DISTRIBUTION AND STRATEGY

Transcription

1 Polytech Paris Sud June 2014 «ENERGY DISTRIBUTION AND STRATEGY» Prof. A. Bouscayrol (University Lille1, L2EP, MEGEVH, France) based on the course of Master Electrical Engineering & sustainable Development University Lille1

2 - Objective: example of HEV control - BAT VSI EM 2 Parallel HEV Fuel ICE Trans. fast subsystem controls EM control ICE control Trans control slow system supervision Energy management (supervision/strategy) driver request

3 BAT - Different control levels (2) - VSI1 EM1 3 Parallel HEV Fuel ICE EMR Trans. fast subsystem controls EM1 control ICE control Trans control Inversion-based control slow system supervision Energy management (supervision/strategy) How to manage energy? Interaction between both levels? driver request

4 - Outline - 1. Control decomposition Local control structure Strategy level for energy management 2. Various inversions of distribution elements Different kinds of energy management Inversion of the different cases 3. Analysis and strategy Analysis of the system possibilities Strategy level 4 4. Application to the automatic subway VAL 206

5 Polytech Paris Sud June «Local control and energy management»

6 - Principle of Inversion-based methodology - 6 cause input System effect output [Hautier 96] measurements? right cause Control desired effect 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 control industrial electronics

7 - EMR and Inversion-based methodology - 7 cause effect input SS 1 SS 2 SS n output measure? measure? measure? right cause C 1 C 2 C n desired effect EMR = system decomposition in basic energetic subsystems (SSs) Remember, divide and conquer! Inversion-based control: systematic inversion of each subsystems using open-loop or closed-loop control

8 - Mirror effect - 8 cause SS 1 SS 2 SS n effect right cause C 1 C 2 C n desired effect The control scheme is developed as a mirror of the model [Hautier 96] [Bouscayrol 03]

9 Legend - Inversion of EMR elements - conversion element direct inversion + disturbance rejection 9 Control = light blue Parallelograms with dark blue contour direct inversion accumulation element controller + disturbance rejection indirect inversion sensor mandatory link facultative link coupling element distribution criteria

10 - Maximum control scheme - 1. EMR of the system 2. Tuning path 3. Inversion step-by-step Strong assumption: all variables can be measured! 10 S1 x 1 x 2 x 3 x 4 x 5 x 6 x 7 y 1 y 2 y 3 y 4 y 5 y 6 y 7 z 23 z 67 S2 x 3-ref x 4-ref x 5-ref x 6-ref x 7-ref 4. Simplification of control 5. Estimation of non-measured variables 6. Tuning of controllers 7. Strategy strategy

11 - Management of the energy distribution - 11 Bat v Env Bat. DCM EP 1 1 ME 1 F res Strategy DCM EP 2 2 ME 2

12 - Different control levels - 12 Energy management of HEVs: Energy management of local subsystems Energy management of the whole system (co-ordination of subsystems) Two control levels can be organized: - local control - system supervision Quasi-static models Dynamic and causal models [Delarue & al 2005] compatibility of the control levels compatibility in term of inputs/outputs

13 - Articulation of control levels - 13 Local control: Fast energy management in each subsystem Objective: ensure the best efficiency of each component in function of the request Inversion of each element, step-step-step, using dynamic causal models Energy management: Coordination of energy between all subsystems Objective: ensure the best energy distribution in function of the global demand Global view for decision using quasi-static or static models Could be considered as an inversion of a global model of the system

14 BAT VSI - Articulation of control & models - EM 14 Fuel ICE Trans. Inversion of causal dynamical models EM control ICE control Trans control Strategy delivers references for local controls Energy management (supervision/strategy) driver request Inversion of a global static model

15 inverter - From dynamical model of IM drive - stator EM windings conversion Park s transformation 15 V DC u d/s inv v s-dq i s-dq i vsi i im i s-dq e s-dq i s-dq-est rd Global behavior of the drive i sd (machine + inverter + control) T im gear r-est e s-dq-est i sd-ref d/s_est r-ref u inv-ref v s-dq-ref i s-dq-ref T im-ref

16 V DC drive - to static and dynamical models - T im T im (Nm) Efficiency map 16 i vsi gear T im-ref gear (rad/s) V DC T im static model i vsi x + - x gear T im-ref

17 - Coherence of dynamical and staticmodel - 17 Static model: no dynamical effect, I/O could be changed V DC T im V DC gear V DC gear i vsi gear i vsi T im i vsi backward T im Tim-ref gear-ref static V DC Dynamic T im What happens if EM = inversion of a static model and IBC = inversion of dynamical models with differents I/O? Bad articulation between control levels i vsi Tim-ref gear

