« study of energetic systems using energetic macroscopic … · 2013-11-24 · 6 barcelona,...

EMR seminarUniversitatPolitècnica de CatalunyaNov. 2013

Prof. A. Bouscayrol(University Lille1, L2EP, MEGEVH, France)

based on the works of “eV” group of Control teamof L2EP Lille

http://emrwebsite.org/

«« Study of Energetic Systems usingStudy of Energetic Systems usingEnergetic Macroscopic Representation Energetic Macroscopic Representation »»

2Barcelona, November 2013

«« Study of energetic systems using EMR Study of energetic systems using EMR »»

- Outline -

1. Research at L2EP / Univ. Lille1

2. Requirements for study of EVs and HEVs

3. EMR and Inversion-based control

4. Example of an EV

5. More advanced Example

Simulation of an EV



- University Lille1, Science & Technology -

Lille and suburbs more than 1.5 million inhabitants

4 universities

(150,000 students)

University of Lille 1

(30,000 students)

at the crossroad of Paris, London and Brussels

Paris

Lille

London

BrusselsCologne

Amsterdam

1h

1h200h35

TGV (railway)airport

0h50

France



- L2EP Lille -

28 professors and associate professors, 41 PhD students, 12 technical and administrative staff

Laboratory of Electrical Engineering and Power (L2EP)http://l2ep.univ-lille1.fr/



Prof. F. Piriou« Optimisation »Prof. M. Hecquet

« Optimisation »Prof. M. Hecquet« Numerical Modelling »

Prof. S. Clénet« Numerical Modelling »

Prof. S. Clénet

- Research at L2EP -

« Electrical grid »Prof. B. Robyns

« Electrical grid »Prof. B. Robyns

« Control »Prof. B. Lemaire-Semail

« Control »Prof. B. Lemaire-Semail

« Power Electronics »Prof. P. Le Moigne

« Power Electronics »Prof. P. Le Moigne



Fq/b

Vid id C

Vid-ref id-ref CrefFq-

ref

vq-ref

vqN

FTCriq Vid C

simulation devéhicule Electrique

Modelling and control tools(COG, EMR, BMC, resonant controllers..)

Modelling and control tools(COG, EMR, BMC, resonant controllers..)

« Control »Prof. B. Lemaire-Semal

« Control »Prof. B. Lemaire-Semal

- “Control” team of L2EP -

3 Professors4 Associate Professors12 PhD students

A. Bouscayrol« Electricityand Vehicle »

A. Bouscayrol« Electricityand Vehicle »

X. Kestelyn«Machine tools »

X. Kestelyn«Machine tools »

B. Lemaire-Semail« Electro-active

actuators »

B. Lemaire-Semail« Electro-active

actuators »

E. Semail« Multiphasemachine »

E. Semail« Multiphasemachine »

Formalisms bring solutions for new applications

New applications lead to the improvement of formalisms



1980 1990 2000 2010

PetriNets(use)

PowerElectronics

(COG)Causal

OrderingGraph

ElectricDrives

LEEI Toulouse (France)

[Hautier 1996]

(MMS)MultimachineMulticonverter

Systemdescription

ElectromechanicalSystems

LEEI / GREENLESiR / GE44

GdR SDSE-ME2MS(France)

[SMM 2000]

(EMR)Energetic

MacroscopicRepresentation

Univ. Trois Rivières (Ca)EPF Lausanne (CH)

FEMTO-STMEGEVH network

[Bouscayrol 2003]

- Modeling and control tools -



• University of Lille, Polytech Lille, EC Lille– Master 1: COG - EMR initiation – Master 2: COG - EMR further development

• Other French Universities and Engineering Schools– COG: Toulouse, Cachan, Belfort, CNAM Paris– EMR: Cachan (since 2004) Belfort (since 2006), ParisTech (2010)

• Universities abroad France– Univ. de Québec Trois-Rivières (since 2002)– EPF Lausanne (since 2005) – Univ. Tsinghua (2008) / Univ. Barcelona (2010)– Univ. Helsinki (2011) / Univ. Graz (2012)

• EMR Summer Schools– EMR’06, Lille (France) / EMR’08, Harbin (China)– EMR’09, Trois Rivières (Canada)– EMR’11, Lausanne (Switzerland) / EMR’12, Madrid (Spain) – EMR’13 Lille (France) / EMR’14 Coimbra (Portugal)

- Graphical description and Education -



(Warwick, UK)

(Switzerland)

(Trois-Rivières,Toronto,Canada)

(Harbin, China)

(the Netherlands)

(Beijing, China)(Madrid, Spain)

Industries : EADS, ETEL, Nexter System, PSA Peugeot Citroën, ST-micro, SNCF, Siemens Transportation Systems, Valéo …

(Cordoba, Argentina)

(Nanjing, China)

(Saitama, Japan)

- Collaborations using graphical descriptions -



- Scientific Influence -

• Special sessions in Conferences“Graphical descriptions for modelling and control”

IEEE-IECON’06 (Paris), ElectriMACS’08 (Québec), IEEE-VPPC’09 (Detroit), IEEE-VPPC’10 (Lille), IEEE-VPPC’11 (Chicago)

“HEVs modelling and control” (within the framework of MEGEVH)IEEE-VPPC’06 (Windsor, UK), IEEE-VPPC’08 (Harbin, China), IEEE-VPPC’10 (Lille)

