SIMULATION AND EMR» Prof. A. Bouscayrol (University Lille1, L2EP, MEGEVH, France) Aalto University Finland May 2011

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May 2011

Aalto University

«

H

ARDWARE

-

I

N

-

THE

-

L

OOP

(

HIL

)

S

IMULATION AND

EMR

»

Prof. A. Bouscayrol

(University Lille1, L2EP, MEGEVH, France)

based on the tutorial

“HIL simulation”, EVS’2009, Stavanger (Norway)

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HIL S

HIL SIMULATION USING IMULATION USING EMREMR

Aalto University, May 2011 2 Outline

-1.

1.

W

HAT IS HAT IS

HIL

S

IMULATIONIMULATION

?

2

. .

S

IGNALS AND IGNALS AND

P

OWER OWER

HIL

S

IMULATION IMULATION

3

. .

F

ULL AND ULL AND

R

EDUCEDEDUCED--SCALE SCALE

HIL

S

IMULATION IMULATION

4

. .

E

XAMPLES OF XAMPLES OF

HIL

S

IMULATIONIMULATION

1 example using EMR

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Aalto University, May 2011 3 Classical approach of development of new product

-Example of an EV

simulation

Prototype I, II, III….

http://www.renault.com

real vehicle

Long development time Important cost Material waste Intermediary step ? HIL simulation ? (towards a unique Prototype)

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HIL S

HIL SIMULATION USING IMULATION USING EMREMR

Aalto University, May 2011 4

process control (blue)

Physical power system (orange)

Graphical and colour Code

-power variables mathematical model (purple) signal variables SIMULATION SOFTWARE REAL-TIME CONTROLLER SUBSYSTEM UNDER TEST

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May 2011

Aalto University

1.

W

HAT IS

H

ARDWARE

-

I

N

-

THE

-

L

OOP

S

IMULATION

?

• Software simulation

• HIL simulation

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HIL S

HIL SIMULATION USING IMULATION USING EMR_EMR

POWER SYSTEM CONTROLLER BOARD (ECU) measurements process control control signals _power electronics electric machine mechanical power train System & Control

-How to develop the control? Software simulation Example of an electric drive for traction

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Aalto University, May 2011 7 SIMULATION ENVIRONMENT power electronics measurements electric machine mechanical power train process control control signals

Limitations of Software simulation

-How to check the control

before real-time implementation? HIL simulation? model accuracy simulation solver

sensor effects (quantification, EMI..) ECU effects (discrete-time...)

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HIL S

HIL SIMULATION USING IMULATION USING EMREMR

SUBSYSTEM UNDER TEST CONTROLLER BOARD (ECU) power electronics electric machine mechanical power train process control control signals

Hardware-In-the-Loop (HIL) simulation:

one simulation part is replaced by an actual part HIL simulation (1)

-Example: test of ECU and Power Electronics electric

machine

mechanical power train

SIMULATION ENVIRONMENT

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Aalto University, May 2011 9 Interface System CONTROLLER BOARD (ECU) power electronics process control HIL simulation:

Includes a hardware part, a software part and a specific interface HIL simulation (2) -electric machine mechanical power train HARDWARE SOFTWARE

HIL simulation = Real-time simulation (but including a hardware part) = Emulation

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HIL S

HIL SIMULATION USING IMULATION USING EMREMR

Aalto University, May 2011 10 Models for HIL simulation

-HIL simulation =

Hardware (energy conversion) + Models computed in real-time in dynamic interactions

HARDWARE

REAL-TIME SIMULATION

energetic model causal model dynamic model

power

efficient results requires an accurate model and a well-defined interface system!

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Aalto University, May 2011 11 HIL simulation appraoch

-Example of an EV simulation prototype http://www.renault.com real vehicle HIL platform

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Aalto University Finland

May 2011

« Energy Management of EVs & HEVs using Energetic Macroscopic Representation »

2.

