« system, causality and energy · energetic macroscopic representation » ... the system exists to...

Graz University of Technology(Austria)April 2012

« Energy Management of complex systemsEnergetic Macroscopic Representation »

«« SSYSTEM, YSTEM, CCAUSALITY AND AUSALITY AND EENERGY NERGY »»

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

Prof. C. C. Chan(University of Hong-Kong, China)

based on the Keynote at EMR’09

2

«« System, Causality and Energy System, Causality and Energy »»

EMR, Graz University of Technology, April 2012

Prof. Alain BOUSCAYROLUniversity of Lille 1, L2EP, FranceCoordinator of MEGEVH, French network on HEVsGeneral Chair of IEEE-VPPC 2010, Lille France

Prof. C.C. ChanTne University of Hong-Kong, ChinaFellow, Royal Academy of Engineering, U.K.Academician, Chinese Academy of EngineeringPresident, Electric Vehicle Association of Asia PacificHonorary Professor, University of Hong Kong

- Speaker and contributor -

3



- Outline -

1. Model, Representation and simulation• Different models• Different representations• Different simulation approaches

2. Energy and Systems• Systemic approach• Energetic Approach

3. Graphical description for engineering• Different graphical descriptions• Model, description and control

4. Energy management of EVs and HEVs

4



core of thelecture

- Philosophy engineering -

Debate, define, revise and pursue the purpose/objectiveThe system exists to deliver capability, the end justifies the means. The statement of a requirement must define how it is to be tested.Requirements reflect the constraints of technology & budgets.

Think “systemic”The whole is more than the sum of the parts –and each part is more than a fraction of the whole

Be creativeSee the wood before the trees

Follow a disciplined procedureDivide and conquer, combine and rule

Take account of the peopleTo err is human ; Ergonomics; Ethics & Trust

Manage the project and the relationshipsAll for one, one for all

Six Principles of Integrated System DesignSix Principles of Integrated System Design

5



Systems for energy conversion

devices in dynamic interactions, organised to achieve a goal

Energy nodeskey of management

• interaction principle:action and reaction

• holistic principle:properties induced by associations

• multi-finality principle:several solutions to achieve the objective

• subjectivity principle:study depending of the user

• no energy disruption• causality principle

physical causality is integral

Physics

Systemic

- Energy and Systems: basic requirements -



1. 1. «« Model, representation Model, representation andand simulation simulation »»

7



Model = description of the system behaviour(validity range function of assumptions)

Representation = organisation of a modelin order to highlight some properties

vCic

vCic

Example

vC

C ic

cc iC

vdtd 1

cc vdtdCi

mathematical model

state space representation

transfer functionCs)s(I

)s(V

c

c 1

bloc diagram

COG

Cs1

- Model and representation -

8



realsystem

systemmodel

assumptions

systemrepresentation

no

assumption

- Simulation of a system (1) -

systemsimulation

modelobjective

limitedvalidity range

organization

valuableproperties

behaviorstudy

prediction

assumptions

9



realsystem

systemmodel

assumptions

systemrepresentation

no

assumption

systemsimulation

assumptions

Intermediary steps are required for complex systems

- Simulation of a system (2) -

Classical way (e.g. Matlab-Simulink©)

10



different kinds of objectives

different kinds of modelling

Objectives:• component design/optimization• component control• system analysis (efficiency…)• energy management of the system• ….

“Modelling”:• structural/functional description• static/dynamic models• causal/ acausal representations• backward/forward simulation• ...

Which model?

[Chan 2010]

- Model objectives -

11



How to describe a system?

Structural description• Physical structure in priority• Physical links between subsystems• Design application

Functional description • function priority• Virtual links between subsystems• Analysis and control application

Example

2i m1i1v m2v

Mathematic modelAssumption: Ideal transformer

3D Finite Element Model

- Structural vs. functional descriptions -

12



- Dedicated software -

two DC machine system

PSIM (structural) Matlab-Simulink (functionnal)

machines connected bya unique link (shaft)

machines connected bytwo links (torque/speed)

13



Which model subsystem?