“Hardware-In-the-Loop simulation” (within the framework of MEGEVH)IEEE-VPPC’07 (Dallas), IEEE-VPPC’08 (Harbin, China), IEEE-ISIE’08 (Cambridge, UK)

“Multiphase drives”IEEE-VPPC’10 (Lille)

• Tutorials & Keynotes in international conferences“Tactile actuators”, EuroHaptics’06 (Paris), ECCE-EPE’11 (Birmingham)“Hardware-In-the-Loop simulation”, EVS’24 (Stavanger, Norway, 2009)“HEVs energy management”, IEEE-VPPC’09 (Detroit), IEEE-IECON’09 (Porto)“HEV and EMR”, IEEE-VPPC’13 (Beijing)

• Guest Editors of archival journals“Hardware-In-the-Loop simulation”, IEEE trans. on Industrial Electronics (2010)“Advanced transportation systems”, IEEE trans. on Vehicular Technology (2011)

• Conference organizationsIEEE-VPPC 2010 (Lille) EPE-ECCE 2013 (Lille)

Special sessionElectrIMAC’08

Tutorial IEEE-VPPC’09


«« 2. Requirement for the study2. Requirement for the studyof of EVsEVs and and HEVsHEVs »»

Based on the works ofMEGEVH, French network on HEVs

12

Barcelona 2013

MEGEVH - MEGEVH network -

Coordination:Prof.A. Bouscayrol

6 projects7 PhDs in progress6 PhDs defended

8 industrial partners10 academic Labs

(Energy management ofHybrid Electric Vehicles)

http://l2ep.univ-lille1.fr/megevh.htm

Lille

Paris

Lyon

Toulouse

Valenciennes

Belfort

LTE

LTN

LAMIHLAMIH

Bordeaux

13

Barcelona 2013

MEGEVH

MEGEVH-macro

MEGEVH-strategy

MEGEVH-optim

theoretical developments

MEGEVH-storeMEGEVH-FC

Development of modellingand energy management

methods

independentlyof the kind of vehicle

- MEGEVH philosophy -

experimental platforms

Reference vehicle

Paper Prize Award of IEEE-VPPC’08

Paper Prize Award of IEEE-VPPC’12



thermalengine

Fuel MT

Thermal vehicle:- fossil fuel- pollution- low efficiency

PE electrcalmachineBattery

Electric Vehicle:- long charging time- low range- battery cost

http://www.thinkev.com/

Think city

H2 PEFC electricmachine

Fuel Cell vehicle :- H2 production- Fuel Cell cost- Fuel cell lifetime

http://www.honda.com/

Honda Clarity FX

- Mono-source vehicles -



fuel

Hybrid vehicle:• several source of energy• advantage of each technology• important cost• complex control

Battery PE

thermalengine

MTelectricalmachine

Key issues for HEVs:1. topologies of the power train2. design of sources and components3. control and energy management

Toyota Prius 3

http://www.toyota.com/

http://www.mpsa.com

Peugeot 3008 HY4

- Multi-source vehicles -



ICE

EM

BAT

EG

Series Parallel HEV

Fuel

ICE

EM

BAT

Fuel

??

ICE

EM

BAT

Fuel

EG

Series HEV electrical node

ICE

EM

BAT

Fuel

Parallel HEV

mechanical node

power flows

- HEV topologies -



• thermal traction

• internal charge of battery

• Stop & Go

• regenerative braking

• electrical boost

• electrical traction

• external charge (Plug-in HEV)

EMICE

ICE EM

ICE EM

ICE EM

ICE

EM

TV

EV

HEV

mild HEV

full HEV

A lot of operation modes to deals with the controldesign must include energy management

- Design and power ratio -



- Control and energy management -

BAT

ICE

VSI1 EM1

FuelParallel HEV Trans.

fast subsystemcontrols

EM1control

ICEcontrol

Transcontrol

Energy management(supervision/strategy)

driver request

slow systemsupervision



- How to study HEVs? -

• low energy consumption• low pollutant emissions• large drive range

An energetic modeling is required to take into account the different power flows

and the subsystems interactions

But more complex than for Thermal Vehicle or Electric Vehicle• multi-physical devices• more power flows• various interconnected subsystems

(for model-based control)

• adapted topology• balanced design• structure control

to achievetogether


«« 3. EMR and inversion3. EMR and inversion--based control ofbased control ofof energetic systems of energetic systems »»

Based on the works of“control team” of L2EP



Simulation for ever!Launching Matlab/Simulink is more and more a “Pavlov reflex”

realsystem

systemsimulation

behaviorstudy

?#@!&?

But:• Why simulation?• Which constraints and objectives?• Which level of accuracy?• How to be sure of the results?

- Typical procedure -



realsystem

systemmodel

assumptions

systemrepresentation

no

assumption

systemsimulation

assumptions

Intermediary steps are required for complex systems

Limitation to mainphenomena in function

of the objective

Organization of themodel to highlight

some properties

- From real system to simulation -



realsystem

systemmodel

assumptions


no

assumption

systemsimulation

assumptions

smoothing inductor

LLl RiidtdLv

(low frequencydynamical model)

ILVL

R+Ls1

(Simulink ©+Runge Kutta)

(bloc diagram +Laplace)

scopeStep

1

L.s+R

- Basic example -



realsystem

systemmodel


systemsimulation

• dynamic/ quasi-static/ static

• structural/functional• causal/non-causal

• backward/ forward

Different possibilities at each step in function of the objective

- Different catgories -



iHe1

Ms

vrame

Fres

Ftot

kbog

kbog

kbog

kbog

Fbog1

Fbog2

k11+1s

xCM1

B1

uHe

xCM1

xk2

1+2skmcc

iei

iee1

x

k21+2s

kmcc

iee2

uHe1

x

x

cHe1

x

x

x

x

uHe2

eee2

eee1

iHe

iHe2

k31+3s

ihach

k31+3s

ifiltrre ufiltrreVDC

[K]

cHi [K]

cHe2 [K]

EUREKA!

