Hybrid Electric Vehicles Energy

7/27/2019 Hybrid Electric Vehicles Energy

1/11


2/11

TANI et al.: DC/DC AND DC/AC CONVERTERS CONTROL FOR HEVS ENERGY MANAGEMENT-ULTRACAPACITORS AND FC 687

Fig. 2. Dynamic model of the ultracapacitors.

MATLABSIMULINK software is used. To validate the pro-posed method for the energy management, an experimental testbench is carried out in the reduced scale. This system includesa module of the ultracapacitors, a FC emulator, and the electricmachines (ac-motor/generator with dc-motor) as a reversibleload. The ultracapacitors module is linked to the dc-bus viaa buck-boost converter which ensures the energetic exchange

between the ultracapacitors and the load. The FC emulator isconnected to the dc-bus using a boost converter for the dc-busvoltage management. The control of these converters dependson the energy management method between the hybrid sources(ultracapacitors/FC) and the requested or supplied energy bythe asynchronous machine.

II. ULTRACAPACITORS AND FC MODELING

A. Ultracapacitors Modeling

To use the ultracapacitors ( ) as energy storage devices inthe hybrid electric vehicles (HEVs), it is necessary to associateseveral cells in series to obtain a high voltage level due to ul-tracapacitor cell voltage of 2.7 V. The used model of the is

presented in Fig. 2. This model includes an internal resistance, and an equivalent capacitor . This capacitor includes

two components, the first component varies linearlywith the ultracapacitors module voltage, and the second compo-nent is a constant capacitor [9], [10]. The analytical modelof the module is presented in (1), where defines the sign of the

current. In this equation, if the ultracapacitors are inthe discharge operations, and for the charge operations.The estimation method of the used parameters is presented in

[10]

(1)

This model describes the dynamic behavior of the ultracapac-itors during the charge and the discharge operations [8]. The es-timated parameters for the BOOST-CAP3000F module are pre-sented in Table I.

To validate the model of the ultracapacitors, a module of eightcells in series is realized. This module is charged into maximumvoltage of 22 V, and discharged with a constant current. Thesimulation and experimental results obtained forare compared in Fig. 3.

TABLE IULTRACAPACITORS MODULE PARAMETERS

Fig. 3. Ultracapacitors model validation.

B. FC Modeling

The FCs are the electrochemical devices that directly con-vert the chemical energy of the Fuel into electricity. The energyis released whenever the hydrogen reacts chemically with theoxygen of the air [11]. In the case of the proton exchange mem-

brane FCs (PEMFCs), which are the focus of the most researchactivities today, the only byproduct is the water and the heat.The PEMFCs technologies are the best candidate among otherFC technologies,due to the low operating temperature, the smallsize, the lightweight, the high-power density, and the relativelyshort time start-up [12], [13]. In this paper, the PEMFC modelis used, and the operations of this one is based on the followingelectrochemical reaction

Electrical Energy+Heat (2)

Many models of the FC can be founded in the literature suchas in [13], [14], [15], [16], and [17]. These models are generally

based on the voltage and current analysis which allows to es-tablish the model of the PEMFC. The PEMFC terminal voltagecan be defined as expressed in (3), where is the thermo-dynamic potential of the cell, is the voltage drop due to theactivation of the anode and the cathode, is the voltage dropdue to series resistance, presents the voltage drop due tothe concentrations, and is the cell number in series

(3)

The thermodynamic potential of the cell and the voltage dropdue to the activation of the anode, and the cathode are respec-tively given in (4) and (5). In these equations, is the celltemperature in Kelvin, and present the pressures in


3/11

688 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 9, NO. 2, MAY 2013

atmosphere of the hydrogen and the oxygen, respectively.is the FCs current; is the parametric coefficients [18], [14]

(4)

(5)

In (5), is the concentration of the oxygen. This concentra-tion can be calculated using the following equation [18]:

(6)

The and components are given in (7) and (8), whereis the current density in , is a constant parameter

extracted in [15], [14]

(7)

(8)

In (7), is the electron flow resistance which is approxi-mately constant, and is the protons flow equivalent resis-tance which is expressed in (9). In this equation, is the thick-ness of the polymer membrane in centimeters, is the activecell area in .