18 Rule-based «Energy Distribution and Strategy» - Energy management of HEVs - deterministic rule-based fuzzy rule-based state machine / power follower/ thermostat control predictive / adpative / conventional 18 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 (supervision/strategy) [Salmasi 2007] driver request

19 Polytech Paris Sud June «Inversion of various coupling elements»

20 - Upstream coupling elements - upstream common part Example: differential 20 u 1 y 1 y 21 u 21 diff T gear T ldiff lwh T rdiff rwh Tldif wh T rdif rwh Tdiff 2 2 lwh y 2p u 1p T ldiff m: number of upstream power flows p: number of downstream power flows T gear wh lwh T rdiff rwh m < p objective = distribution of energy into p different power flows m=1, p=2 (m<p) objective = distribution of torque

21 - Downstream coupling elements - downstream common part Example: chassis of a train 21 y 21 u 21 y 2 4 traction bogies y 2p u 1m u 2 F bog1 F bog2 v train F tot m: number of upstream power flows p: number of downstream power flows F bog3 F bog4 v train v train v train v train m > p objective = collect energy into m different power flows m=4, p=1 (m>p) objective = collecting bogie forces

22 - Neutral coupling elements - 22 u 11 y 11 u 21 y 21 Example: Park s transformation d/s u 13 i s1 u 23 v sd i sd v sq u 1m y 1m u 21 y 21 i s2 i sq m: number of upstream power flows p: number of downstream power flows m = p objective = reorganize power flows m=2, p=2 (m=p) objective = convert AC voltages and currents into DC voltages and currents

23 - Inversion of neutral coupling elements - 23 u 11 y 11 u 21 y 21 Example: Park s transformation d/s u 1m y 1m u 2p y 2p u 13 i s1 u 23 i s2 v sd i sd v sq d/s i sq u 11 y 21-ref u 13-ref v sd-ref u 1m y 2p-ref u 23-ref v sq-ref no measurement no controller / direct inversion use of the inverse matrix

24 u 1 «Energy Distribution and Strategy» - Inversion of upstream coupling elements - y 21 u 21 Implement a compromise or prioritize outputs. Example: differential T ldiff 24 y 1 u 1 y 2p-ref y 2p u 2p no measurement no controller (p - m) weighting variables y 21-ref T gear wh T gear lwh T rdiff rwh T diff-ref1 T diff-ref2 u k W1 k W(p-m) ' ' 1 kw1y21ref... ( 1 kwi ) y2 pref T gear k w 2 kwtdiff ref 1 (1 kw ) Tdiff ref 2

25 Case 1: all inputs are used u 11 y 11 - Inversion of downstream coupling elements (1) - Example: chassis of a train F bog1 25 y 2 F bog2 v train F tot u 1m y 1m u 2 F bog3 v train v train v train u 11 no measurement no controller (m - p) distribution variables F bog4 F bog1 F bog2 v train u 1m k D1 k D(m-p) y 2-ref u u 1m 11 k k ' D 1... y ' Dm p 2ref y 2ref F bog3 F bog4 k D1 k D2 F tot-ref k D3

26 Polytech Paris Sud June «Analysis of the system possibilities and strategy level»

27 - Critical factors - 27 n O = number of objectives Example: 3-leg Voltage-Source-Inverter i 1 n C = number of constraints (in order to achieve the objectives) V DC s 11 s 12 s 13 i 2 u 13 n T = number of tuning variables (in order to act on the system) s 21 s 22 s 23 i vsi n O = 2 (u 13 and u 23 ) u 23 n C = 3 (3 commutation cells) S i 1 S i 2 for i 1,2,3 n T = 6 (6 switching orders S ij )

28 - Ideal case - 28 n T n O n C They are enough tuning inputs to achieve the objective and realize the constraints Example: 3-phase induction machine Park s transformation u d/s inv i im stator windings v s-dq i s-dq i s-dq e s-dq EM conversion T im rd n O = 1 (T im ) i sd gear n T n O n C n C = 1 (flux r ) n T = 2 (u 13 and u 23 ) Field-oriented control is a way to impose flux and torque by voltages (using a decoupling (d,q) frame)

29 - Optimisation case - 29 n T n O n C Example: 3-leg Voltage-Source-Inverter i 1 They are more tuning inputs than the objective and the constraints V DC s 11 s 21 s 12 s 22 s 13 s 23 i 2 u 23 u 13 i vsi all tuning inputs must be used the degrees of freedom can be used for Optimisation a strategy level is required to define the degrees of freedom n O = 2 (u 13 and u 23 ) n C = 3 (3 commutation cells) n T = 6 (6 switching orders S ij ) Various optimization strategies for VSI such as 3 rd harmonic injection, flat-top modulation