Fingers in the pockets!

But block diagrams:• can be confusing for complex systems• are limited to continuous and linear systems• do not highlight energy properties• do not highlight interaction between subsystems

Remember, See the wood before the trees!

- Limitation of classical block digrams -



Systemic approachStudy of subsystems and their interactionsHolistic property: associations of subsystem induce new global properties.

Cartesian approachThe study of subsystems is

sufficient to know the system behaviour.

For better performances of a systemInteractions and physical laws must be considered!

System = interconnected subsystems organized for a common goal

Cybernetic systemicblack box approach.

behaviour model

Cognitive systemicphysical laws

knowledge model

- Systemic & Cartesian approaches -



Interaction principleEach action induces a reaction

action

reaction

S2S1

Power exchanged by S1 and S2 = action x réaction

power

- Interaction principle -

Example

battery load

Vbat

Vbat

iload

loadbatteryVbat

iload

P=Vbat iload



If the interaction principle is not respected for 1 subsystem action

S2S1

Power = 0

- Interaction mistake -

iHe1

Ms

vrame

Fres

Ftot

kbog

kbog

kbog

kbog

Fbog1

Fbog2

k11+1s

xCM1

B1

uHe

xCM1

xk2

1+2skmcc

iei

iee1

x

k21+2s

kmcc

iee2

uHe1

x

x

cHe1

x

x

x

x

uHe2

eee2

eee1

iHe

iHe2

k31+3s

ihach

k31+3s


[K]

cHi [K]

cHe2 [K]

Error in the energy analysisfor the whole system

(reaction = 0)



area xdt

knowledge of past evolution

OK inreal-time

Principle of causalityphysical causality is integral input output

cause effect

t1

t

x

knowledge of future evolution

slopedtdx

?

impossible inreal-time

- Causality principle -



- Causality principle -

Example

vC

C iccc v

dtdCi

2

2

1cCc vE

delayno energy disruption

vCic

risk of damage

vC icddt

For energetic systemsphysical causality is VITAL



If the causality principle is not respected for 1 subsystem Voltage

ScapsChopper

- causality mistake -

iHe1

Ms

vrame

Fres

Ftot

kbog

kbog

kbog

kbog

Fbog1

Fbog2

k11+1s

xCM1

B1

uHe

xCM1

xk2

1+2skmcc

iei

iee1

x

k21+2s

kmcc

iee2

uHe1

x

x

cHe1

x

x

x

x

uHe2

eee2

eee1

iHe

iHe2

k31+3s

ihach

k31+3s


[K]

cHi [K]

cHe2 [K]

Risk of damage!No real-time management

BOUM!


«« 3a. Energetic Macroscopic Representation 3a. Energetic Macroscopic Representation »»

Based on the works of“control team” of L2EP



- The different elements -

An energetic system:

Energy sources

Energy storage elements

Energy conversion elements

Energy distribution elements

Key elements are:

• energy storage element

(delay, state variable, closed-loop control)

• energy distribution element

(power flow coupling, control with criteria)



Source oval pictogrambackground: light greencontour: dark green1 input vector (dim n)1 output vector (dim n)

- Energetic sources -

terminal elements which represent the environment of the studied system

generator and/or receptor of energy

power system

reaction

actionupstream

sourcedownstream

sourcex1

y1

x2

y2

p1= x1. y1 p2= x2. y2

direction ofpositive power(convention)

n

iii yx

1



i1

u13

u23

i2

gridVDC

iBat.

VDC

i

- Energetic sources: examples (1) -

structuraldescription

EMR(functionaldescription)

p=VDC i

Battery Electrical grid

p=u i

u

i

23

13uu

u

2

1ii

i

2 independent currents!

2 independent voltages!



pload

qwind

wind

qwind [m3/s]

Pload [Pa]

bulbI

u

I

uWind

(air flow source)generator energy

VDC

iBat

VDC

i

- Energetic sources: examples (2) -

Battery(voltage source)generator and

receptor of energy

Ligthing bulbreceptor of energy

IC engine(torque source)

generator of energy

Tice

ICE

Tice

Tice-ref



Accumulator rectangle with an oblique barbackground: orangecontour: redupstream I/O vectors (dim n)downstream I/O vectors (dim n)

- Accumulation elements -

internal accumulation ofenergy (with or without

losses)

reaction

actionx1

y

y

x2

p1= x1. y p2= x2. y

causality principle

output(s) = input(s)

dtxxfy ),( 21

y = output, delayed frominput changes

fixed I/O (causal description)



i1L, rL

u’13

u’23

i2u13

u23

)'(2112

31 uuiri

dtdL L

v1 v2

i

L, rL

21L vviridtdL

v1

v2

i

i

- Accumulation elements: examples (1) -

inductor 3-phase line

structuraldescription

EMR (causalrepresentation)

mathematicalModel

i

i

u

u’



inductorv1

v2

i

i

v1 v2

i

L

v

i2i1

Ccapacitor

i1

i2

v

v

inertia

J

T2T1

T1

T2

stiffness

k1 2

TT

1

2

T

T

2 21 iLE

2 21

JE

2 121 T

kE

2 21 vCE

- Accumulation elements: examples (2) -



conversionelement various pictograms

background: orangecontour: redupstream I/O vectors (dim n)downstream I/O vectors (dim p)Possible tuning input vector (dim q)

