«About energy and causality principles»

Transcription

1 EMR 16 UdeS - Longueuil June 2016 Summer School EMR 16 Energetic Macroscopic Representation «About energy and causality principles» Prof. X. KESTELYN 1, Prof. A. BOUSCAYROL 2, Prof. CC. CHAN 3 1 L2EP, Arts et MétiersParisTech, France 2 L2EP, Université Lille1, MEGEVH network, France 3 University Hong-Kong, France - Modern s- 2 Modern s are composed of more and more interconnected components (subs), leading to: Complex s Multiphysical s Difficulties to model and control 1

2 - Typical study procedure - A solution seems to be offered by softwares making possible to copy/paste the to be simulated 3 real But for complex and multiphysical s: What are the models used for simulation? How to design and tune the control? How to be sure of the validity of the results? - Our proposal : Be RIGOROUS, OPEN and METHODOLOGIC - 4 Six Principles of Integrated System Design Debate, define, revise and pursue the purpose/objective Reflexion before action ( ) Think Holistic (Systemic) The whole is more than the sum of the parts and each part is more than a fraction of the whole Be creative (multi-level vision) See the wood before the trees Follow a disciplined procedure Divide and conquer, combine and rule Take account of the people To err is human ; Ergonomics; Ethics & Trust Manage the project and the relationships All for one, one for all Prof. CC. Chan 2

3 - Outline Model, Representation and Simulation Different models Different representations 2. Systems and Interactions Systemic approach Cartesian Approach 3. Energy and Causality Integral causality Delay and risks EMR 16 UdeS - Longueuil June 2016 Summer School EMR 16 Energetic Macroscopic Representation 1. «Model, Representation and Simulation» Which steps for a good analysis? 3

4 - From real s to simulation - model representation 7 simulation real Limitation to main phenomena according to the expected objective Organization of the model to highlight properties Intermediary necessary to tackle with complex s - Basic example - model representation 8 simulation real vl d L il RiL V L 1 R+Ls I L Step 1 L.s+R scope smoothing inductor (low frequency dynamical model) (bloc diagram +Laplace) (Simulink +Runge Kutta) 4

5 - Different categories - model representation 9 simulation real dynamic quasi-static static. structural/functional causal/non-causal backward forward. Different possibilities are offered at each step - Representation step is fundamental- The choice of a representation has some consequences. Because the choice of an accurate representation impacts on the gain of a simulation (simulation time, stability, etc ) 10 d C i model c v i c C c C v simulation v C d i c Representation I C 1 Cs V C simulation i c v C The Representation step: the best moment for good questions 5

6 EMR 16 UdeS - Longueuil June 2016 Summer School EMR 16 Energetic Macroscopic Representation «2. System and Interaction» A though as subs in interaction System = - Input and output of a - - in interaction with its environment - composed of interconnected subs organized for a common objective 12 Input System Output Environment Boundaries between Environment & System must be clearly defined 6

7 System = - Systemic and Cartesian approaches - - in interaction with its environment - composed of interconnected subs organized for a common objective 13 Systemic approach Study of subs and their interactions Holistic property: associations of sub induce new global properties. Cartesian approach The study of subs is sufficient to know the behaviour. Cybernetic ic black box approach. behaviour model Cognitive ic physical laws knowledge model Interactions and physical laws must be considered - Systemic versus Cartesian approach - DC machine and smoothing inductor i L f r f L m r m u i u 2 u 2 i e 14 u u 2 L f di u u2 rf i di Lm u2 e rmi L f +L m r f +r m u i e di ( L f Lm ) u e ( rf rm )i Association of both subs must be studied globally L L f f m m r r L r f f L r m m 7

8 - Interaction principle - 15 Nothing is lost, nothing is created, everything is transformed. S1 action reaction power S2 Power exchanged by S1 and S2 = action x reaction Interaction principle: An action induces a reaction [Paynter 61][MMS 00] - Interaction principle - 16 Example V bat battery V bat load V bat i load i load battery load P=V bat i load If the interaction principle is not respected battery V bat load Error in the analysis of the whole Power = 0??? 8