30 - Compromise case - 30 n T n O n C Example: differential T gear T ldiff lwh They are not enough tuning inputs than the objective and the constraints wh T rdiff rwh a compromise must be defined (priorities) a strategy level is required to define the priority T gear k W n O = 2 (T diff1 and T diff1 ) n C = 0 n T = 1 (T gear ) T diff-ref1 T diff-ref2 Ex: k W =1 priority to T diff1

31 - Coupling elements and strategy - 31 All downstream and upstream Coupling elements Example: differential T gear T ldiff lwh Requires criteria (distribution or weighting) wh T rdiff rwh these criteria variables must be defined by the strategy level T gear k W T diff-ref1 T diff-ref2 Strategy Ex: during straight lines, T gear = average value of both resquets

32 - Strategy level - 32 Strategy level: global management of the whole system Has to define: local control references distribution coefficients weighting coefficients optimization inputs compromises For energetic system: definition of priority between performances and efficiency Inversion of a global static model (different possibilities) Example: Flux weakening in EVs U U max rated U depends on the battery voltage V bat depends on velocity v EV ref static plots global view V bat-meas v EV-meas

33 Polytech Paris Sud June «Application to the control of an automatic subway» part II: real system Prof. A. Bouscayrol (University Lille1, L2EP, MEGEVH, France) based on PhD of J. N. Verhille in collaboration with Siemens Transportation Systems

34 - EMR of electrical part - 34 rail filter parallel choppers MCC in series bogies u filter i field1 T mach1 i arm Seq1 ES V DC i filter u filter u filter u chop2 i arm e arm1 bog1 i filter u filter i tot u filter i arm m chop2 e tot Seq2

35 - Control of electrical part - 35 rail filter parallel choppers MCC in series bogies u filter i field1 T mach1 i arm Seq1 ES V DC i filter u filter u filter u chop2 i arm e arm1 bog1 i filter u filter i tot u filter i arm m chop2 e tot Seq2 u filter-mes i field2-ref i arm-ref =k W i arm-ref1 +(1-k W )i arm-ref2 u chop3-ref u chop2-ref i arm-ref i arm-ref2 i arm-ref1 T mach2-ref T mach1-ref u chop1-ref i field1-ref

36 - Control of electrical part - 36 rail filter parallel choppers MCC in series bogies u filter i field1 T mach1 i arm Seq1 ES V DC i filter u filter u filter u chop2 i arm e arm1 bog1 i filter u filter i tot u filter i arm m chop2 e tot Seq2 u filter-mes i field2-ref Strategy has to define the 3 criteria u chop3-ref u chop2-ref i arm-ref i arm-ref2 i arm-ref1 T mach2-ref T mach1-ref u chop1-ref i field1-ref

37 rail filter parallel - Control of electrical part - choppers MCC in series bogies 37 ES V DC T mach1 bog1 Seq1 Seq2 u filter-mes i field2-ref u chop3-ref u chop2-ref u chop1-ref i arm-ref i field1-ref i arm-ref2 T i mach2-ref arm-ref2 arm-ref1 Actual control: no possibility of independent T mach1-ref torque

38 motor - EMR of mechanical part - differential brake gearbox wheels contact chassis environ. 38 T dif1 bk1 T gear1 gear1 v wh1 F cont1 T mach1 m T dif bk1 T gear1 gear1 T wh1 F cont1 v VAL F bog1 v VAL DCM SM m T gear dif T dif2 bk2 T gear2 gear2 v wh2 F cont2 v VAL F res motor MOTEUR TRANSMISSION PONT + DIFFERENTIEL bk2 T gear2 T roue2 F cont2 gear2 v VAL non-linear devices 1/2 ESSIEU 1/2 ESSIEU PNEU REDUCTEUR REDUCTEUR PNEU T red backslash F slip

39 - Control of mechanical part - 39 DCM1 DCM2

40 - Control of mechanical part - 40 DCM1 DCM2 T mach2ref v VAL-ref T mach1ref

41 - Control of mechanical part - 41 DCM1 DCM2 T mcc2-ref Master-slave control v rame-ref T mach1ref

42 - Actual control of mechanical part - 42 DCM1 DCM2 arb-mes v rame-est Merging of control blocs T mach1ref T mach1ref Actual control: no local management of anti-slip v rame-ref

43 - Simulation model - Matlab/Simulink TM electrical part mechanical part 43 Actual control

44 - Validation model (1) - 44 experimental simulation armature current (A) field current (A) experimental simulation