(9)

In this equation, is the specific membrane resistivity for theflow of the hydrated protons in cm. This parameter can beestimated as presented in (10), where presents the membraneshumidity ratio [18]. The used parameters of the FC model are

presented in Table II

(10)

III. DC/DCDC/AC CONVERTERS AND ELECTRIC

MACHINES MODELING

A. Buck-Boost Converter Modeling

To establish a model of the buck-boost converter illustratedFig. 4, it is necessary to analyze the buck and the boost op-erations. During the boost operations, semiconductor isswitched and is in OFF position. In this condition, theultracapacitors module provides energy to the dc-bus. In buckmode, is switched and become inOFF position. So theultracapacitors receives the energy from the dc-bus

(11)

TABLE IIFC PARAMETERS

The resulting analytical model is given in (11), where is thesign of the current, and is the equivalent value of the con-verter duty cycle[10]. In this equation, and present

respectively the boost and the buck converters duty cycles.

B. Boost Converter Modeling

By an analogy to the buck-boost converter, the analyticalmodel of the boost converter is presented in (12), where

presents the converter duty cycle [8]

(12)

C. DC/AC Converter Modeling

The dc/ac converter (inverter) presented in Fig. 4 includessix bidirectional semiconductors (IGBT). The six transistors

are in the anti-parallel configuration withsix diodes. The analytical model of the inverter must be estab-lished from the sequences analysis of the converters operation.These sequences are due to logical signals switching basedon the following conventions:

is and isis and is

(13)

The resulting analytical model of the inverter is presented in(14) for the voltage, and the corresponding current is expressedin (15)

(14)

(15)

D. Electric Machines Modeling

The analytical model of the asynchronous machine is given in(16), where , , , and are respectively the equivalent

currents in the stator and the rotor, and are respectivelythe stator and the rotor resistances. In this equation, and


4/11


Fig. 4. Hybrid system topology for the embedded energy management.

present the pulsations of the flux in the stator and the rotor, re-spectively. and present the dq components of the fluxin the stator; is the rotorflux in direction of axis

(16)

The equations of the flux are given in (17), where andare the inductances in the stator and the rotor, is the mutualinductance

(17)

The pulsation in the stator is given in (18), where is thenumber of pair of pole, and is the mechanical speed in rad/s

(18)

To emulate the HEV behavior during the deceleration and thebraking operations, a dc-motor is used to drive the asynchronousmachine (MG). The analytical model of the dc-motor is givenin (19), where is the internal resistance, is the internalinductance of the motor, and is the dc-motor EMF

(19)

In this equation, is the buck converter duty cycle, andpresents the dc-motor terminal voltage.

IV. HYBRID SYSTEM CONTROL METHOD

The originality of this paper is focused on the bidirectionalload (motor and generator operations) power sharing betweenthe FC and the ultracapacitors, using the New European DriveCycle (NEDC) and the polynomial control technique. The pro-

posed control method compared to others methods enables toallocate the average power to the FC and the fluctuating power

(due to the acceleration, the deceleration, and the braking opera-tions) to the ultracapacitors. In other terms, the proposed methodtakes into account the dynamic characteristics of the ultracapac-itors and the FC, which allows improving the energetic perfor-mances and the life time of the sources.

The polynomial control technique presents a robust algorithmwith good performance in the following situations. When thesystem has a pure delay or presents a dynamic characteristicwhich changes during operations. The polynomial control tech-nique is also interesting if the reference should not be exceeded.The polynomial controller is an interesting alternative solutionto the conventional PI controller. In other words, the polyno-

mial controller presents the better performances in term of therapidity and the robustness compared to conventional PI con-troller. The polynomial controller improves the disturbance re-

jection. For more information, a comparative study of the PIand the polynomial controller is presented in [10]. More detailsabout the polynomial control technique can be found in [19].

A. Polynomials Coefficients Estimation Method

The degree of , , and polynomials arefixed according to the degree of the discrete system transferfunctions . The estimation method of the polynomialscoefficients is based on the closed-loop analysis as illustrated inFig. 5 for the current, and the dc-bus voltage management.The transfer functions of the discrete systems are presented in(20), where , and , present


5/11


Fig. 5. Polynomials coefficients estimation diagrams.

the denominators and the numerators of the transfer functions,is the dc-bus voltage smoothing capacitor, is the ul-

tracapacitors current smoothing inductance, is the samplingperiod

(20)The simplified closed loop transfer function for the ultracapac-itors current control is expressed as follows:

(21)The desired polynomial in the closed loop is presented in (22)

(22)