Static model• steady state operations• no transient states• fast computation time• global behavior

Dynamic model • transient state operations• but also steady state operations• long computation time• detailed behavior

- Static vs. dynamic models -

Quasi-static model• static model + main time constant• intermediary computation time• intermediary behavior

14



0 1000 2000 3000 4000 5000 6000-150

-100

-50

0

50

100

150

Speed in rpm

Torq

ue in

Nm 8885

8060

30

88

85

8580

60

30

DC

tDC U

Pi ),(

VSd RSiSd dSd

dt SSq

VSq RSiSq dSq

dt SSd

0 RRiRd d Rd

dt RRq

0 RRiRq d Rq

dt RRd

)( SdRqSqRdR

mem ii

LLpT

- Example of electrical machine -

static efficiency map dynamic model

fTTdtdJ loadem

quasi-static model

fTTdtdJ loadem

0 100020003000400050006000-150-100-50050100150

88858060

30

8885

858060

30

+

15



How to connect subsystem?

Causal description• fixed input and output• output = integral function of inputs• difficult interconnection subsystems• basic solver

Non-causal (acausal)description • non-fixed inputs and outputs• different relationships• easy subsystem interconnection• specific solver required• simulation library

- Causal vs. non-causal representation -

21 TTdtdJ

T1 T2

T1

T2

T1 T2

16


EMR, Graz University of Technology, April 2012Example

- Subsystem interconnections -

211 TTdtdJ

T1 T2

T2 T3

ICEelectricalmachine

322 TTdtdJ

causal description

T1

T2

T2

T3

T1

T3

J1 J2

Jequ

3121 )( TTdtdJJ

T1

T2

J1

T3J1

T2

derivative relationship

specific solver

acausal description

17



Which method to compute the model?

Forward approach• from the cause to the effect• respect of the energy flow• controller required

Backward • from the desired effect to the

required cause• anticipate energy flow• no controller required

- Forward vs. backward simulation -

vref

restract FFvdtdM

vFtract

Fres

Ftract-ref

control

vFtract

drive cycle

Fres

drive cycle

derivative relationship(no real-time application)

18



- Example of fuel consumption of a vehicle -

vrefcontrol drive cycle

ForwardForward

Fuel ICE TM Vehicle

Fres

vFtract

v

Ttract

dfuel

p

consumption

Fuel ICE TM VehicleFres

Ftract

v

Ttract

dfuel

p

consumption

vref

BackwardBackward

drive cycle

could be same models, but different representations (cf. I/O)



2. 2. «« Energy and Systems Energy and Systems »»

20



Necessity of optimization ofcost, management, integration, reliability…

Objective : 1+ 1 > 2

Prof. CC Chan(Philosophy of engineering)

association of various subsystemIn order to combine their advantages

“System” is a key word!

- Necessity of a system approach -

HEVs = multi-physical systemsmulti-layers systemsenergy nodes….

[Chan & al 09]

21



- Systemic approach -

System = interconnected subsystems organized for a common objective,in interaction with its environment

Systemic = science of study of systems and their interactionsholistic property: the system is a whole which cannot be

deduced by the study of its subsystems

Cartesian approach = the study of subsystems is sufficient toknow the system behavior

Interactions and associations of subsystemswill indicate which approach is required

22



- Input and output of a system -

SystemInput Output

Environment

Input: produced by environment, imposed to the system for evolution(independent of the system)

Output: consequence of the system evolution, imposed to its environment(not directly dependant on the environment)

Environment & System must be defined first!

23



- Cybernetic Systemic -

control

or “Black box” approach: no internal knowledge

in out identification test:observation of out(t) from selected in(t)

Behavior model:out(t) = f(t) in(t)

closed-loop control of out:for uncertainty compensations

in out

outref

24



- Cognitive Systemic -

control

or “White box” approach: prior internal knowledge

in out

Knowledge model:out(t) = f(t) in(t)

control = inversion of model:(closed loop = an inversion way)

in out

outref

Physical laws ofsystem components

25



- Systemic example -

DC machine and smoothing inductor

u

i

u2

Lf rf

u i u2

iruudtdiL f2f

Lm rm

u2 i e

ireudtdiL m2m

i)rr(eudtdi)LL( mfmf

Lf +Lm rf +rm

u i e

Association of both subsystems must be studied globally

m

m

f

f

mf

mf

rL

rL

rrLL

26



Necessity of optimization ofefficiency

reduction of energy consumptionand pollutant emissions

Energy management is a priorityEnergy accumulator must be

carefully manipulated to avoiddamages!

“Energy” is a key word!

