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EMR’16

UdS - Longueil

June 2016

Summer School EMR’16

“Energetic Macroscopic Representation”

« ENERGETIC MACROSCOPIC

REPRESENTATION (EMR) »

Prof. Alain BOUSCAYROL1, Prof. João P. Trovão1, 1 L2EP, Université Lille1, MEGEVH network, France

2 e-TESC, Université de Sherbrooke, Canada

Within the DL program of IEEE-VTS

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« Energetic Macroscopic Representation (EMR) »

EMR’16, UdS Longueil, June 2016

- Outline -

1. EMR basic elements

• Source, accumulation and conversion elements

• Coupling and adaptation elements

2. EMR of a complete system

• Action and tuning path

• Association rules

3. Conclusion: towards control organization

2

3



real

system

system

model

system

representation

- Level of study -

system

simulation

model

objective

limited

validity range

organization

valuable

properties

behavior

study

prediction

4



real

system

representation

- Representation I/O -

Model objective:

“control”

causal &

systemic

organization

Highlight energetic

and systems properties

prediction

dynamical

models

Real-time

control &

Energy

management

forward

approach

3

EMR’16

UdS - Longueil

June 2016



1. « EMR basic elements »

6



- The different elements -

Energy sources

Energy storage

Energy conversion

Energy distribution

Only 4 energy functions

are required to describe

energy conversion systems

EMR = 4 graphical elements associated with the 4 energy functions

4

7



Sourceoval pictogram

background: light green

contour: dark green

1 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

action

upstream

source

downstream

sourcex1

y1

x2

y2

p1= x1. y1 p2= x2. y2

direction of

positive power

(convention)

n

i

ii yx

1

11

8



pload

qwind

wind

qwind [m3/s]

Pload [Pa]

bulbI

u

I

u

Wind

(air flow source)

generator energy

VDC

iBat

VDC

i

- Energetic sources: examples -

Battery

(voltage source)

generator and

receptor of energy

Ligthing bulb

receptor of energy

IC engine

(torque source)

generator

of energyTice

WICE

Tice

W

Tice-ref

5

9



Accumulatorrectangle with an oblique bar

background: orange

contour: red

upstream I/O vectors (dim n)

downstream I/O vectors (dim n)

- Accumulation elements -

internal accumulation of

energy (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 with

regard to input changes

fixed I/O (causal description)

10



inductor

v1

v2

i

i

v1 v2

i

L

v

i2i1

C

capacitor

i1

i2

v

v

inertia

WJ

T2T1

W

T1

T2

W

W

stiffness

kW1 W2

TT

W1

W2

T

T

2 2

1iLE

2 2

1W JE

2 1

2

1T

kE

2 2

1vCE

- Accumulation elements: examples -

6

11



conversion

elementvarious pictograms

background: orange

contour: red

upstream 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 without

losses)

action /

reaction x1

y1

y2

x2

p1= x1. y1 p2= x2. y2

),(

),(

21

12

zxfy

zxfy

z

tuning vector

no delay!

upstream and downstream

I/O can be permuted

(floating I/O)

12



kF

- Conversion elements: examples -

dcmdcmdcm euiridt

dL

VDCuconv

iloadiconv

VDC

iconv

uconv

s

iload

s

i

u DCM

Wgear

TgearT1

W2

W2

T3

kgear

3gear2 TTdt

dJ W

WF

F

ke

ikT

dcm

dcmdcm

idcm

u idcm

edcm

Tdcm

W

Wgear

T1

W2

Tgear

W2

T3

2geargear

1geargear

k

TkT

WW

m

loadconv

DCconv

imi

Vmu

Bat

7

13



coupling element

various overlapped pictograms

background: orange

contour: red

pairs of I/O vectors

N pairs, N-1 pictograms

- coupling elements -

distribution of energy

without energy

accumulation

without tuning

(with or without

losses)

action /

reaction x1

y1

p1= x1. y1

)x,..x(fy

...

)x,..x(fy

nnn

n

1

111no delay!

x2

xn

yn

y2

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- Coupling elements: examples -

2

TTT

gearrdifldif

iarm

uarmDCM

iexc

uexc

Wexcdcm

armexcdcm

ike

iikT iarm

uarm

iexc

uexc

iarm

earm

Tdcm

W

eexc

iexc

Field winding DC machine

Mechanical differential

Wdiff

Tgear

Wlwh

Wrwh

Tldiff

Trdiff

Tldiff

Wrwh

Trdiff

Wlwh

Tgear

Wdiff2

ΩΩΩ rwhlwh

diff

8

15



- EMR main properties -

Energy

source

Energy

accumulation

Energy

conversion

(potential tuning)

Energy

distribution

highlight energetic functions

all power I/O are defined

by accumulation elements

(causality)

only conversion elements

can have tuning inputs

all elements are connected

by action/ reaction (power link)

(systemic)

valuable for control design

EMR’16

UdS - Longueil

June 2016



3. « EMR of a complete system »

Prof. Alain BOUSCAYROL, Dr. Walter LHOMME

(University Lille1, L2EP)

9

17



OKy2

x2

- Association rules: direct connection -

x2

y2y1

x1 x3

y3

direct connection if:

Out(S1) = In (S2)

In(S1) = Out(S2)