To reduce the number of the parameters to be identified, the usedmethod consists to choose and identical. Theseones are identified, using a simple comparison between the de-sired polynomial and the denominator of the transferfunction in closed loop as presented in (23)

(23)

(24)

The resulting coefficients from this comparison are given in

(24), where the and depend of the dynamic response ofthe system in closed loop.These coefficients can be obtained using the following de-

sired polynomial, where is the system control bandwidth

(25)

B. DC-Bus Voltage Control Method

To manage the dc-bus voltage, two control loops are nec-essary. The first is the current feedback (inner loop), and thesecond is the voltage loop (outer loop) as illustrated in Fig. 6. Ingoal to obtain a minimal static error with disturbance rejection,the following polynomials correctors: , ,

, , , and are selectedas expressed in (26)

(26)

The final coefficients obtained from the closed loops analysis,in the case of the FC current and the dc-bus voltage control arerespectively expressed in (27) and (28). In these equationsis the dc-bus voltage smoothing capacitor, is the FCs cur-rent smoothing inductance, is the sampling period, is thecurrent control bandwidth, is the converter control frequency

(27)

(28)

The dc-bus voltage control law established from (12) is given in(29). To control the dc-bus voltage, the cascaded control loopsare necessary: an inner loop for the FCs current control and theouter loop for the dc-bus voltage ones

(29)

The FC reference current estimated from electric powersbalances between the boost converter input and output is pre-sented in (30)

(30)

In this equation, is the boost converter output current,and present the dc-bus voltage control loop output signal.This last one corresponds to estimate instantaneous dc-bus ca-

pacitors current.

C. Ultracapacitors Current Control Method

To control the ultracapacitors current, the polynomial controlmethod presented in Fig. 7 is used. In this case, the cascadedcontrol loop is not necessary, because, the control is focused di-rectly on the ultracapacitors current. To obtain a minimal staticerror with disturbance rejection, the , and

are selected as expressed in (31), where andestimation method is same to that presented in (27)

(31)

To control the ultracapacitors current, the bidirectionalconverter control laws obtained from the buck-boost convertermodeling are used. These control laws are presented in (32)for the buck operations, and in (33) for the boost mode. These


6/11


Fig. 6. DC-bus voltage control method.

Fig. 7. Ultracapacitors current control loop.

Fig. 8. Ultracapacitors reference current estimation method.

control laws are compared to triangular waveform to modulatethe PWM signals for the buck and the boost operations [8]

(32)

(33)

The reference current of the ultracapacitors is estimatedusing (34), where is the FC rated current. The corresponding

diagram is illustrated in Fig. 8. This method enables to allocatethe dynamic power of the load to ultracapacitors, and the av-erage power to the FC. In other words, this method takes intoaccount the conventional dynamic behavior of the ultracapaci-tors and the FC [20]

(34)

D. Speed Control of the Electric Machine

To control the asynchronous machine speed, the used methodis based on indirect rotorflux orientation control. This method isconventionally obtained from the following assumption:

and . The pulsation in the rotor is estimated as

Fig. 9. Speed control of the asynchronous machine.

expressed in (35). To estimate the rotorflux in the direction ofaxis, (36) is used [21]

(35)

(36)

Theused method for thespeed control is illustrated in Fig. 9. Forthis method, two cascaded control loops are necessary. The firstis the current feedback (inner loop), and the second is the speedcontrol loop (outer loop). To obtain a minimal static error withdisturbance rejection for the speed control, the ,

and are selected. These polynomialscorrectors are same to that presented in (26).

The final coefficients obtained from the closed loops analysis,in the cases of currents, the speed and the flux control are re-spectively expressed in (37), (38), and (39). In these equations,


7/11


TABLE IIIPARAMETERS OF THE VEHICLE

is the sampling period, is the current control bandwidth,is the speed control bandwidth, is the flux control band-

width, is the moment of inertia, is the friction coefficient

(37)

(38)

(39)

The PWM signals are generated by comparing three signals (ob-tained from the two to three phases park transformation) to atriangular waveform. The resistive torque which must be com-

pensated by the vehicle to move forward is presented in (40).In this equation, is the grade of the road in degree, is thevehicles speed in , and is the radius of the wheel in .These parameters are presented in Table III

(40)

In this paper, the road is assumed flat, i.e. . Using (40), thedynamic equation can be expressed as given in (41), where

is the electromagnetic torque

(41)

E. DC-Motor Speed Control

To control the dc-motor speed, the control strategy presentedin Fig. 10 is used. To obtain a minimal static error with distur-

bance rejection , and polyno-mials are selected as expressed in the following:

(42)

Fig. 10. DC-motor speed control during the deceleration and the braking op-erations.

