backstepping control of a fuel c vehicle using emr» - backstepping co… · 08/07/2016 1 emr’16...

08/07/2016

1

EMR’16

UdeS - Longueuil

June 2016

Summer School EMR’16

“Energetic Macroscopic Representation”

«BACKSTEPPING CONTROL OF A FUEL CELL VEHICLE

USING EMR»

C. DEPATURE 1,2, Dr. W. LHOMME1, Prof. P. SICARD2,

Prof. A. BOUSCAYROL1, Prof. L. BOULON 2

1L2EP, Université Lille1, MEGEVH network, France2GREI, Université du Québec à Trois-Rivières, Canada

EMR’16, UdeS, Longueuil, June 20162

« Backstepping control of a FC vehicle using EMR »

FC + PE

ubus

ibus

Tim

Ωgear

its

ufc

ifc

Safe energy management: stable control

traction power

SC+ PE usc

isc

supercapacitor power

fuel cell power (f(difc/dt)

- Fuel cell vehicle structure -

time (s)

power (pu)

• Important driving

range

• No local emission

• Power

transients

08/07/2016

2



- EMR and Inversion-Based Control (2000) -

system model

assumptions energetic approach

Local / Global

Control

Representation

(integral causality)

inversion

• Graphical tool.

• Energy management and real time applications.

• Global stability not demonstrated.

strategy

electromechanical thermal

Peugeot 3008 HY4, … DW10, … Ballard FC, …

electrochemical piezo-electric

Stimtac Standalone



• Mathematical tool.

• Tracking control. Non linear systems. No EV and HEV application.

• Ensure a stable control.

assumptionsenergetic approach : stability

criterion (Lyapunov)

system model stable control

- Backstepping : step by step iterative procedure (1990) -

State representation

(Heuristic)

robotics

Linear IM autonomous vehicle “Red Rover”

chaotic

Duffing oscillator

electromechanics

08/07/2016

3



- Objective -

• Deduce a stable control of a Fuel Cell/ Supercapacitor vehicle.

It is possible to use EMR and Backstepping?

Based on the Tazzari Zero



- Outline -

1. Modelling and Representations

2. Backstepping control

3. Simulation

4. Conclusion and perspectives

08/07/2016

4

EMR’16

UdeS - Longueuil

June 2016



« 1. MODELLING AND REPRESENTATION»



ubus

is

Tim

Ωgear

its

ufc

ifc

usc

isc

- EMR of the FC vehicle -

ifc

isc

ufc

usc

FC

SC

is

ubus

ihfc

ihsc

ubus

ubusuhsc

uhfc

ifc

isc

mhsc

mhfcits

trac.

ubus

ubus

its

Traction Subsystem

TS equivalent current source

voltage (V)

current (A)

itot1

usc Csc

rsc isc speed (m/s)

force (N)

08/07/2016

5



- State representation of the FC vehicle -

ubusis

ihfcifc

uhfc

mhfc

ihscisc

mhsc

uhsc

scfcscfcbusschfcscfc

scfc

scfc

schfcschfcscfc

hschfcs

tss

bus

bus

irumuL

idt

d

imi

iii

iiC

udt

d

,,,,

,

,

,,,

1

1

Tunning path from EMR State representation

3 state variables : ubus, ifc, isc

2 tuning variables : mhfc,mhsc

2 cascade loops : ihfc, ihsc

ifc

isc

ufc

usc

FC

SC

is

ubus

ihfc

ihsc

ubus

ubusuhsc

uhfc

ifc

isc

mhsc

mhfcits

trac.

ubus

EMR’16

UdeS - Longueuil

June 2016



« 2. BACKSTEPPING CONTROL»

08/07/2016

6



- Backstepping control of the FC vehicle -

1. External loop control law : dc bus voltage loop

busrefbus uue 1error e1

Stability

criterion as

dV1/dt ≤0

1st local control

law

211111

211

2

1

ecedt

deCV

dt

d

eCV

bus

bus

tsrefbusbusrefs iudt

dCeci 11

energetic approach first feedback gain

to solve dV1/dt ≤0

111

1 Ptsrefbusrefs CeiuPi

is

ubus its

trac.

