« energetic macroscopic representation (emr) · two pictograms background: orange contour: red...
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EMR’12
Madrid
June 2012
Joint Summer School EMR’12
“Energetic Macroscopic Representation”
« Energetic Macroscopic
Representation (EMR) »
Prof. Daniel HISSEL
FEMTO-ST Energy Department, FCLAB, University of Franche-Comte,
Dr. Walter LHOMME, Prof. Alain BOUSCAYROL
L2EP, University of Lille 1,
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EMR’12, Madrid, June 2012 2
« Energetic Macroscopic Representation »
- Outline -
1. EMR basic elements
• General principles
• Source, accumulation and conversion elements
• Coupling elements
2. EMR of a whole system
• Action and tuning path
• Association rules
• Example
3. Conclusion
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Joint Summer School EMR’12
“Energetic Macroscopic Representation”
EMR’12
Madrid
June 2012
« EMR basic elements »
![Page 4: « Energetic Macroscopic Representation (EMR) · two pictograms background: orange contour: red upstream I/O vectors (dim n) downstream I/O vectors (dim p) Possible tuning input vector](https://reader033.vdocuments.us/reader033/viewer/2022051908/5ffb9fc24aade97c9c183ec8/html5/thumbnails/4.jpg)
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
Interaction principle
Each action induces a reaction
action
reaction
S2 S1
Power exchanged between S1and S2 = action x reaction
power
- Interaction principle -
Example
battery load
Vbat Vbat
iload
load battery Vbat
iload
P=Vbat iload
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
area xdt
knowledge
of past evolution
OK in
real-time
Principle of causality
physical causality is integral input output
cause effect
t1
t
x
knowledge
of future evolution
slope
dt
dx
?
impossible in
real-time
- Causality principle -
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
- State variable (1/3) -
Energy is the integrative of power
t
dPowertEnergie0
).()(
System
Power 1 Power 2
Energy
A system can store energy : in this case power1(t) ≠ power2(t)
Energy cannot vary instantaneously !
)()( tEnergydt
dtPower we would obtain Power(t) In fact, as :
Physically impossible
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
- State variable (2/3) -
Energy varies thus « slowly », according to the charge and the discharge of the
energy storage devices Examples :
- filling the tank of a car
- energy storage in the capacitors
- energy storage in flywheels
- thermal energy storage in a heater
- compressed air storage
- …
Variable linked to the stored energy Example :
- energy stored in a capacitor (value C) :
variable linked to energy = voltage V
the voltage V of a capacitor cannot vary instantaneously
2.2
1VCE
v
i2 i1
C
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
- State variable (3/3) -
State variable = Energy linked variable
Variable representing the stored energy Example :
- energy stored in a flywheel (moment of inertia J) :
variable linked to the energy = angular speed Ω
angular speed Ω of a flywheel cannot vary instantaneously
angular speed directly represents the energetic state of the flywheel system
2.2
1 JE
J
C2
C1
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
- Different elements -
An energetic system:
Energy sources
Energy storage elements
Energy conversion elements
Energy distribution elements
Energy storage element versus energy conversion element:
Different in view of control – state variable control
The state of a energy storage element can not change
instantaneously
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
Source oval pictogram
background: light green
contour: dark green
1 output vector (dim n)
1 input 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
source x1
y1
x2
y2
p1= x1. y1 p2= x2. y2
direction of
positive power
(convention)
n
i
ii yx
1
11
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
i1
u13
u23
i2
grid
VDC
i Bat.
VDC
i
- Energetic sources: examples (1) -
structural
description
EMR
(functional
description)
p=VDC i
Battery Electrical grid
p=u i
u
i
23
13
u
uu
2
1
i
ii
2 independent currents
2 independent voltages
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
pload
qwind
wind
qwind [m3/s]
Pload [Pa]
bulb I
u
I
u
Wind
(air flow source)
Energy generator
VDC
i Bat
VDC
i
- Energetic sources: examples (2) -
Battery
(voltage source)
generator and
receptor of energy
Ligthing bulb
Energy receptor
IC engine
(torque source)
Energy generator Tice
ICE
Tice
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
Accumulator rectangle 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
action x1
y
y
x2
p1= x1. y p2= x2. y
causality principle
output(s) = input(s)
dtxxfy ),( 21
y = output, delayed from
input changes
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
i1 L, rL
u’13
u’23
i2 u13
u23
)'(21
12
3
1uuiri
dt
dL L
v1 v2
i
L, rL
2 1 L v v i r i dt
d L
v1
v2
i
i
- Accumulation elements: examples (1) -
inductor 3-phase line
structural
description
EMR (causal
representation)
mathematical
Model
i
i
u
u’
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
inductor
v1
v2
i
i
v1 v2
i
L
v
i2 i1
C
capacitor
i1
i2
v
v
inertia
J
T2 T1
T1
T2
stiffness
k 1 2
T T
1
2
T
T
2 2
1iLE
2 2
1 JE
2 1
2
1T
kE
2 2
1vCE
- Accumulation elements: examples (2) -
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
conversion
element two 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 -
action /
reaction x1
y1
y2
x2
p1= x1. y1 p2= x2. y2
z
tuning vector
conversion of energy
without energy
accumulation
(with or without
losses)
),(
),(
21
12
zxfy
zxfyno delay!