Fig. 11. Measured speed compared to its reference.

To generate the reference speed for the dc-motor, the fol-lowing equation is used:

(43)

The conditions for the dc-motor control are focused on the signof as presented in the following:

DC-motor is no controlledDC-motor is controlled

(44)

During the deceleration and the braking operations of the HEV,, the dc-motor is controlled from the

buck converter so that its speed become higher than .During the acceleration and the constant speed operations, i.e.

, the asynchronous machine is controlledfrom the inverter as illustrated in Fig. 9.

V. SIMULATION AND EXPERIMENTAL VALIDATIONS

A. Test Conditions

For the hybrid system simulations, the dc-bus voltage ref-erence is fixed to 47 V and the used value of thein the simulation and experimental tests is fixed to 5 A. Theused parameters for the hybrid system simulations are givenin Tables IIV. Fig. 11 presents the control result of the elec-tric machine speed using the NEDC. This figure shows that, themeasured speed is very close to its reference. The correspondingloads current profile obtained from the HEVs behavior simula-tion is plotted in Fig. 12.

The speed control result obtained from the motor and the gen-erator operations conditions is illustrated in Fig. 13. The acceler-ation and the constant speed situations correspond


8/11


Fig. 12. Loads current profile.

TABLE IVUSED PARAMETERS FOR THE HYBRID SYSTEM CONTROL

Fig. 13. Speed control result obtained from the motor and the generator oper-ations conditions.

to motor operations. The deceleration and the braking opera-

tions correspond to generator mode.

B. Experimental Setup

An experimental test bench is carried out to validate the pro-posed control methods outlined above. The developed experi-mental test bench includes an ultracapacitors module (18 cellsin series with a maximum voltage of 49 V), a programmabledc-source is used as the FC system with a maximum power of1 kW (25 V/40 A), a buck-boost converter, a boost converter, aninverter, and the asynchronous machine coupled to a dc-motor.These electric machines are used as a bidirectional load (motorand generator operations). The dc-motor is connected to a buckconverter. This converter is controlled during the decelerationand the braking operations. The proposed control algorithms

Fig. 14. DC-bus voltage control result.

Fig. 15. Measured current on the load during the simulation and the experi-mental tests.

for the embedded energy management are implemented in twoPIC18F4431 microcontrollers. The used parameters for the hy-brid system control are presented in Table IV.

C. Simulation and Experimental Results

The dc-bus voltage control result is plotted in Fig. 14. Thisfigure shows that, the experimental and the simulation curvesare close to reference voltage regardless of the loads demand.In other words, the proposed dc-bus voltage control method issatisfactory. The measured current on the load during thesimulation and the experimental tests are plotted in Fig. 15. Thiscurrent presents four steps. The first step presents the acceler-ation operations which are characterized by the loads currentincreasing when the HEVs speed increase. The second step ischaracterized by the constant speed. During this step, the loads

current is constant, and its value is small. The third step cor-responds to the deceleration operations. During this step, theloads current presents the negative peaks current (energy re-covery operations). The last step is characterized by a null speedwhich corresponds to zero current of the load. During this laststep, the dc-bus voltage presents some variations around its ref-erence (48 V) as illustrated between the 0 and 1000s of theFig. 14.

The contribution of the ultracapacitors in the dc-busis plotted in Fig. 16. This contribution has the same shape asthe loads current, and it presents two steps during the tractionoperations (asynchronous machine in the motor mode): the firstoperation corresponds to negative current of the due tolow current of the load compared to the FC contribution; thesecond operation corresponds to positive current of the .


9/11


Fig. 16. Contribution of the ultracapacitors in the dc-bus.

Fig. 17. Measured current in the dc-bus from the FC.

During this operation the ultracapacitors module contributes tothe traction energy supply via the boost converter.

During the energy recovery operations (asynchronous ma-

chine in the generator mode), the contribution of the ultracapac-itors , and the measured current on the load are negative.So, the ultracapacitors module recovers the energy from the FCand the load.

