«EMR AND INVERSION-BASED CONTROL

Transcription

1 EMR 17 Lille June 2017 Summer School EMR 17 Energetic Macroscopic Representation «EMR AND INVERSION-BASED CONTROL OF AN ELECTRIC VEHICLE» Dr. A. Castaings, Dr. W. Lhomme, Prof. Alain Bouscayrol L2EP, Université Lille1, MEGEVH, France

2 - Introduction - Simulation is a key issue before working on a real system 2 model representation simulation real system How to organize simulation? Control? Tazzari Zero of L2EP d J gear Tdcm Tgear f gear dt d Larm idcm uchop edcm Rarmidcm dt uchop mchop Vbat ichop mchop idcm Tdif kdif Tgear dif kdif wh Tdcm kdcmidcm Tgear kgear Tdcm edcm kdcm gear gear kgear diff d M vev Ftot Fres dt

3 - Outline Studied EV 2. EMR of the studied EV 3. Inversion-based control of the EV

4 EMR 17 Lille June 2017 Summer School EMR 17 Energetic Macroscopic Representation «STUDIED ELECTRIC VEHICLE»

5 - Tazzari Zero EV - 5 Tazzari Zero characteristics 15 kw induction machine 80V 160Ah LiFePO4 battery 542 kg (empty mass) Maximum speed: 85km/h i ts i i im2 im1 T im gear

6 - Simplified EV - Objective: control of the traction system in straight lines 6 Simplifications: a permanent magnet DC machine is considered in a first step an equivalent wheel is considered (no curve) i ts i T im gear

7 EMR 17 Lille June 2017 Summer School EMR 17 Energetic Macroscopic Representation «EMR OF THE STUDIED ELECTRIC VEHICLE»

8 Structural Representation i ts - EMR of the EV - 8 T im gear Functional Description u chop Battery i chop i arm m chop u i chop chop m m chop chop V i bat arm

9 - EMR of the EV - 9 i ts i T im gear u chop i dcm T dcm gear Battery i chop i arm e dcm shaft T gear m chop L arm d dt i dcm u chop e dcm R i arm dcm T e dcm dcm k k dcm dcm i dcm gear J d dt gear T dcm T gear f gear

10 - EMR of the EV - 10 i ts i T im gear u chop i dcm T dcm shaft gear diff v ev Battery i chop i arm e dcm shaft T gear T diff T wh F wh m chop T gear gear k k gear gear T diff shaft T dif dif k k dif dif T wh gear T v wh ev R R wh wh F wh diff

11 - EMR of the EV - 11 i ts i T im gear u chop i dcm T dcm shaft gear diff v ev F wh v ev Battery i chop i arm e dcm shaft T gear T diff T wh F wh v ev F res m chop M d dt v ev F tot F res

12 - EMR of the EV - 12 i ts i T im gear u chop i dcm T dcm shaft gear diff v ev F wh v ev Battery i chop i arm e dcm shaft T gear T diff T wh F wh v ev F res m chop Conflict of association: shaft and v ev state variables but v ev R wh permutations k diff k gear shaft

13 - EMR of the EV - 13 i ts i T im gear u chop i dcm T dcm T gear T diff F wh v ev F wh v ev Battery i chop i arm e dcm shaft gear diff v ev F res v ev F res m chop Conflict of association: a unique state variable is required! M eq d dt v ev F tot F M res eq M merging k J 2 gear shaft 2 diff k R 2 wh

14 - EMR of the EV - 14 A F aero ½ F roll F grade ½ F roll a M g a u chop i dcm T dcm T gear T diff F wh v ev Env. Battery i chop i arm e dcm shaft gear diff v ev F res m chop F res k roll 1 Mg cosa 2 air AC x v 2 ev Mg sina

15 EMR 17 Lille June 2017 Summer School EMR 17 Energetic Macroscopic Representation «INVERSION-BASED CONTROL OF THE STUDIED ELECTRIC VEHICLE»

16 - Tuning path - 16 chopper DC machine gearboxes wheel chassis u chop i dcm T dcm T gear T diff F wh v ev Env. Battery i chop i arm e dcm shaft gear diff v ev F res m chop Objective: control the EV velocity Tuning variable: modulation ratio of the DC-DC converter