S

IGNALS AND

P

OWER

HIL

S

IMULATIONS

• Signal HIL simulation

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Aalto University, May 2011 13 CONTROLLER BOARD (ECU) power electronics measurements electric machine mechanical power train process control control signals

The actual controller board containing the process control is tested.

Signal HIL simulation (1)

-subsystem to test

Objectives:

- control assessment - reliability of ECU

- fault operation of ECU - …

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HIL S

HIL SIMULATION USING IMULATION USING EMREMR

SYSTEM UNDER TEST CONTROLLER BOARD (ECU) power electronics measurements electric machine mechanical power train process control control signals

Signal HIL simulation (3)

-Second controller board with its own interface Fast dynamic: FPGA?

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Aalto University, May 2011 15 Example of signal HIL simulation

-ECU subsystem to test EMULATION CONTROLLER battery + chopper (1) DC machine (2) (3) mechanical trans. (4)-(6) drive control s_ij u u_chop i_dcm i_{dcm_meas} T_dcm



_gear



_{gear_meas} V_{bat_meas} SYSTEM UNDER TEST CONTROLLER BOARD (ECU)

Remark: dynamic causal models

(2) e Ri u i dt d L _dcm  _chop  _dcm  _dcm u u_chop idcm 



uchopdt

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HIL S

HIL SIMULATION USING IMULATION USING EMREMR

CONTROLLER BOARD (ECU) power electronics measurements electric machine mechanical power train process control control signals

Power HIL simulation (1)

Objectives:

- control assessment

- power device assessment - interactions (EMI…)

- … The actual controller board and a power subsystem are tested.

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Aalto University, May 2011 17 SIMULATION ENVIRONMENT SYSTEM UNDER TEST CONTROLLER BOARD (ECU) power electronics electric machine mechanical power train process control control signals

Power HIL simulation (2)

-Interface System: • control and power signals • equivalent measurements • power action and reaction

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HIL S

HIL SIMULATION USING IMULATION USING EMREMR

EMULATION CONTROLLER SYSTEM UNDER TEST CONTROLLER BOARD (ECU) power electronics electric machine mechanical power train process control control signals

Power HIL simulation (4)

-The same controller board can be used for: • control of the emulation system

• real-time simulation of subsystems

emulation system

emulation control

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Aalto University, May 2011 19 CONTROLLER BOARD (ECU) power electronics measurements electric machine mechanical power train process control control signals

Power HIL simulation (extension)

Objectives:

- drive assessment

- test on static platform - …

Electrical power HILS

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HIL S

HIL SIMULATION USING IMULATION USING EMREMR

Aalto University, May 2011 20 Example of electrical power HIL simulation (1)

-ECU subsystem to test L u u_chop i_dcm

i_{dcm_ref} current loop

example of emulation system

i_dcm i_dcm battery + chopper DC machine (2) (3) u u_chop battery + chopper emulation system u u_chop must impose the same current

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EMULATION CONTROLLER

• fast current loop

• small sampling period Example of electrical power HIL simulation (2)

-L u u_chop i_dcm i_{dcm_ref} current loop emulation system real battery and chopper drive control ECU DC machine (2) (3) u u_{chop_est} mechanical trans. (4)-(6)

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HIL S

Aalto University, May 2011 22 Example of mechanical power HIL simulation (1)

-ECU

subsystem to test



_{gear _ref} speed / current loops

example of emulation system

T_dcm



_gear

must impose the same speed



_gear DC machine mechanical trans. (4)-(6) T_dcm ?

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Aalto University, May 2011 23 emulation system T_dcm



_gear real machine real bat. & chopper EMULATION CONTROLLER

Example of mechanical power HIL simulation (2)

-drive control ECU T T_{dcm_est} mechanical trans. (4)-(6)



_{gear _ref}

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Aalto University Finland

May 2011

« Energy Management of EVs & HEVs using Energetic Macroscopic Representation »

4. E

XAMPLES OF XAMPLES OF

HIL S

IMULATIONSIMULATIONS a. Test of traction drive for an EV

a. Test of traction drive for an EV

[Bouscayrol 2006b] In collaboration with

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Aalto University, May 2011 25 Objectives -wh2 Tdiff2 Vbat iinv gear Tim diff Tgear wh1 Tdiff1 s11 s21 s31

Traction of the studied EV: VSI + Induction machine + differential + 2 driven wheels

Drive implementation for an Electric Vehicle ?