9 EMR 16 UdeS - Longueuil June 2016 Summer School EMR 16 Energetic Macroscopic Representation «3. Energy and Causality» How to manage energy in the best way? - Energetic approach - 18 Energy = amount of work that can be performed by a force, an object, a Ideal energy conversion: energy conservation (no losses) and instantaneous transfer (no delay) but Energy dissipation: losses, reduction of efficiency Energy accumulation: delay in energy transfer Energy is the key parameter for building an efficient model 9

10 Example c c C i c v C i C E c - Energy and Causality principle - d 1 C 2 v v 2 c i c v C 19 delay Physical: no energy disruption v C d i c To represent energetic s: physical causality is important Not physical - Causality principle - 20 Principle of causality physical causality is integral input output x? cause effect t x OK in real-time area [Iwasaki 94][Hautier 96] knowledge of past evolution t 1 slope impossible in real-time knowledge of future evolution dx 10

11 - Comparison of modelling tools - Energy & System 21 Energetic Puzzles (Laplace, France) Bond Graph (USA, The Netherlands ) Power Oriented Graph (Italy) Signal Flow Diagram (Germany, Japan...) Structural description for analysis and design 1 0 mathematical model global controls Block diagrams COG (L2EP-LEEI, France) EMR (L2EP, France) functional descriptions for simulation and control Remember, divide and conquer! inversion graphs cascaded control EMR 16 UdeS - Longueuil June 2016 Summer School EMR 16 Energetic Macroscopic Representation «Conclusion» System = subs in interaction A ic approach to be privileged Graphical representation = model organization A useful intermediary step Energy = respect of the physical causality Energy management requires a causal approach Control -> inversion of a causal model of the in order to respect its energy properties Remember, follow a disciplined procedure! 11

12 - References - S. Astier, A. Bouscayrol, X. Roboam, "Introduction to Systemic Design", Systemic Design Methodologies for Electrical Energy, tome 1, Analysis, Synthesis and Management, Chapter 1, ISTE Willey editions, October A. Bouscayrol, G. Dauphin-Tanguy, R Schoenfeld, A. Pennamen, X. Guillaud, G.-H. Geitner, "Different energetic descriptions for electromechanical s", EPE'05, Dresden (Germany), September (common paper of L2EP, LAGIS and University Dresden). C.C. Chan & al. "Philosophy of Engineering and Modelling of Electric Drives, ICEMS, 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 (common paper of Honk-Kong University and L2EP Lille). G. H. Geitner, "Power Flow Diagrams Using a Bond graph Library under Simulink", IEEE-IECON'06, Paris, Nov H. Grebber, The philosophy of Engineering, IEEE sepctrum, vol. 3, no. 10, 1966, pp J.P. Hautier, J. Faucher, Le graphe informationnel causal, Bulletin de l'union des Physiciens, vol. 90, pp , juin 1996 I. Iwasaki, H. A. Simon, Causality and model abstraction, Artificial Intelligence, Elsevier, vol. 67, pp , MMS project, A. Bouscayrol, & al. "Multimachine Multiconverter System: application for electromechanical drives", European Physics Journal - Applied Physics, vol. 10, no. 2, May 2000, pp (common paper L2EP, GREEN, and LEEI, according to the MMS project of the GDR- SDSE). H. Paynter, "Analysis and design of engineering s", MIT Press, R. Zanasi, R. Morselli, "Modeling of Automotive Control Systems Using Power Oriented Graphs", IEEE-IECON'06, Paris, November Speaker and contributors - 24 Prof. Xavier KESTELYN Arts et Métiers ParisTech, L2EP, France, Research interest on Electromecanichal s with multiple inputs and coupled dynamics, EMR. Prof. Alain BOUSCAYROL Université Lille1, L2EP, France Coordinator of MEGEVH, French network on HEVs Chair of the VPP Tech Com of IEEE-VTS Research interest on EMR, HEVs, HIL simulation Prof. C.C. Chan Tne University of Hong-Kong, China Fellow, Royal Academy of Engineering, U.K. Academician, Chinese Academy of Engineering President, Electric Vehicle Association of Asia Pacific Honorary Professor, University of Hong Kong 12