- Necessity of energetic approach -

HEVs = multiple subsystemsmultiple energy sourcesenergy nodes….

27



- Energetic approach -

Energy = amount of work that can be performed by a force, an objecta system

Energy accumulation in subsystemsis key transformation for safety and efficiency

Ideal energy conversion: energy conservation (no losses)and instantaneous transfer (no delay)

butEnergy dissipation: losses, reduction of efficiencyEnergy accumulation: delay in energy transfer

28



Interaction principleEach action induces a reaction

action

reaction

S2S2

Power exchanged by S1and S2 = action x réaction

power

- Interaction principle -

Example

battery load

Vbat

Vbat

iload

loadbatteryVbat

iload

P=Vbat iload

29



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 -

30



- Causality principle -

Example

vC

C iccc v

dtdCi

22

1cc vE

delayno energy disruption

vCic

risk of damage

vC icddt

For energetic systemsphysical causality is VITAL



3. 3. «« Graphical descriptionsGraphical descriptionsfor system engineering for system engineering »»

32



Remember, See the wood before the trees!

Use of GRAPHICAL DESCRIPTIONSfor modelling and control of non-elementary systems

System:sub-systems in interactionsorganised for a common objective

transportation systemsrenewable energy applicationsproduction machinesdrives in industry processtactile interfaces...

intermediary stepfor another view of the system

• synthetic description• respect of physic properties• linked to classical modelling

new ways to design, analysesimulate and manage such systems

- Graphical description -

33



iei

VDC iHiuHi

voiture

vrame

ufiltr

e

iHe2

iee2

iHe1iee1uHe1

uHe2

ihachifiltre

+CM1

B1 CM2

B2

Fres

- Example of a railway traction system -

iHe1

Ms

vrame

Fres

Ftot

kbog

kbog

kbog

kbog

Fbog1

Fbog2

k11+1s

xCM1

B1

uHe

xCM1

x

k21+2s

kmcc

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]

Simplified block diagramcausality?action/reaction?

34



iei

VDC iHiuHi

voiture

vrame

ufiltr

e

iHe2

iee2

iHe1iee1uHe1

uHe2

ihachifiltre

+CM1

B1 CM2

B2

Fres


Causal Ordering Graph (COG)

ufiltrre

vrame

Fres

CM1

B1

eei

iee1

cHe1

iHe2

ifiltrre ufiltrreVDC

cHi

uHi iei

uHi

iee1Fbog1

vrame

CM2

B2

Fbog2ufiltrre uHe2

cHe2

iee2

iee1

iHe2

iHi

iHe1

[Hautier 96][Guillaud 01]

causality OKaction/reaction?

35



iei

VDC iHiuHi

voiture

vrame

ufiltr

e

iHe2

iee2

iHe1iee1uHe1

uHe2

ihachifiltre

+CM1

B1 CM2

B2

Fres


Bond Graph (BG)

[Paynter 61][Dauphin 99]

R : Rind2

1

vrame

Fres

CM1

B1eei1

cHe1

ifiltre

ufiltreSe : VDC

cHi

uHi

iei

uHe1

iee1

Fbog1vrame

iHi

iHe1

1VDC

0

R : Rf

ifiltre

ufiltre

I : Lf C : Cf

MTF 1

MTF

ufiltre R : Rind1

I : Lind1

1

I : Lind2

MGY

iei

eei2

iei

TF

1I : M vrameCharge

CM2

B2MGY TF

Fbog2 vrame

1

R : Rex1

I : Lex1

1cHe1uHe2

iee2

MTF 1

R : Rex1

I : Lex1

iHe2

ufiltre

physical causality?action/reaction OK

36



iei

VDC iHiuHi

voiture

vrame

ufiltr

e

iHe2

iee2

iHe1iee1uHe1

uHe2

ihachifiltre

+CM1

B1 CM2

B2

Fres


Energetic MacroscopicRepresentation (EMR)

[Bouscayrol 00][Bouscayrol 05]

rail

filtre

mise en // enroulements

hacheurs

conv. EM

bogie

couplage

rame

environnement

Ftot

vrame

iHe1

M2

eee2

iee2

VDC

ihach

uHe2

iee2He2

CM2

B2 B2

FB2

vrame

vrame

Fres

cHe2

SMSE

ufiltre

iHi

uHi

ieiHi

cHi

iei

eei

M1CM1

B1 B2

FB1

vrameiei

eei1ieieei2

ufiltre

ifiltre

ufiltre

iHe2

eee1

iee1uHe1

iee1He1

cHe1

ufiltre

mise en série

physical causality OKaction/reaction OK

37



Remember,divide and conquer!