- Conversion elements -

conversion of energy without energy accumulation

(with or withoutlosses)

action /reaction x1

y1

y2

x2

p1= x1. y1 p2= x2. y2

),(),(

21

12zxfyzxfy

z

tuning vector

no delay!

upstream and downstreamI/O can be permuted

(floating I/O)



- Conversion element pictograms -

VDC

iconvuconv

iload

VDC uconv

iload

m

iconv

loadconv

DCconvimiVmu

m: modulation function of the convertercycleduty Dm

Circle = multiphysical conversion

Square = monophysical conversion



- Conversion elements: examples -

dcmdcmdcm euiridtdL

VDC uconv

iloadiconv

VDC

iconvuconv

s

iload

s

i

u DCM

gear

TgearT1

2

2

T3kgear

3gear2 TTdtdJ

keikT

dcm

dcmdcm

idcm

u idcm

edcm

Tdcm

k

gear

T12

Tgear

2

T3

2geargear

1geargear

kTkT

m

loadconv

DCconvimiVmu

Bat



Bat

- Coupling elements -

VDC

icoup

i1

i2

vcoup1 = VDC

vcoup2 = VDCVDC

icoup

i1

i2vcoup1

vcoup2

21coup

DC

iiicommonV

parallel connexion

distributionof energy

no tuningvector

couplingelements

overlapped pictogramsbackground: orangecontour: red

multiphysicalcoupling

Monophysicalcoupling



- Coupling elements: examples -

2T

TT gearrdifldif

iarm

uarmDCM

iexc

uexc

excdcm

armexcdcm

ikeiikT iarm

uarm

iexc

uexc

iarm

earm

Tdcm

eexc

iexc

Field winding DC machine

Mechanical differential

diff

Tgear

lwh

rwh

Tldiff

Trdiff

Tldiff

rwh

Trdiff

lwh

Tgear

diff2ΩΩΩ rwllwh

diff


«« 3b. Inversion3b. Inversion--based control based control »»

Based on the works of “control team” of L2EPIn collaboration with Prof. P. Sicard

(Univ. Quebec Trois Rivières, Canada)



Systemcause effect

- Principle of Inversion-based methodology -

desired effectControl

right cause

measurements?

control = inversion of the causal path

1. Which algorithm? (how many controllers)2. Which variables to measure?3. How to tune controllers?4. How to implement the control?

Inversion-based methodology

automatic controlindustrial electronics

input output

[Hautier 96]



- EMR and Inversion-based methodology -

desired effectright cause

measure?

SS1

causeeffect

inputoutputSS2 SSn

C1 C2 Cn

measure?measure?

EMR = system decomposition in basic energetic subsystems (SSs)

Remember,divide and conquer!

Inversion-based control: systematic inversion of each subsystems usingopen-loop or closed-loop control



?

Example:

- Inversion 1: single-input time-independant relationship -

output depends on a single inputwithout delay

Ku(t) y(t)

)( )( tuKty

yref(t)u (t)1/K

directinversion

)(1)( tyK

tu ref

1. no measurement2. no controller(open-loop control) Assumption: K well-know and constant

Example: Resistance

1/R

R

vt) i(t)

)( 1)( tvR

ti

directinversion

)( )( tiRtv refiref(t)v(t)



?

Example:

- Inversion 2: multiple-input time-independant relationship -

Output depends on several inputswithout delay

u1(t) y(t))()( )( 21 tututy

yref(t)u1 (t)

u2(t)+

+

1. measurement of the disturbance input2. no controller(open-loop control)

directinversion

)()()( 21 tutytu measref

+-

u1 is chosen to act on the output y

u2 becomes a disturbance input

Assumption: u2 well-know and can be measured



?

Example:

- Inversion 3: single-input causal relationship -

output depends on a single input and time (delay)

u(t) y(t)

dt )()( tuty

yref(t)u (t)

causality principle

directinversion

)()( tydtdtu ref

not possible in real-time

dt

1. measurement of output2. a controller is required(closed-loop control)

indirectinversion

)()()()( tytytCtu measref

closed loop controller

C(t)+-



- Example: PM-DC machine -

i

u

Lm rm

u i e

ireudtdiL mm

multi-input causal relationship

irudtdiL mm

decomposition

euu

U(s)

E(s)-

+ K1+s

U(s) I(s)

+

U (s)

+direct

inversion

Iref(s)Uref(s)C(s)

+-closed-loop



Manipulate u21 u1 is a disturbance

u1 y2

u2

Objective: to control y2

y2-refu1-meas

uHb= mHb VDC

iHb= mHb idcm

Ex : H-bridge chopper

mHb = uHb_ref / VDC_meas

uHb_ref⁄ ×

mHbVDC_meas

y1 u21

y2 = f(u1, u21 )

- Inversion of a conversion element -



y2

u2


y2-refu1-reg

u1

y1

Ex : pulley or roller

y2 = f(u1 )