upstream and downstream
I/O can be permuted
(floating I/O)
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
- Conversion element pictograms -
Circle = electromechanical conversion
Square = electrical conversion
Triangle = mechanical conversion
Circle = multiphysical conversion
Square = monophysical conversion
For multiphysical systems
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
- Conversion element pictograms -
VDC
iconv
uconv
iload
VDC uconv
iload
m
iconv
loadconv
DCconv
imi
Vmu
m: modulation function of the converter
cycleduty Dm
Circle = multiphysical conversion
Square = monophysical conversion
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
- Conversion elements: examples -
dcmdcmdcm euiridt
dL
VDC uconv
iload iconv
VDC
iconv
uconv
s
iload
s
i
u DCM
gear
Tgear T1
2
2
T3
kgear
3gear2 TTdt
dJ
ke
ikT
dcm
dcmdcm
idcm
u idcm
edcm
Tdcm
k
gear
T1
2
Tgear
2
T3
2geargear
1geargear
k
TkT
m
loadconv
DCconv
imi
Vmu
Bat
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
- Coupling elements -
coupling
elements
overlapped pictograms
background: orange
contour: red
electro
mechanical
coupling
electrical
coupling
mechanical
coupling
distribution
of energy
no tuning
vector
coupling
elements
overlapped pictograms
background: orange
contour: red
multiphysical
coupling
Monophysical
coupling
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
Bat
- Coupling elements -
VDC
icoup
i1
i2
vcoup1 = VDC
vcoup2 = VDC VDC
icoup
i1
i2
vcoup1
vcoup2
21coup
DC
iii
commonV
parallel connexion
distribution
of energy
no tuning
vector
coupling
elements
overlapped pictograms
background: orange
contour: red
multiphysical
coupling
Monophysical
coupling
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
- Coupling elements: examples -
2
TTT
gearrdifldif
iarm
uarm DCM
iexc
uexc
excdcm
armexcdcm
ike
iikT 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
diff 2
ΩΩΩ rwllwh
diff
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
« EMR of a whole system »
EMR’11
Lausanne
July 2011
Joint Summer School EMR’11
“Energetic Macroscopic Representation”
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
- Example of an electromechanical conversion system -
PE Bat. Fres EM
S1 S2
upstream
source
downtream
source
x1 x2 x3 x4 x5 x6 x7
y1 y2 y3 y4 y5 y6 y7
z23 z45 z67
upstream
source
downtream
source
Convention: direction of positive power flow
(could be negative for bidirectional system)
electromechanical
conversion electrical
conversion mechanical
conversion
energy storage =
power adaptation
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
- Tuning path -
PE Bat. Fres EM
S1 S2
upstream
source
downtream
source
x1 x2 x3 x4 x5 x6 x7
y1 y2 y3 y4 y5 y6 y7
z23 z45 z67
Tuning path:
Technical requirements: action on z23 and x7 to be controlled
x3 x4 x5 x6 x7
z23
The tuning path is independent
of the power flow direction
(e.g. velocity control in acceleration AND regenerative braking)
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
OK y2
x2
- Association rules: direct connection -
x2
y2 y1
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
iL iL
iL
u
L iL
VDC
iL
iL
u
Bat
uVidt
dL DCL
i state variable
Example
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
x1
x1 y2
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
y3 x1
y1
1 equivalent function for
2 elements / systemic
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
- Merging rule: example -
DC machine and
smoothing inductor
u
i
u2
Lf rf
u i u2
iruudt
diL f2f
Lm rm
u2 i e
ireudt
diL m2m
i
u2
u
i e i
u2 i
i)rr(eudt
di)LL( mfmf
Leq+Lm rf +rm
u i e
i
e
u
i
Assumption: Lf, Lm constant
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
x2
y2 y1
x1 x3
y3
x’2
y’2 y1
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 z x2
y2 y1
x1 x3
y3
z