ubus

CP1

P1-1

ubus

strategyubus-ref

its

is-ref

e1+

-

+

+



2. Parallel connection and boost choppers

ihfc and ihsc are mutually considered themselves as perturbations

Solution : Inversion Based Control rules.

coupling repartition

refrechrefhfc

refrechrefsrefhsc

ii

iii

schfc

refschfc

refscfcm

ii

,

,

,

conversion direct inversion


is

ubus

ihfc

ihsc

ubus

ubusuhsc

uhfc

isc

mhsc

mhfcits

trac.

ubus

CP1

P1-1

ubus

Thermostat

strategy

usc

ubus-ref

its

is-ref

%

Xihfc-ref

e1+

-

+

+

-

irech-ref

%

Xihsc-ref

mhfc

mhsc

ifc-ref

isc-ref

ifc

08/07/2016

7



3. FC and SC current loops


scfcrefscfc iie ,,3,2 error e2,3

Stability

criterion as

dV2,3/dt ≤0

4th local control law

2

3,23,23,23,2,13,2

2

3,213,22

1

ecedt

deLV

dt

dV

dt

d

eLVV

scfc

fc

3,23,2,3,2,,,,,

1ecueiri

dt

dL

um scfcrefscfcscfcrefscfcscfc

bus

schfc

energetic approach

feedback gain

3,23,2,

1

3,2,, /1 Prefscfcscfcbusschfc CeiPuum



- Control structure analysis -

ifc

isc

ufc

usc

FC

SC

ifc

ufcifc-ref

ubus

-

3 proportional controllers CP1, CP2, CP3 + 3 direct inversions : P1-1, P2

-1, P3-1

2 control inputs: ubus-ref, irech-ref 2 control ouputs: mhfc, mhsc

CP2

P2-1

%

X

+e2-

+

is

ubus

ihfc

ihsc

ubus

ubusuhsc

uhfc

isc

mhsc

mhfcits

trac.

ubus

CP1

P1-1

ubus

Thermostat

strategy

usc

ubus-ref

its

is-ref

%

X ihfc-ref

e1+

-

+

+

-

irech-ref

%

X

mhfc

mhsc

CP3

P3-1

%

X

+e3-

+

ubusmhsc

mhfc

ihsc-ref

uhfc-ref

uhsc-ref

usc

isc-ref

ifc-ref

08/07/2016

8

EMR’16

UdeS - Longueuil

June 2016



« 3. SIMULATION »



- Specifications -

irech-ref / mhfc

usc usc-min

300 A

0 A usc-max

2 strategy levels

1. Bus voltage : ubus-ref=80 V (supply voltage of the traction of

the Tazzari Zero)

2. SC recharge : Thermostat strategy

Fuel Cell vehicle parameters

Fuel Cell 78-55 V, 20 kW

Supercapacitor 54 V, 130 F

Smoothing inductors 5.5 mΩ, 0.25 mH

dc bus capacitor 80 V, 53 mF

Electric drive 15 kW

Vehicle 811 kg

Feedback gains c1 = 0.62, c2,3 = 0.13

08/07/2016

9



- Matlab Simulink Simulation -

Fixed step at 1 ms using continuous derivatives.

EMR library

Simulink

diagram blocs



- Simulation results -

vehicle speed vev (m/s)

power (kW)

voltage ufc usc (V)

time (s)

Pfc

Psc

Pbus

ufc

usc

FC OFF FC OFF FC ON

(a)

(b)

(c)

Current ifc (A)

current isc (A)

time (s)

ifc-ref

ifc

isc-ref

isc

voltage ubus (V)

ubus-ref

ubus

(a)

(b)

(c)

Energy management Voltage regulation

08/07/2016

10

EMR’16

UdeS - Longueuil

June 2016



« 4. CONCLUSION AND PERSPECTIVES »



- Conclusion -

FC vehicle

model

assumptions

EMR

State

representation

Stable control

+ energy

management

causality

Lyapunov

inversion

tuning

path

strategy

08/07/2016

11



itot1

ubus iim2

Tim

gear iim1

its ibus ich1

uch1

Lsc

usc

iLsc

Lfc

ufc

iLfc

uch2

ich2

Rb

iRb

iESS

uRb

Perspective : application in real time -

• Definition of stability rules : EMR control formalisation.