1. Simulation of the EV (drive + vehicle dynamics) 2. Test of the actual traction drive

3. Test of the whole prototype

drive control

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HIL S

HIL SIMULATION USING IMULATION USING EMREMR

Aalto University, May 2011 26 Park’s transformation T_im _gear induction machine u_dq i_dq e_dq i_dq _d/s _rw _lw T_dif T_dif T_gear _dif gearbox differential m_steer chassis environ. wheels MS v_ev F_tot F_lw F_rw F_res v_ev v_lw v_rw battery ES u_bat i_vsi VSI s_vsi u_vsi i_im _rw T_dif ubat ivsi _gear T_im _dif T_gear _lw T_dif s11 s21 s31

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-Aalto University, May 2011 27

control

Maximum control structure

-m_steer

battery VSI _Park’s transformation chassis environ. wheels MS v_ev F_tot F_lw F_rw F_res v_ev v_lw v_rw _rw _lw T_dif T_dif T_gear T_im _dif ES u_bat i_vsi s_vsi _gear u_vsi induction machine u_dq i_dq i_im i_dq e_dq _d/s gearbox differential power F_tot-ref v_ev-ref v_ev-mes F_res-mes F_lw-ref k_R k_P T_diff-ref1 T_gear-ref T_im-ref F_rw-ref T_diff-ref2 PWM __m-ref i_dq-ref v_dq-ref u_vsi-ref FOC _d/s-est

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HIL S

HIL SIMULATION USING IMULATION USING EMREMR

control

Estimation and strategy

-m_steer

battery VSI _Park’s transformation chassis environ. wheels MS v_ev F_tot F_lw F_rw F_res v_ev v_lw v_rw _rw _lw T_dif T_dif T_gear T_im _dif ES u_bat i_vsi s_vsi _gear u_vsi induction machine u_dq i_dq i_im i_dq e_dq _d/s gearbox differential power k_R k_P F_tot-ref v_ve-ref F_lw-ref T_diff-ref1 T_gear-ref T_im-ref v_ev-mes F_res-mes F_rw-ref T_diff-ref2 i_dq-ref v_dq-ref u_vsi-ref FOC __m-ref _d/s-est strategy PWM model i_{dq_est} e_dq _ m-est

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control

Synthetic EMR

-m_steer

battery VSI _Park’s transformation chassis environ. wheels MS v_ev F_tot F_lw F_rw F_res v_ev v_lw v_rw _rw _lw T_dif T_dif T_gear T_im _dif ES u_bat i_vsi s_vsi _gear u_vsi induction machine u_dq i_dq i_im i_dq e_dq _d/s gearbox differential power v_ev-mes k_R k_P F_tot-ref v_ve-ref F_lw-ref T_diff-ref1 T_gear-ref T_im-ref F_res-mes F_rw-ref T_diff-ref2 i_dq-ref v_dq-ref u_vsi-ref FOC __m-ref _d/s-est PWM traction drive drive control mechanical power train power train control v_ev-ref T_im-ref T_im



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HIL S

HIL SIMULATION USING IMULATION USING EMREMR

mechanical power train traction drive

drive control

control

HIL simulation principle

-power power train control v_ev-ref T_im-ref T_im T_im model mechanical power train DC load drive drive control



_ref=



_mod



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controller board

HIL simulator

Overall HIL simulation

-MS ES

v_ve-ref

PWM FOC

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HIL S

Aalto University, May 2011 32 Experimental setup

-IM

DCM

inverter chopper dSPACE 1103 controller board

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Aalto University, May 2011 33 Experimental setup

-IM

DCM

inverter chopper dSPACE 1103 controller board

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HIL S

Ngear-mes Tim-ref iim1-mes idcm-mes vlw-mod Ngear-ref im-ref ubat v_ev-ref vev-mod vrw-mod ma2-est

