CELL VEHICLE» Graz University of Technology (Austria) April 2012

Transcription

1 Graz University of Technology (Austria) April 2012 «Energy Management of complex systems Energetic Macroscopic Representation» «FUEL CELL VEHICLE» L. Gauchia*, A. Bouscayrol,** J. Sanz*, R. Trigui*** and P. Barrade**** * Power Electric Dpt., University Carlos III of Madrid, Spain ** L2EP, University of Lille1, France *** IFSTTAR, France **** LEI, EPFL, Switzerland lgauchia@ing.uc3m.es ²

2 - Outline - 2 Interest on vehicles V topologies V with EMR Simulation results Practical implementation Conclusions

3 - Interest on V - Internal combustion engine vs. electric propulsion 3

4 - Interest on V - Internal combustion engine vs. electric propulsion 4 Batt Batt+SC +SC +Batt+SC

5 - Interest on V - Internal combustion engine vs. electric propulsion 5 RAGONE DIAGRAM Specific energy (Wh/kg) Fuel cell ICE Li batteries Pb batteries Ni batteries Supercapacitors Specific power(w/kg)

6 Hybrid energy system topology - V topologies - 6 / B SC Inv + EM / B / SC Inv + EM / / B SC Inv + EM / / B / SC Inv + EM

7 Hybrid energy system topology - V topologies - 7 BAT / EM Vve Fres / SC /

8 EMR basics «EMR an Fuel Cell Vehicle» - V with EMR - 8 ES Action reaction principle i p=v i v 1 V i 1 Respect causality i 2 Allows to methodically: Model multi-physical elements Design control schemes Design control strategies Inversion principle ES [Bouscayrol 00]

9 Fuel cell representations - V with EMR - 9 Anode: Cathode: H H Global reaction: e 1 O2 2H 2e H 2O 2 H O H 2 2O electricity heat

10 Fuel cell representations - V with EMR - 10 Air circuit 25% Humidification circuit 5% Cooling circuit 3% Hydrogen circuit <2% Net electrical power 65% Hissel

11 Fuel cell representations - V with EMR - 11 Passive non-controllable system i + u u i d dt p RT H 2 V an r q H2 NI 2F out q H2 k H2 p H2 in q H2 out q H2 r q H2 - E E 0 RT 2F ln p H 2 p O2 p H2 O [Gauchia 09]

12 Fuel cell representations - V with EMR - 12 Simplified controllable system q H2 in p H2 H2 q H2 ref p H2 q O2 in q H2 r p O2 E u u O2 i i i q O2 ref p O2 q O2 r Tº ΔS u dl i dl Tº Controlled to fulfill objective: Avoid O2 starvation Maximum efficiency

13 - V with EMR - Fuel cell model and representations 13 More complex controllable system u i [Boulon 10]

14 Energy storage representations - V with EMR - 14 Battery ibatt + BATT ubatt Ubat - ibatt Supercapacitors ibatt + Ubat ubatt - - SC usc isc isc + USC - [Gauchia 09]

15 - V with EMR - 15 Energy generation Parallel coupling - converter machine Gearbox Differential. Wheels Chassis Environment T l_wh F l_wh BAT i batt i 1 i total m 1 U chop_dcm i dcm i dcm e dcm T dcm Ω gear T gear Ω dif l_wh T r_wh v l_wh F r_wh F tot v ev v ev F res ENV U fc i fc i chop_fc Ω r_wh v r_wh SC i fc U sc U ind_fc i sc m 2 i chop_sc i total i sc U ind_sc m 3 EM v ev F res U fc + = U sc + =

16 - V with EMR - 16 Energy generation Parallel coupling - converter machine Gearbox Different. Wheels Chassis Environment T l_wh F l_wh BAT i batt i 1 i total m 1 U chop_dcm i dcm i dcm e dcm T dcm Ω gear T gear Ω dif l_wh T r_wh v l_wh F r_wh F tot v ev v ev F res ENV U fc i fc i chop_fc Ω r_wh v r_wh SC i fc i sc U sc U ind_fc i chop_sc U ind_sc m 3 i sc m 2 Vehicle speed control Objective variable: v ev Tuning variable: m 1 Constrain variables: none if machine