• Reduced scale Hardware in the loop simulation.

• Filtering strategy

• Taking into account the perturbation (adaptive Backstepping)



- Authors -

Prof. Alain BOUSCAYROL

University Lille 1, L2EP, MEGEVH, France

Coordinator of MEGEVH, French network on HEVs

PhD in Electrical Engineering at University of Toulouse (1995)

Research topics: EMR, HIL simulation, traction systems, EVs and HEVs

Clément Dépature


Université du Québec à Trois Rivières, GREI, Canada

PhD student in Electrical Engineering at University Lille1 and UQTR (2013)

Research topics: Formalization of control systems , EVs and HEVs

Dr. Walter Lhomme


PhD in Electrical Engineering at University Lille1 (2007)

Research topics: modelling, control and energy management for

hybrid and electric vehicles.

08/07/2016

12



Prof. Pierre Sicard

Université du Québec à Trois-Rivières, GREI

PhD in Electrical Engineering at Rensselaer Polytechnic Institute (1993)

Research topics: Controller and observer design for nonlinear systems,

control of power electronics and multidrive systems, adaptive control, and neural

networks.

Prof. Loïc Boulon

Université du Québec à Trois-Rivières, GREI, IRH

PhD in Electrical Engineering at University of Franche-Comté (2009)

Research topics: hybrid electric vehicles, energy and power sources,

and fuel-cell systems.



- References -

• A. Veziroglu, and R. Macario, “Fuel cell vehicles: State of the art with economic and

environmental concern”, International Journal of Hydrogen Energy, vol. 36, no. 1, pp. 25–

43, Jan. 2011.

• A. Khaligh and L. Zhihao, “Battery, Ultracapacitor, Fuel Cell, and Hybrid Energy Storage

Systems for Electric, Hybrid Electric, Fuel Cell, and Plug-In Hybrid Electric Vehicles: State

of the Art,” IEEE Trans. Veh. Technol., vol. 59, no. 6, pp. 2806–2814, Jul. 2010.

• A. Payman, S. Pierfederici, F. Meibody-Tabar, and B. Davat, “An Adapted Control Strategy

to Minimize DC-Bus Capacitors of a Parallel Fuel Cell/Ultracapacitor Hybrid System,”

IEEE Trans. Power Ele., vol. 26, no. 12, pp. 3843–3852, Aug. 2009.

• J. Jia, Q. Li, Y. Wang, Y. T. Cham, and M. Han, “Modeling and Dynamic Characteristic

Simulation of a Proton Exchange Membrane Fuel Cell,” IEEE Trans. Ener. Conv., vol. 24,

no. 1, pp. 283–291, Jan. 2009.

• A. Y. Alanis, E. N. Sanchez, and A. G. Loukianov, “Real-time Discrete Backstepping

Neural Control for Induction Motors,” IEEE Trans. Control Syst. Tech., vol. 19, no. 2, pp.

359–366, Feb-2010.

• H. El Fadil, F. Giri, J. M. Guerrero, and A. Tahri, “Modeling and Nonlinear Control of a Fuel

Cell/Supercapacitor Hybrid Energy Storage System for Electric Vehicles”, IEEE Trans. on

Vehicular Technology, vol. 63, no. 7, pp. 3011-3018, Sep. 2014.

• M. Rajabzadeh, S. MohammadTaghi Bathaee, and M. Aliakbar Golkar, “Dynamic

modeling and nonlinear control of fuel cell vehicles with differents hybrid power sources”,

International Journal of Hydrogen Energy, vol. 41, no. 30, pp. 3185-3198, Jan. 2016.

backstepping control of a fuel c vehicle using emr» - backstepping co… · 08/07/2016 1 emr’16...

Documents