1_ref = trans_ref / rtpull

Vtrans= rpull1

Ttrans = rtpull Fload

Vtrans_ref1_ref

1rtpull

- Inversion of a conversion element (2) -



Manipulate u1 u2 is a disturbance


u2

y2

y1

u1

y2-ref

u2-meas

y2-meas

u1-reg

y2=f(u1, u2 )

f is in integral form

Direct inversion isin derivative form

Approximate inversionby closed loop control

Ex : rotating shaft

loadTemTfdtdJ

+

ref

C(t)Tem_ref

meas

+-

Tload_meas

+

measloadTmeasreftCrefemT

_

))((_

- Inversion of an accumulation element -



u1m

u11

- Inversion of downstream coupling elements -

y2

u2

y2-ref

kD1…kD(m-p)

no measurement no controller

(m - p) distributionvariables

refDmm

refD

yku

yku

2'

1

2'

111

...

y11

u11

y1m

u1m

Example: chassis of a train

Fbog1

vtrain

Ftot

Fbog4

Fbog2

Fbog3

vtrain

vtrain

vtrain

vtrain

Ftot-ref

Fbog1

Fbog4

Fbog2

Fbog3

kD1 kD3kD2



2. Tuning path

SE SM

1. EMR of the system

Maximal Control Scheme :- maximum of sensors- maximum of operations

- Maximum control scheme -

3. Inversion step-by-step Strong assumption: all variables can be measured!



fusion4. Simplification of the control scheme

2. Tuning path

1. EMR of the system

3. Inversion step-by-step

5. Estimation of non-measured variables

6. Tuning of controllers

PID controllerCalculation of kP kI kD

3b. Strategy

- Practical control scheme -

SE SM

Simple tuning is possible by time coordination/separation of the control loops



DCM1

DCM2

PE1

PE2

Bat. Fres

EM1

EM2

- Energetic Macroscopic Representation (EMR) -

Bat. Env.

Specific pictograms for the analysis of power flows:• source of energy (green oval)• accumulation of energy (orange crossed rectangle)• conversion of energy (orange square or circle)• distribution of energy (overlapped pictograms)

Better understanding, efficient energy management



v

vref

- Inversion-based control -

Bat. Env.

strategy

Remember, Divide and conquer!


«« 4. Inversion4. Inversion--based control of an EV based control of an EV »»

T. Letrouvé, with the prepration of W. Lhommebased on the works of “control team” of L2EP



battery

itot

ESubat

- System Modeling using EMR -



battery

itot

ESubat


chopper

iamcha

ucha

achacha

batchacha

imiumu



battery

itot

ESubat

chopper

iamcha

ucha

ea

ia

DC machine

dtdiLireu a

aaaacha

without saturation of the DC machine

input output

cause effect




battery

itot

ESubat

chopper

iamcha

ucha

ea

ia

DC machine Trans- wheels

Tem

emema

aem

keikT




battery

itot

ESubat

chopper

iamcha

ucha

ea

ia


Tem

em

Trans- wheels

vev

evwh

redem

emwh

redtran

vRk

TRkF

neither the contact law nor curving road

Ftran




battery

itot

ESubat

chopper

iamcha

ucha

ea

ia


Tem

em

Trans- wheels chassis

vev

vev

Fres

restranev FFvdtdM

Ftran




Trans- wheels chassis environ.

