective : find Li of the variable feedback gain

Resolution? Polyquadratic stability hypothesis Linear Matrix Inequalities (LMI)

Take into account the noise Single high level tuning parameter s Link between error and noise through and

Polytopic observers : principle

without with


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Battery ageing : Need to obtain cell temperature

Mass production sensor : too much filtered!

Thermal modeling42

Application to electrified vehiclePhD M. Debert


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Obtained model : Linear Parameter VaryingLPV

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Application to electrified vehiclePhD M. Debert


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Results Good estimation of cell temperature Internal resistance estimation

Perspectives Possibility of diagnosis through

internal resistance Permits to take into account internal

battery parameters into vehicle energy management

Application to electrified vehicle

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PhD M. Debert


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Main results Linear parameter varying observers using physical and generic

models (e.g. neural networks) Easy tuning between measurement confidence and model fiability

through a single high level parameter Application to

In-cylinder air-mass estimation Battery temperature (patent)

And also for estimation … Black box modeling (volumetric efficiency) Turbocharger modeling and look-up-tables extrapolation using a

good mix between physics and generic models

Observer : polytopic approachConclusion


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General philosophy : a model based approach Plant nonlinearities and specificities by gray box modeling (physical and generic)

Real time energy management strategies with minimal sub-optimality for Hybrid vehicles : credible & efficient Reduction of the complexity of optimal strategies with real-time sub-optimal generic

approaches and the choice of prediction to look-ahead system behavior Demonstrated the potential of hybrid vehicles (pneumatic or electric), proposition of

solutions to maximize this potential by using prediction/recognition and by taking into account constraints such as pollutant emissions, comfort or battery temperature

Quasi-systematic methodologies for internal combustion engine control Methodology of robust or predictive control synthesis from specifications to real

application for non linear multivariable systems compatible with industrial constraints Maximizing engine efficiency and minimizing pollutant emissions using a good

knowledge of the engine Estimation of non-measured states

Definition of Linear Parameter Varying observer with an easy tuning between measurement confidence and model fiability through a single high level parameter

And experimental validations of nearly all of the proposed methods

To conclude, in summary

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Multi-criteria global optimal control Taking into account a maximum of constraints Increase the number of states into strategy Optimization on a dynamic trajectory between different modes

Today is static, tomorrow will be dynamics In Real time! Application to mobility and energy

Homogeneous combustion into hybrid electric vehicle new possibilities for the vehicle

Vehicle environment (habitation, drivability, pollution, battery) Energy Management with several sources, e.g. wind hybrid power system

Future prospects 1

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Robust Predictive control Prove properties (robustness) to predictive control for non linear

multi-variable systems and choose explicit to solve Application to mobility and energy

Pollutant modeling and control (cold) When delay are important, prediction will help to stabilize (low pressure EGR) Robust Predictive Energy Management w.r.t dispersions (Flex-fuel, …)

Applications to energy and automotive are a great source of complex problem to solve Encountered systems : nonlinear, constrained, multi-variable,

multi-objectives, with limited computation time Fundamental aspects = application independent : generic

ENERGETICS FOR CONTROLCONTROL FOR ENERGETICS

Future prospects 2

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Lars and Janan for coming to my defense Alain, Thierry-Marie and Olivier for their report Yann and Gérard for orienting me and their friendship! Alain, Domi, Benoit, Kristan, Sylvie and every laboratory

colleagues for their helps All the PhD students : Andrej, Maxime, Chao, Jamil, Pierre,

Abderrahim, Thomas, and … Ma famille : Emilie, Lucie, Timothée Mes collègues de Polytech Mes amis, mes parents et mes beaux parents

En bref, merci à tous !!!

Many Thanks!

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Questions?

0 50 100 150 200 250 300 350 400 450 5000

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Non-square control

Air tank

Documents

Habilitation à Diriger des Recherches Guillaume Colin 5 décembre 2013