17 - V with EMR - 17 Energy generation Parallel coupling - converter machine Gearbox Different. Wheels Chassis Environment T l_wh F l_wh BAT i batt i 1 i total Ubatt m 1 U chop_dcm i dcm i dcm e dcm T dcm Ω gear T gear Ω dif l_wh T r_wh v l_wh F r_wh F tot v ev v ev F res ENV U fc i fc i chop_fc Ω r_wh v r_wh i fc U ind_fc m 2 SC U sc i sc i sc U ind_sc i chop_sc m 3 e dcm mes i dcm mes T r_wh ref F r_wh ref v ev mes F res mes F tot ref mes U chop_dcm ref i dcm ref T dcm ref T gear ref kw T l_wh ref F rlwh ref k r v ev ref [Bouscayrol 06]

18 - V with EMR - 18 Energy generation Parallel coupling - converter machine Gearbox Different. Wheels Chassis Environment T l_wh F l_wh BAT i batt i 1 i total m 1 U chop_dcm i dcm i dcm e dcm T dcm W gear T gear Ω dif l_wh T r_wh v l_wh F r_wh F tot v ev v ev F res ENV U fc i fc i chop_fc Ω r_wh v r_wh SC i fc U sc U ind_fc i sc m 2 i chop_sc i sc U ind_sc m 3 Hybrid energy supply

19 - V with EMR - 19 Energy generation Parallel coupling Drive + mechanical system Which are the objectives?? BAT i batt i 1 i total ES Pref There seems to be no global objective Only local objectives respecting parallel coupling U fc i fc i chop_fc i total i bat i chop _ fc i chop _ sc SC i fc U sc U ind_fc i sc m 2 i chop_sc Example of objective variables: i sc U ind_sc m 3 Fuel cell operation point Hybrid energy supply SC chopper current Constrains: none (will depend on control opportunities) Tuning variables: m 2, m 3

20 Energy generation Parallel coupling - V with EMR - 20 BAT ES Control i fc, i sc_chop i batt i 1 i total U fc i fc i chop_fc Pref SC i fc U sc U ind_fc i sc m 2 i chop_sc i sc U ind_sc m 3 U sc mes Ubatt mes i sc mes isc ref U ind_sc ref U fc mes m i 2 fc mes i sc_chop ref i fc ref U ind_fc ref i ESS ref Strateg y [Gauchia 11]

21 Strategy Generate current references for fuel cell and SC chopper Ways to distribute energy: Fuel cell works at its most efficient point Always at partial loads SC manage the higher frequency loads Limitations: Battery Voltage State-of-charge (SoC) Charge and discharge current Supercapacitor Voltage Charge and discharge current Fuel cell Current Current slew-rate «EMR an Fuel Cell Vehicle» - V with EMR - P (W) Efficiency Voltage I (A) I (A) 21

22 - Simulation results v (km/h) P total (W) t (s)

23 - Simulation results - 23 I chop fc (A) I battery (A) I chop sc (A) t (s)

24 - Simulation results SoC battery (%) SoC SC (%)

25 - Conclusions - 25 EMR is a useful tool for design, modeling and control of fuel cell vehicles. It allows to acquire a methodology, known and repeatable. EMR also allows to study more in-depth the control schemes and energy strategies

26 - Future works- 26 Study strategies for minimization of fuel consumption Evaluate effect of strategy on life-cycle and consumption Search optimization criteria for energy management

27 - References - [Boulon 10] L. Boulon, D. Hissel, A. Bouscayrol, and M.-C. Péra, From modelling to control of a PEM fuel cell using energetic macroscopic representation, IEEE Trans. Ind. Electron., vol. 57, no. 6, June [Bouscayrol 00] A. Bouscayrol, B. Davat, B. de Fornel, B. François, J.P. Hautier, F. Meibody-Tabar and M. Pietrzak-David, Multimachine multiconverter system: application for electromechanical drives, Eur. Phys. J, Appl. Phys., vol. 10, no. 2, pp , May [Bouscayrol 06] 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 [Gauchia 09] L. Gauchia, Nonlinear dynamic per-unit models for electrochemical energy systems. Application to a hardware-in-the-loop simulation. Doctoral Dissertation on Electrical Engineering. Advisor: Dr. J. Sanz, University of Carlos III, Madrid, Spain, December [Gauchia 11] L. Gauchia, A. Bouscayrol, J. Sanz, R. Trigui, and P. Barrade, Energetic macroscopic representation of a fuel cell-battery-supercapacitor hybrid electric vehicle, IEEE VPPC, Chicago, EEUU, 2011 (Accepted for presentation) [Thounthong 09] P. Thounthong, S. Raël, and B. Davat, Energy management of fuel cell/battery/supercapacitor hybrid power source for vehicle applications, Journal of Power Sources, vol. 193, pp ,