Fres

ichaucha

iaem

Tem

dif

ubat

if uchf

Tred

wl

wrTdif

Tdif

vev

ichf

vev

Tem

em

vev

FresMS

itot

Ftrac

vev

)sin(MgF

vCSF

slope

evxfrontairdrag

221

slopeFFF dragres

Sfront

Ftran


battery

itot

ESubat

chopper

iamcha

ucha

ea

ia




battery

itot

ESubat

chopper

iamcha

ucha

ea

ia


Tem

em


vev

Ftran vev

FresMS




- Inversion-based Control -

Step 3a: By inversion of tuning path, obtain control path

vev_refia_refucha_ref Tem_ref Ftran_ref

mcha_ref

battery

itot

ESubat

chopper

iamcha

ucha

ea

ia


Tem

em


vev

Ftran vev

FresMS




Step 3b: Maximum Control Structure


mcha_ref

battery

itot

ESubat

chopper

iamcha

ucha

ea

ia


Tem

em


vev

Ftran vev

FresMS

restranev FFvdtdM

vev_refC(s)Ftran_ref

vev_mea

+-

Fres_mea

++

vevFtran

- Fres

+sM/1

vev






mcha_ref

battery

itot

ESubat

chopper

iamcha

ucha

ea

ia


Tem

em


vev

Ftran vev

FresMS

evwh

redem

emwh

redtran

vRk

TRkF

Tem_ref Ftran_ref

Tem Ftran

Ωem

wh

red

Rk

red

wh

kR

vev

wh

red

Rk




mcha_ref

battery

itot

ESubat

chopper

iamcha

ucha

ea

ia


Tem

em


vev

Ftran vev

FresMS



evema

em

keIakT

Ia_ref Tem_ref

Ia Tem

ea

k

Ωemk

k1






mcha_ref

battery

itot

ESubat

chopper

iamcha

ucha

ea

ia


Tem

em


vev

Ftran vev

FresMS

dtdiLireu a

aaaacha vev

Fres

SM

ia_refC(s)ucha_ref

ia_mea

+-

ea_mea

++

iaucha

-

ea

+)/1(

/1RsL

R

ia






mcha_ref

battery

itot

ESubat

chopper

iamcha

ucha

ea

ia


Tem

em


vev

Ftran vev

FresMS

achacha

batchacha

imiumu

vev

Ftran vev

Fres

MS ucha_ref

mcha

PWM

ubat




mcha_ref

battery

itot

ESubat

chopper

iamcha

ucha

ea

ia


Tem

em


vev

Ftran vev

FresMS






mcha_ref

battery

itot

ESubat

chopper

iamcha

ucha

ea

ia


Tem

em


vev

Ftran vev

FresMS


Step 4: Practical control structure - simplification




mcha_ref

battery

itot

ESubat

chopper

iamcha

ucha

ea

ia


Tem

em


vev

Ftran vev

FresMS


Step 4: Practical control structure - estimation

Tem

em vev

Ftran




Step 6: Control tuning – Step 7: implementation

Control Unit


mcha_ref

mcha

Tem

em vev

Ftran


«« 5b. Application to other5b. Application to otherinnovative systems innovative systems »»

Based on the works of“control team” of L2EP and on MEGEVH



• flexible power and control devices

• HIL simulation

« eV » platform: objective

study of anew vehicle

• EMR methodology• simulation (EMRlibrary)

Objective of the “electricity & vehicle” (eV) platform of the control team: real-time validation of energy management of new vehicle concepts for more efficient and less pollutant transportation systems

pre-validation onthe “eV” platform

validation ona real prototype

• real vehicle• IBC and EMSintegration

HIL simulation of the 3008 HY4 traction system (« ev » platform)

Simulation of the 3008 HY4 using EMR

Ex: PhD of T. Letrouvé (double parallel HEV of PSA)

validation of the control onthe 3008 HY4 prototype



Flexibility: “eV” is the open platform of MEGEVH for the real-time validationof various concepts of new vehicles

MaxwellScaps

dSPACE 1005

studied Vehicle(power components + HIL simulations)

BatscapScaps

NexaFC50 kW

converters

dSPACE 1103

10 kWconverters 2 kW MS+IM+DCM

2 kW MS+IM+DCM

10 kW PMSM+DCM

20 kW MSAP+IM

DP geartrain + machines

e-bike (Li-ion)

e-scooter (NiMH)

EV Tazzari Zero

PbBat

NiMHBat

Li-ionBat

« eV » platform: flexibility

electricalmachines

electricvehicle



Collaboration: a valuable platform for industrial and international collaborationswith the framework of EMR methodology

“eV” platfrom

FranceHIL of an EV

HIL of Scaps vehiclesHybrid ESS HIL of various HEV powertrain

Hybrid ESS

different controls of Scaps systems

(Canada)

HIL simulation of WECS (Denmark)

HIL of subway tractionsanti-slip control of VAL

EMS of NeoVAL (Scaps)HIL of a DPG Hybrid truck

HIL of a double parallel HEV

HIL of FC vehicles

HIL of theNanquing subway

(China)

(Spain)

(Argentina)

EMS ofEVs

« eV » platform: collaborations

HIL of a hybrid train



vref

control

simplifications

vref

rail

ES MS

power electronics DC machines mechanical power train environ.

EMR

[Verhille & al. 2007]

- Control of subway VAL 206 traction system -



vsub_refFtot1_ref

Fbog2_ref

kD

wh2_ref

shaft2_refTdcm2_ref

kD2kW2

wh1_ref

kD1kW1

Fbog1_ref

newstrategy

wh22_meswh21_meswh12_meswh11_mes

shaft1_mes

Tdcm1_ref

shaft2_mes

vsub_mes

shaft1_ref

New strategy:slip detection

Reduction of torqueof the slipping wheel

Increase of othertorques

- Anti-slip control of subway VAL 206-

experimental validationusing HIL simulation

[Verhille & al. 2007]

current (A)ifield2

ifield1



PV panelDC/DC DC/AC

supercapacitorbank

DC/DC DC/ACPMSM

hydraulicmachine

oiltank

air compressedaccumulators

(Switzerland)[Bossmann & al. 2007]

Master of T. Bossman, 2006

- EMR of a hybrid storage system -



PV panelDC/DC DC/AC

supercapacitorbank

DC/DC DC/ACPMSM

hydraulicmachine

oiltank

air compressedaccumulators

PV chopper 1

PVichop1

schop1

loadivsi1

svsi1

ucap

ucap

uvsi1

ioaduchop1

ipv

VSI 1 loaddc bus

schop2

uchop2 ucap

ifilt

Scapsifilt

uscaps ichop2

itot

itot

ucap

ucap

Scaps inductor chopper 2 ivsi2svsi2

ucap uvsi2

ism

ism

esm

Tsm

shaft

shaft

Thm

oil

qhm

pvalv

qoil

mvalvpatm

qoil

qoil

pair

VSI 2 PMSM hydraulicmachine

valveoil tank

air accumulator

(Switzerland)[Bossmann & al. 2007]

Master of T. Bossman, 2006




PV load

oil

Scaps

MEPTON/OFFucap-ref

MPPT

ON/OFF




Automatic subway VALsupplied by a DC rail

Supercapacitor storage systemwithout supply rail

• energy savings• cost reduction• safety operation• modern product

1 Sizing of on-boardenergy

3 Different topologies of

power electronics

2 Sizing of Supercaps

bank

Supercapacitor bank of L2EP

4 Simulation of the global

system using EMR

Matlab-Simulaink model of VAL 206 [Allègre & al. 2010]