17 - Maximum Control Structure - 17 chopper DC machine gearboxes wheel chassis u chop i dcm T dcm T gear T diff F wh v ev Env. Battery i chop i arm e dcm shaft gear diff v ev F res m chop u chop-ref i dcm-ref T dcm-ref T gear-ref T diff-ref F wh-ref v ev-ref Maximum Control Structure: inversion of each element step-by-step all variables are assume measurable

18 - Inversion of chassis - 18 Battery F tran v ev chopper + DC 1/ Mmachine gearboxes wheel chassis s u chop i dcm T dcm F T gear T diff F res wh v ev Env. - v ev i chop i arm F res_mea m chop e dcm shaft v ev_mea gear diff v ev F res F wh_ref + + C(s) u chop-ref i dcm-ref - + T dcm-ref v ev_ref T gear-ref T diff-ref F wh-ref v ev-ref Maximum Control Structure: inversion of each element step-by-step all variables are assume measurable

19 - Inversion of wheel - 19 T diff F wh chopper DC 1 machine gearboxes wheel chassis Battery Ω diff i chop R wh u chop 1 R wh i arm i dcm e dcm T dcm v ev shaft T gear T diff F wh v ev Env. gear diff v ev F res m chop R wh T diff-ref u chop-ref i dcm-ref T dcm-ref F wh-ref T gear-ref T diff-ref F wh-ref v ev-ref Maximum Control Structure: inversion of each element step-by-step all variables are assume measurable

20 - Practical Control Structure - 20 chopper DC machine gearboxes wheel chassis u chop i dcm T dcm T gear T diff F wh v ev Env. Battery i chop i arm e dcm shaft gear diff v ev F res m chop u chop-ref i dcm-ref T dcm-ref T gear-ref T diff-ref F wh-ref v ev-ref Example of simplification: merging of gains k tot =k 1 k 2 k 3 k 4 merging

21 - Practical Control Structure - 21 chopper DC machine gearboxes wheel chassis u chop i dcm T dcm T gear T diff F wh v ev Env. Battery i chop i arm e dcm shaft gear diff v ev F res m chop v ev-est u chop-ref i dcm-ref T dcm-ref T gear-ref T diff-ref F wh-ref v ev-ref Example of estimation: estimation of velocity

22 - Simulation - 22 Matlab-Simulink, using the EMR library

23 - Implementation on the real vehicle - 23 Electronic Control Unit sensors

24 EMR 17 Lille June 2017 Summer School EMR 17 Energetic Macroscopic Representation «REFERENCES»

25 - References - 25 A. Bouscayrol, W. Lhomme, P. Delarue, B. Lemaire-S , S. Aksas, Hardware-In-the-Loop simulation of electric vehicle traction systems using Energetic Macroscopic Representation, IEEE-IECON'06, Paris, November A. Bouscayrol, A. Bruyère, P. Delarue, F. Giraud, B. Lemaire-S , Y. Le Menach, W. Lhomme, F. Locment, Teaching drive control using Energetic Macroscopic Representation - initiation level, EPE'07, Aalborg, September K. Chen, P. Delarue, A. Bouscayrol, R. Trigui, Influence of control design on energetic performances of an electric vehicle, IEEE-VPPC'07, Arlington (U.S.A.), September K. Chen, A. Bouscayrol, W. Lhomme, EMR and Inversion-based control: application to an Electric Vehicle with an electrical differential, Journal of Asian Electric Vehicles, vol. 6, no.1, p , June J. P. Trovao, M. R. Dubois, M. A. Roux, E. Menard and A. Desrochers, Battery and SuperCapacitor Hybridization for a Pure Electric Three-Wheel Roadster, IEEE-VPPC 15, Montreal, QC, October C. Depature, W. Lhomme, A. Bouscayrol, Teaching Electric Vehicle drive control using Energetic Macroscopic Representation, EVS 27, Barcelona (Spain), November W. Lhomme, Ph. Delarue, Ph. Barrade, A. Bouscayrol, Maximum Control Structure of a series hybrid electric vehicle using supercapacitors, EVS'21, Monaco, April 2005.

26 26 «BIOGRAPHIES»

27 - Authors - 27 Dr. Ali CASTAINGS University Lille 1, L2EP, France PhD in Electrical Engineering at Univ.Lille1 (2016) Research topics: Energy management of multi-sources vehicles, HIL simulation, Optimal control Dr. Walter LHOMME University Lille 1, L2EP, MEGEVH, France PhD in Electrical Engineering at University of Lille1 (2007) Research topics: EMR, HIL simulation, EVs and HEVs, Energy Storage Subsystem, Traction subsystems,