Energy & System

mathematical model

global controls

analysisdesign

Energetic Puzzles (Laplace, France)

Bond Graph (USA, The Netherlands…)

Power Oriented Graph (Italy)

Signal Flow Diagram (Germany, Japan...)...

1 0

- Comparison of modelling tools -

COG (L2EP-LEEI, France)

EMR (L2EP, France)

causal descriptions

for simulation and control

cascaded control

inversion graphs

38



- Graphical modelling tools -

1960 2000 20201980

use ofPetri Nets

Power Electronics

discrete eventsystems

Bond Graph

system analysisand design(structural approach)

MechanicalEngineering

ElectricalEngineering

USA The Netherlands worldwide

Causal Ordering Graph (COG)

drive control(functional approach)

ElectricDrivesFrance

EnergeticMacroscopicRepresentation (EMR)

system control(functional approach)

ElectricSystemsFrance Canada

SwitzerlandDenmark China…

EnergeticSystems



4. 4. «« Application to energyApplication to energymanagement of management of EVsEVs and and HEVsHEVs »»

40



- -

Multi-physical system

Real-time control

Systemic approach

Dynamical modelingCausal modeling

Energy management Energetic approachCausal modeling

System control Functional description

Moreover a graphical description could be a valuableintermediary step for such complex systems

- Which model for EV/HEV control? -

41



- Different control levels -

Energy management of HEVs:Energy management of local subsystemsEnergy management of the whole system (co-ordination of subsystems)

Two control levels can be organized:- local control- system supervision

Dynamic and causal modelsQuasi-static

models

compatibilityin term of

inputs/outputs

compatibilityof the

control levels[Delarue & al 2005]

42



- Different control levels (example) -

BAT

ICE

VSI EM

FuelParallel HEV Trans.

fast subsystemcontrols

EMcontrol

ICEcontrol

Transcontrol

Energy management(supervision/strategy)

driver request

slow systemsupervision

43



- Local control -

EMcontrol

ICEcontrol

Transcontrol

Local energy management: must take into account power flows in all parts of subsystems

Classical controls of subsystems: required dynamic and energetic models to managepower flows in real-time

44



- Energy management methods -

[Salmasi 2007]

Energy management(supervision/strategy)

driver request

Rule-based

deterministic rule-based

fuzzyrule-based

state machine / power follower/ thermostat control…

predictive / adpative /conventional…

Optimization based

global optimization

real-time optimization

Dynamic programming / stochastic DP /Game theory / Optimal control….

Robust control / Model predictive / decoupling control / l control…


« Energy Management of complex systemsEnergetic Macroscopic Representation »«« Conclusion Conclusion »»

system = subsystems in interactionbest performances require a systemic approach

energy = respect of the physical causalityenergy management requires a causal approach

control -> inversion of a causal model of the systemin order to respect its energy properties

graphical description = model organizationuseful intermediary step

Remember, follow a disciplined procedure!

46



- References -

P. J. Barre, & al, "Inversion-based control of electromechanical systems using causal graphical descriptions", IEEE-IECON'06, Paris, November 2006.

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, G. Dauphin-Tanguy, R Schoenfeld, A. Pennamen, X. Guillaud, G.-H. Geitner, "Different energetic descriptions for electromechanical systems", EPE'05, Dresden (Germany), September 2005. (common paper of L2EP, LAGIS and University Dresden).

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

C.C. Chan, A. Bouscayrol, K. Chen, "Philosophy of Engineering and Modelling of Electric Drives”, International Conference on Electrical, Keynote, October 2008, Wuhan (China)

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 Lille and Honk-Kong University).

G. H. Geitner, "Power Flow Diagrams Using a Bond graph Library under Simulink", IEEE-IECON'06, Paris, November 2006.

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.

H. Paynter, "Analysis and design of engineering systems", MIT Press, 1961.

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

R. Zanasi, R. Morselli, "Modeling of Automotive Control Systems Using Power Oriented Graphs", IEEE-IECON'06, Paris, November 2006.

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