- Subway NeoVAL using supercapacitor -



- Subway NeoVAL using supercapacitor -

computer

SC1 SC1

dSPACE

Interface

Fibreoptique

rectifier

Chopper 1

Grid

Smoothinginductor

SmoothinginductorSC2 SC2

Interface

inverter

Chopper 2

InductionMachine

Controlled DC machine

Chopper 3

MechanicalPowertrain

model

ESS in station on-board ESSemulated

traction system

track profile

1

1 Slow charge of SC1

2 Fast transfer to SC2

2

Next steps : Full-scale HIL simulationtest on a real vehicle

3

4

Traction operation

Energy recovery

3

4

0 10 20 30 40 50 60 70120

125

130

135

140

t(s)

Scps

volta

ge(V

)

experimental Usc2

90

MEGEVH

Barcelona 2013

Bat1

Bat1

Res Res Res

ResResRes

tank ICE

[Boulon & al. 2010]

- High-redundancy HEV -

traction current (A)

[Boulon & al. 2010]

uC2

ig2

ig1

uC1

uC1

Tice

Ttot

gen

gen

gen

Tg1Tg1-ref

double generator

ICE

Tg2-ref

Tice-ref Tg2

Bat1Vbat1 iL1

iL1 uh1mh1

ih1

itot1

uC1

uC1

uC2

Bat2Vbat2 iL2

iL2 uh2mh2

ih2uC2

uC2

iMT1

iMT2

battery sets

DC buses

connections

itot3

6

6

Ttot

gear

Tm1

mvsi1

Tm2

itot1

uC1

em1im1

uvsi1 im1

em2im2

uvsi2 im2

mvsi2

gear

gearitot2

uC2

vhev

Environ.Fres

6vhev

Brake

vhevFbk

Fbk-ref

vhev

FtractFtotTgear Fwh

wh vhev

double-machine drive wheel and brake chassis

91

MEGEVH

Barcelona 2013[Boulon & al. 2013]

uC2

ig2

ig1

uC1

uC1

Tice

Ttot

gen

gen

gen

Tg1Tg1-ref

double generator

ICE

Tg2-ref

Tice-ref Tg2

Bat1Vbat1 iL1

iL1 uh1mh1

ih1

itot1

uC1

uC1

uC2

Bat2Vbat2 iL2

iL2 uh2mh2

ih2uC2

uC2

iMT1

iMT2

battery sets

DC buses

connections

itot3

6

6

uC2-refitot3-refig2-meas

uh2-ref

iL2-ref ih2-ref

gen-ref Ttot-ref kD

Tg1-ref

Tg2-ref

Ttot

gear

Tm1

mvsi1

Tm2

itot1

uC1

em1im1

uvsi1 im1

em2im2

uvsi2 im2

mvsi2

gear

gearitot2

uC2

vhev

Environ.Fres

6vhev

Brake

vhevFbk

Fbk-ref

vhev

FtractFtotTgear Fwh

wh vhev

vhev-refFtot-ref

kD2

Twh1-ref

Tm2-ref

Ftract-refTm1-ref

kD4

Twh1-ref Fwh-ref

kD3im1-refuvsi1-ref

m1-ref

m2-ref

uC2-refitot3-refig2-meas

uh2-ref

iL2-ref ih2-ref

double-machine drive wheel and brake chassis

strategy

Strategy = coordination of subsystems

- High-redundancy HEV -

92

MEGEVH

Barcelona 2013

MEGEVH

Series–Parallel HEVs:• high efficiency for cars (e.g. Toyota Prius)• use of a single planetary geartrain (SPG)• use of 1 ICE and 2 Electric Machines (EMs)

new topology using a EVT• integration of EMs and SPG

no real comparison betweenEVT-based and SPG-based HEVs

EVT for Toyota Prius II?

Tem1

em1

Tem2

em2

- HEV using Electric Variable Transmission -

93

MEGEVH

Barcelona 2013

Tem2-ref

Fbk-ref

ivsi1

ubat vhev

Env.Fres

Brake vhev

Fbk

vhevvhev

FtotFwhTtot

em2

chassis

Tice

Tem1

ice

ice

ICE

Tice-ref Tem1

em1

em2

Tem2

idq1

idq1 edq1

vdq1uvsi1

iem1

d/s1

idq2

idq2 edq2

vdq2uvsi2

iem2

d/s2

Bat.

mvsi1

ubat

ubat

itot ivsi2

Tem1

em2wheels

EVT sub-systemICE shaft

ICE-ref

mvsi2

vhev-refFtot-ref

kD

Fwh-refTtot-refidq2-refvdq2-refvvsi2-ref

idq1-refvdq1-refvvsi1-ref Tem1-ref

TEM1-ref

Tem1-ref

strategy SOCest, driver request

id2-refid1-ref

d/s1

d/s2

EMR for the development of the control

Simulation of a drive cycle (EUDC)Comparison of the EVT-based HEV with Toyota Prius II:• EVT-based vehicle has more consumption• all operation modes and dynamics are possible• efficiency should be increased at high velocity• EVT has to be re-design in that objective

- HEV using Electric Variable Transmission -

[Cheng & al. 2011]


A graphical description could be a valuable stepto respect physics’ principle and to organize the control …

such as EMR

Energetic systems require new tools and more interactions

Interaction between Academics and Industry is a key issue…such as MEGEVH



Coimbra – PortugalUNESCO Wold Heritage

October 27-30, 2014

http://www.vppc2014.org

IEEE Vehicle Power and Propulsion Conference““Spreading ESpreading E--Mobility EverywhereMobility Everywhere””

Organization:

Supported by:

Digests deadline: 3131thth March 2014March 2014

Notification deadline: 1818thth May 2014May 2014

Final paper deadline: 0101thth July 2014 July 2014

Conference in a Carbon Care Philosophy

We will be pleased to Welcome you in We will be pleased to Welcome you in Coimbra!Coimbra!