45 Test at normal operating 16 - Validation model (2) - experimental simulation Test at maximal torque velocity (m/s) velocity (m/s) 8 experimental simulation

46 Polytech Paris Sud June «Application to the control of an automatic subway» part III: anti-slip strategy Prof. A. Bouscayrol (University Lille1, L2EP, MEGEVH, France) based on PhD of J. N. Verhille in collaboration with Siemens Transportation Systems

47 - VAL 206 and control - 47 VAL Automatic Subway: Lille, Paris airport, Chicago, Taïpeh... Actual control VAL 206 identical torque for 4 motors global anti-slip (cancellation of torque) 4-mes T ref v ref T 1-ref 1-mes T 2-ref 2-mes T 3-ref 3-mes T 4-ref 4-mes? v ref STS Independent controls with same power structure? Performances with local anti-slip?

48 Tdcm_ref wh22_mes wh21_mes wh12_mes wh11_mes strategy - Anti-slip control - v sub_mes F tot1_ref v sub_ref Actual control: slip detection 48 Torque set to zero T dcm_ref F bog_ref shaft2_mes T dcm2_ref shaft2_ref wh2_ref k W2 k D2 F bog2_ref v sub_mes New strategy: slip detection wh22_mes wh21_mes wh12_mes wh11_mes new strategy k D F tot1_ref v sub_ref Reduction of torque of the slipping wheel T dcm1_ref shaft1_mes shaft1_ref wh1_ref k W1 k D1 F bog1_ref Increase of other torques

49 - Philosophy of the strategy - 49 F F Normal operation: equal distribution of traction forces 1 kd kd1 kd2 kw1 kw 2 2 Bog1 tot F Bog2 FBog i F Bog3 F Bog4 1 4 F tot k W2 k D2 Slipping on bogie 1: cancelation of F bog1 and new distribution of other forces k k k D D2 D1 1 2 k k W 2 W1 1 0 wh22_mes wh21_mes wh12_mes wh11_mes new strategy k W1 k D1 k D F F F tot Bog1 Bog2 0 F Bog3 F Bog i F Bog4 1 3 F tot

50 - Simulation results - Loss of adhesion of wheel no actual control velocity (m/s) New anti-slip strategy velocity (m/s) v sub_ref v sub v sub_ref v sub traction force (Nm) F tot (Nm) traction force (Nm)

51 - HIL simulation of the VAL traction system - 51 traction drive T im mechanical AC load drive power train drive control T im ref = mod mechanical power train drive control power train control T im-ref v ev-ref power control model

52 - Experimental results - 52 current (A) i arm velocities (m/s) i field2 current (A) v sub_ref v sub i field1 Loss of adhesion on wheel 1 F bog1 is decreased by acting on i field1 F bog2 is increased by acting on i arm

53 -Conclusion- 53 New anti-slip control Energetic Macroscopic Representation of VAL traction Inversion-based control from EMR Best repartition of traction effort Best performances during slipping phenomena HIL simulation real-time validation of the control dual drive system to be implemented on the actual system

54 Polytech Paris Sud June 2014 «Conclusion» Inversion based control = inversion of EMR inversion of dynamic causal models a unique Maximum Control Schemes Strategy Level = inversion of a global static model global model and local dynamic models must have same I/O different strategies are possible Distribution elements Highlight degree of freedom, to be defined in the strategy level Efficient energy management of EVs and HEVs Well-defined local controls of subsystems coherent articulation between control levels efficient strategy level

55 - References - 55 A. Bouscayrol, B. Davat, B. de Fornel, B. François, J. P. Hautier, F. Meibody-Tabar, M. Pietrzak- David, Multimachine multiconverter system: application for electromechanical drives, European Physics Journal - Applied Physics, vol. 10, no. 2, May 2000, p (common paper GREEN Nancy, L2EP Lille and LEEI Toulouse, according to the SMM project of the GDR-SDSE). 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 (common paper L2EP Lille and Jeumont SA). 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 (common paper of L2EP Lille and LEEI Toulouse). J. N. Verhille, A. Bouscayrol, P. J. Barre, J. C. Mercieca, J. P. Hautier, E. S , "Torque tracking strategy for anti-slip control in railway traction systems with common supplies", IEEE-IAS 04, proceeding vol. 4. pp , Seattle (USA), October 2004 (common paper L2EP Lille and Siemens Transportation Systems).. 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) L. Horrein, V. Derache, A. Bouscayrol, J. N.Verhille, P. Delarue, Different models of the traction system of an automatic subway, ElectrIMAC 11, Cergy Pointoise (France), June 2011 (common paper of L2EP and Siemens Transportation Systems).