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
1
1
T2
T1 2
T3
- Permutation rule: example -
Shaft + gearbox
1
T2 T1
1
2
T3
J
211 TTdt
dJ
12
32
k
TkT
1
T’2 T1 2
T3 2
J/k2 3222
' TTdt
d
k
J
21
32
1
1
k
Tk
T
variable
change
no assumption
strict equivalence
(same model)
‘ 1
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
Assumptions:
J1 , J2 constant
no backslash
- Interest of rules -
1 1
T2
T3
2
T1
T4
2 J1
J2
1
1
T2
T1 J1 2
T3 2
2
T4
T3 J2
to solve conflict
of association
k
J1/k2
1
T’2 T1 2
T3 2 2
2
T4
T3 J2
permutation
k
22
1eq J
k
JJ
1
T’2 T1
2
T4 2
Jeq merging
k
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
- Lift example: EMR -
Tm Ls, rs
uch
um
ich
VDC
im
inductor shaft pulley DCM chopper supply
counter
weight
cage
Tpul
vcage
filter
uc
iL Lf, rf
C
VDC iL
iL uC
Bat Env
m
ich
uC
im
uch
em
im
Tm Fpul
vcage
vcage
Fres
filter chopper DC machine pulley cage+CW
merging permutation
and merging
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« Energetic Macroscopic Representation »
EMR’12, Madrid, June 2012
up down
- Lift example: tuning path -
Tm Ls, rs
uch
um
ich
VDC
im
inductor shaft pulley DCM chopper supply
counter
weight
cage
Tpul
vcage
filter
uc
iL Lf, rf
C
VDC iL
iL uC
Bat Env
m
ich
uC
im
uch
em
im
Tm Fpul
vcage
vcage
Fres
filter chopper DC machine pulley cage+CW
tuning path
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Joint Summer School EMR’12
“Energetic Macroscopic Representation”
EMR’12
Madrid
June 2012
« Conclusion »
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EMR’12, Madrid, June 2012 43
« Energetic Macroscopic Representation »
x1
x1 y2
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)
EMR respects natural
integral causality
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EMR’12, Madrid, June 2012 44
« Energetic Macroscopic Representation »
« BIOGRAPHIES AND REFERENCES »
EMR’11
Lausanne
July 2011
Joint Summer School EMR’11
“Energetic Macroscopic Representation”
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EMR’12, Madrid, June 2012 45
« Energetic Macroscopic Representation »
- Authors -
Prof. Daniel HISSEL
University of Franche-Comte, FEMTO-ST, FCLAB, MEGEVH,
France
Director, FCLAB CNRS Research Federation
Head, Hybrid & Fuel Cell Systems Research Team, FEMTO-ST
PhD in Electrical Engineering at University of Toulouse (1998)
Research topics: Modeling, control, diagnosis of hybrid and fuel
cell systems
Prof. Alain BOUSCAYROL
University of Lille 1, L2EP, MEGEVH, France
PhD in Electrical Engineering at University of Toulouse (1995)
Research topics: EMR, EVs and HEVs
Dr. Walter LHOMME
L2EP, University Lille1, MEGEVH, France
PhD on Hybrid Electric Vehicles at University Lille1, 2007
Engineer at AVL (UK) (2007-2008) Associate Prof. at Univ. Lille1 (2008)
Research topics: EMR, EVs and HEVs
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EMR’12, Madrid, June 2012 46
« Energetic Macroscopic Representation »
- 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, M. Pietrzak-David, P. Delarue, R. Peña-Eguiluz, P. E. Vidal, X. Kestelyn, “Weighted control of traction
drives with parallel-connected AC machines”, IEEE Transactions on Industrial Electronics, December 2006, vol. 53, no.
6, p. 1799-1806 (common paper of L2EP Lille and LEEI Toulouse).
Boulon, L., Hissel, D., Bouscayrol, A., Pape, O., Péra, M.C., “Simulation model of a military HEV with a highly
redundant architecture”, IEEE Transactions on Vehicular Technology, vol. 59, n°6, pp. 2654-2663, 2010.
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).
Boulon, L., Hissel, D., Bouscayrol, A., Péra, M.C., “From Modeling to Control of a PEM Fuel Cell Using Energetic
Macroscopic Representation”, IEEE Transactions on Industrial Electronics, vol. 57, n°6, pp. 1882-1891, 2010.
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).