ReferencesReferences



[Allègre 09] A. L. Allègre, A. Bouscayrol, P. Delarue, P. Barrade, E. Chattot, S. El Fassi, “Energy Storage System with supercapacitor for an innovative subway", IEEE transactions on Industrial Electronics, vol. 57, no. 12, pp. 4001-4012, December 2010 (common paper of L2EP Lille, EPF Lausanne and Siemens Transportation Systems).

[Boulon 10] L. Boulon, D. Hissel, A. Bouscayrol, O. Pape, M-C Péra, “Simulation model of a Military HEV with a Highly Redundant Architecture", IEEE transactions on Vehicular Technology, Vol. 59, no. 6, pp. 2654-2663, July 2010 (common paper of FEMTO-ST, L2EP Lille and Nexter Systems within MEGEVH, French network on HEVs)

[Boulon 13] L. Boulon, A. Bouscayrol, D. Hissel, O. Pape, M-C Péra, “Inversion-based control of a highly redundant military HEV", IEEE transactions on Vehicular Technology, vol. 62, no. 2, February 2013, pp. 500-5010 (common paper of IRH-Univ Québec Trois-Rivières, L2EP Lille, FEMTO-ST and Nexter within MEGEVH, French network on HEVs)

[Bouscayrol 03] A. Bouscayrol, "Formalismes de représentation et de commande des systèmes électromécaniques multimachines multiconvertisseurs", (text in French) HDR Univ. Lille1, déc. 2003.

[Chan 07] C.C. Chan: "The state of the art of electric, hybrid, and fuel cell vehicles“, Proc. of the IEEE, April 2007, Vol. 95, No.4, pp. 704 - 718.

[Chan 10] C. C. Chan, A. Bouscayrol, K. Chen, “Electric, Hybrid and Fuel Cell Vehicles: Architectures and Modeling", IEEE transactions on Vehicular Technology, vol. 59, no. 2, February 2010, pp. 589-598 (common paper of L2EP and Honk-Kong Univ.).

[Chen 08] K. Chen, A. Bouscayrol, A. Berthon, P. Delarue, D. Hissel, R. Trigui, “Global modeling of different vehicles, using EMR to focus on system functions and system energy properties”, IEEE Vehicular Technology Mag., vol. 4, no. 2, June 2009, pp. 80-89 (common paper L2EP, FEMTO and INRETS within MEGEVH network)

[Cheng 11] Y. Cheng, R. Trigui, C. Espanet, A. Bouscayrol, S. Cui, " Analysis of Technical Requirements from the Toyota Prius II for the Design of a PM-EVT”, IEEE transactions on Vehicular Technology, vol. 60, no. 6, pp. 4106-4114, November 2011 (common paper L2EP Lille, LTE-INRETS, FEMTO-ST and Harbin Institute of Technology, within MEGEVH,)

- References (1) -



[Emadi 05] A. Emadi, K. Rajashekara, S. S. Willaimson, S.M. Lukic, “Topological overview of Hybrid Electric and Fuel Cell vehicula power systems architectures and configurations”, IEEE Trans. on Vehicular Technology, May 2005, Vol. 54, No. 3, pp. 763-770.

[Eshani 05] M. Eshani, Y. Gao, S. E. Gay, A. Emadi, "Modern electric, hybrid electric and fuel cell vehicles", CRCPress, New York, 2005.

[Lhomme 08] W. Lhomme, R. Trigui, P. Delarue, B. Jeanneret, A. Bouscayrol, F. Badin, "Switched causal modeling of transmission with clutch in hybrid electric vehicles”, IEEE Trans. on Vehicular Technology, Vol. 57, no. 4, July 2008, pp. 2081-2088, (common paper L2EP, LTE-INRETS in the framework of MEGEVHnetwork)

[Letrouvé 13] T. Letrouvé, W. Lhomme, A. Bouscayrol, N. Dollinger, “Control validation of Peugeot 3∞8 Hybrid4 vehicle using a reduced-scale power HIL simulation", Journal of Electrical Engineering and Technology, September 2013, vol. 8, no. 5, pp. 1227-1233 (common paper of L2EP Lille, and PSA Peugeot Citroën within MEGEVH)

[Salmasi 07] F. R. Salmasi, "Control strategies for Hybrid Electric Vehicles: evolution, classification, comparison and future trends", IEEE Trans. on Vehicular Technology, September 2007, Vol. 56, No. 3, pp. 2393-2404.

[Syed 12] S. A. Syed, "Energetic Macroscopic Representation and Multi-level energy management fro heavy duty hybrid vehicles suing double planetary geartrain", PhD report, University Lille1, June 2012.

[Verhille 07] J. N. Verhille, A. Bouscayrol, P. J. Barre, J. P. Hautier, “Validation of anti-slip control for a subway traction system using Hardware-In-the-Loop simulation”, IEEE-VPPC’07, Arlington (USA), September 2007 (common paper L2EP Lille and Siemens Transportation System).

- References (2) -

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