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-May 2011

Aalto University

C

ONCLUSION

HIL simulation interest:

valuable intermediary step before the implementation of the control or a new subsystem in a real process

HIL simulation requirements:

• respect of interactions between subsystems

• respect of the physical causality for real-time implementation • development of an adapted interface system

• use of a accurate causal dynamical model

EMR = valuable tool for organization of the numerous blocks of HIL simulation

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HIL S

HIL SIMULATION USING IMULATION USING EMREMR

Aalto University, May 2011 36 References

-A. L. Allègre, -A. Bouscayrol, J. N. Verhille, P. Delarue, E. Chattot, S. El Fassi, “Reduced-scale power Hardware-In-the-Loop simulation of an innovative subway", IEEE transactions on Industrial Electronics, vol. 57, no. 4, April 2010, pp. 1175-1185 (common paper of L2EP Lille and Siemens Transportation Systems).

A. Bouscayrol, W. Lhomme, P. Delarue, B. Lemaire-Semail, S. Aksas, “Hardware-in-the-loop simulation of electric vehicle traction systems using Energetic Macroscopic Representation”, IEEE-IECON'06, Paris, November 2006, (common paper L2EP Lille and dSPACE France).

A. Bouscayrol, X. Guillaud, R. Teodorescu, P. Delarue, W. Lhomme, “Hardware-in-the-loop simulation of different wind turbines using Energetic Macroscopic Representation”, IEEE-IECON'06, Paris, November 2006, , (common paper L2EP Lille and Aalborg university, Denmark).

A. Bouscayrol, “Different types of Hardware-In-the-Loop simulation for electric drives”, IEEE-ISIE’08, Cambridge (UK), June 2008,

A. Bouscayrol, X. Guillaud, P. Delarue, B. Lemaire-Semail, “Energetic Macroscopic Representation and inversion-based control illustrated on a wind energy conversion systems using Hardware-in-the-loop simulation”, IEEE transaction on Industrial Electronics,vol. 56, no. 12, December 2009, pp. 4826-4835.

A. Bouscayrol, "Hardware-In-the-Loop simulation", Industrial Electronics Handbook, second edition, tome 3, Chapter M35, Editions Taylor & Francis, Chicago, March 2010, ISBN 9781848210967.

H. Hanselmann, "HIL simulation testing and its integration into a CACSD toolset", IEEE-CACSD’96, Dearborn, September 96.

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Aalto University, May 2011 37 References (2)

-H. Li, M. Steurer, S. Woodruff, K. L. Shi, D. Zhang, "Development of a unified design, test, and research platform for wind energy systems based on hardware-in-the-loop real time simulation", IEEE trans. on Industrial Electronics, vol. 53, no. 4, June 2006, pp. 1144-1151.

W. Lhomme, R. Trigui, P. Delarue, A. Bouscayrol, B. jeanneret, F. Badin, “Validation of clutch modeling for hybrid electric vehicle using Hardware-In-the-Loop simulation”, IEEE-VPPC’07, Arlington (USA), September 2007, (common paper L2EP Lille and LTE INRETS Bron according to MEGEVH project).

D. Maclay, "Simulation gets into the loop", IEE Review, May 1997, pp. 109-112.

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 Systems).

C. A. Rabbath, M. Abdoune, J. Belanger, K. Butts, “Simulating hybrid dynamic systems”, IEEE Robotics & Automation Magazine,Vol. 9, no. 2, June 2002, pp. 39 – 47.

P. Terwiesch, T. Keller, E. Scheiben, "Rail vehicle control system integration testing using digital

hardware-in-the-loop simulation', IEEE trans. on Control System Technology, Vol. 7, no. 3, May 99, pp. 352-362.

R. Trigui, B. Jeanneret, B. Malaquin, F. Badin, "Hardware-In-the-Loop Simulation of a diesel parallel mild hybrid vehicle", IEEE-VPPC’07, Arlington (USA), September 2007.