S1 and S2 any sub-systems

Bat

VDC

iL

u

VDC VDC

iLiL

iL

u

L iL

VDC

iL

iL

u

uVidt

dL DCL

i state variable

Example

Bat

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x1

x1y2

y2

x1

y1 x1

y3

- Association rules: merging rule -

NO

2 accumulation elements

would impose the same

state variable x1

Conflict of association

merging

x1

y3x1

y1

1 equivalent function for

2 elements / systemic

10

19



x2

y2y1

x1 x3

y3

x’2

y’2y1

x1 x3

y3

- Association rules: permutation rule -

permutation possible if same global behavior:

strictly the same effects (y1 and x3) from the same causes (x1 , y3 and z)

z zx2

y2y1

x1 x3

y3

z

20



Assumptions:

J1 , J2 constant

no backslash

- Interest of rules -

W1W1

T2

T3

W2

T1

T4

W2

J1

J2

W1

W1

T2

T1

J1 W2

T3 W2

W2

T4

T3

J2

to solve conflict

of association

k

J1/k2

W1

T’2T1 W2

T3W2 W2

W2

T4

T3

J2

permutation

k

22

1eq J

k

JJ

W1

T’2T1

W2

T4W2

Jeqmerging

k

11

21



- Example: a lift -

Assumptions:

- ideal switches

- DC Machine not saturated

W

TmLs, rs

uch

um

ich

VDC

im

shaft pulleyDCMchoppersupply

counter

weight

cage

Tpul

vcage

filter

uc

iLLf, rf

CW

inductor

Technical requirement:

- control of velocity vcage

- tuning input = modulation ratio of chopper m

22



- Lift example: EMR -

W

TmLs, rs

uch

um

ich

VDC

im

inductor shaft pulleyDCMchoppersupply

counter

weight

cage

Tpul

vcage

filter

uc

iLLf, rf

CW

mmerging

permutation

and merging

VDC iL

iL uC

Bat Env

ich

uC

im

uch

em

im

W

Tm Fpul

vcage

vcage

Fres

filter chopper DC machine pulley cage+CW

12

23



up down

- Lift example: tuning path -

W

TmLs, rs

uch

um

ich

VDC

im

inductor shaft pulleyDCMchoppersupply

counter

weight

cage

Tpul

vcage

filter

uc

iLLf, rf

CW

tuning path

VDC iL

iL uC

Bat Env

ich

uC

im

uch

em

im

W

Tm Fpul

vcage

vcage

Fres

filter chopper DC machine pulley cage+CW

m

24



x1

x1y2

y2

- EMR and systemic -

I/O are independent

of power flows

Tuning paths:

• defined by the technical requirements

• independent of the power flow direction

EMR describes energetic

functions

EMR is adapted for control design

x1

y1 x1

y3

x1

y2 x1

y3

Priority to the function

by keeping the physical causality

(systemic)

13

EMR’16

UdS - Longueil

June 2016


“Energetic Macroscopic Representation”« Conclusion »

EMR = multi-physical graphical description

based on the interaction principle (systemic)

and the causality principle (energy)

Basic elements = energetic function

sources, accumulation, conversion and distribution of energy

Association rules = holistic property of systemic

enable keeping physical causality in conflict of association

Applications

analysis, simulation, control organisation…

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- Speaker & contributors -

Prof. Alain BOUSCAYROL

University Lille 1, L2EP, MEGEVH, France

Coordinator of MEGEVH, French network on HEVs

PhD in Electrical Engineering at University of Toulouse (1995)

Research topics: EMR, HIL simulation, tractions systems, EVs and HEVs

… and other colleagues from the control team of L2EP Lille

Prof. João P. Trovão

Université de Sherbrooke, e-TESC Lab., Qc, Canada

PhD in Electrical Engineering at University of Coimbra (2012)

Research topics: Electric Vehicles, Multiple Energy Storage, Energy Management

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27



- Some references -

A. Bouscayrol, & al. "Multimachine Multiconverter System: application for electromechanical drives", European

Physics Journal - Applied Physics, vol. 10, no. 2, May 2000, pp. 131-147 (common paper GREEN Nancy, L2EP Lille

and LEEI Toulouse, according to the SMM project of the GDR-SDSE).

A. Bouscayrol, "Formalism of modelling and control of multimachine multiconverter electromechanical systems” (Texte

in French), HDR report, University Lille1, Sciences & technologies, December 2003

A. Bouscayrol, J. P. Hautier, B. Lemaire-Semail, "Graphic Formalisms for the Control of Multi-Physical

Energetic Systems", Systemic Design Methodologies for Electrical Energy, tome 1, Analysis, Synthesis and

Management, Chapter 3, ISTE Willey editions, October 2012, ISBN: 9781848213883

K. Chen, A. Bouscayrol, W. Lhomme, "Energetic Macroscopic Representation and Inversion-based control: Application

to an Electric Vehicle with an electrical differential”, Journal of Asian Electric Vehicles, Vol. 6, no.1, June issue, 2008,

pp. 1097-1102.

P. Delarue, A. Bouscayrol, A. Tounzi, X. Guillaud, G. Lancigu, “Modelling, control and simulation of an overall wind

energy conversion system”, Renewable Energy, July 2003, vol. 28, no. 8, p. 1159-1324 (common paper L2EP Lille and

Jeumont SA).

J. P. Hautier, P. J. Barre, "The causal ordering graph - A tool for modelling and control law synthesis", Studies in

Informatics and Control Journal, vol. 13, no. 4, December 2004, pp. 265-283.

W. Lhomme, “Energy management of hybrid electric vehicles based on energetic macroscopic representation”, PhD

Dissertation, University of Lille (text in French), November 2007 (common work of L2EP Lille and LTE-INRETS

according to MEGEVH network).

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