The simulation and the experimental results of the FC con-tribution in the dc-bus are plotted in Fig. 17. This con-tribution is always positive, and it presents less variation com-

pared to the loads current variations. In other words, the ultra-capacitors ensures the dynamic components of the load, and theFC provides the average components. In consequently, the lifetime and the size of the FC are improved. Fig. 18 presents themeasured current on the ultracapacitors. This current has the

same shape as the current, but they are not identical duebuck-boost converter conversion ratio. The terminal voltage ofthe ultracapacitors is plotted in Fig. 19. This voltage presentstwo situations. The first situation is characterized by the loadsdemand which corresponds to ultracapacitors module dischargeoperations. The second situation corresponds to ultracapacitorsvoltage increasing due to low current of the load compared tothe FC contribution or energy recovery process (asynchronousmachine in the generator operations). The measured current onthe FC is plotted in Fig. 20. This current presents less variationcompared to the variations of the load. Fig. 21 shows the FCterminal voltage which is also constant because the measuredcurrent on this last one is constant.

The performances of the proposed control are illustrated inFigs. 1517, which enable to conclude that, the ultracapacitors

Fig. 18. Measured current on the ultracapacitors.

Fig. 19. Ultracapacitors module terminal voltage.

Fig. 20. Measured current on the FC.

Fig. 21. FC terminal voltage.

ensures the dynamic components of the load, and the FC pro-vides the average power.

To conclude this section, the simulation and the experimentalresults present some differences in term of the fluctuations andthe average values. The differences related to average value aredue to used model for the hybrid system simulation which doesnot takes into account the losses in the semiconductors and the


10/11


Fig. 22. (a) Spectrum of the loads current measured in the dc-bus. (b). Spec-trum of the ultracapacitors current measured in the dc-bus. (c). Spectrum of theFCs current measured in the dc-bus.

electrical wiring. The observed fluctuations in Figs. 14, 17, 20,and 21 are due to the performances limitation of the used sen-sors for data acquisition. In other words, the used filters in theexperimental tests are not same to the simulation ones.

Fig. 22(a) and (b) present, respectively, the spectrum of theloads current , and the ultracapacitors ones. The FC current

spectrum is illustrated in Fig. 22(c). These spectrumsshow that the FC provides the average power and the ultraca-

pacitors module meets the dynamic fluctuations of the load. Inother words, the energy management is focused on the loadsdemand sharing between the FC and the ultracapacitors with amajor allocation of the loads variations to the ultracapacitorsduring the transient operations. This approach enables to avoidthe accelerated aging of the FC due to transient currents of theload.

VI. CONCLUSION

This paper presents the energy management method for theHEVs applications. The FC and the ultracapacitors models are

presented. The dc-bus voltage and the current control methods

are proposed. The proposed methods are evaluated through thehybrid system behavior simulations. To validate the theoreticalstudy, an experimental test bench is carried out in reduced scale.The obtained results from the experimental tests are analyzedand compared to that of the simulations ones. The simulationand the experimental results enable to conclude that, the pro-

posed methods for the energy management based on the poly-nomials correctors are interesting and effortless to implementin the PIC18F4431 microcontroller.

Finally, the energy management based on the loads demandsharing according to the dynamic responses of the FC and theultracapacitors enables to avoid the accelerated aging of the FCdue to transient currents of the load.

REFERENCES

[1] M. Zandi, A. Payman, J. P. Martin, S. Pierfederici, B. Davat, and F.M. Tabar, Energy management of a fuel cell/supercapacitor/batterypower source for electric vehicular applications, IEEE Trans. Veh.Technol., vol. 60, no. 2, pp. 433443, Feb. 2011.

[2] M. Bertoluzzo and G. Buja, Development of electric propulsion sys-tems for light electric vehicles, .IEEE Trans. Ind. Inf., vol. 7, no. 3,pp. 428435, Aug. 2011.

[3] P. Thounthong, S. Pierfederici, J. P. Martin, M. Hinaje, and B. Davat,Modeling and control of fuel cell/supercapacitor hybrid source basedon differential flatness control, IEEE Trans. Veh. Technol., vol. 59, no.6, pp. 27002710, Jul. 2010.

[4] I. Aharon and A. Kuperman, Topological overview of power trains forbattery-powered vehicles with range extenders, IEEE Trans. PowerElectron., vol. 26, no. 3, pp. 868876, Mar. 2011.

[5] P. J. Grbovi, P. Delarue, P. Le Moigne, and P. Bartholomeus, Theultracapacitor-based controlled electric drives with braking and ride-through capability: Overview and analysis, IEEE Trans. Ind. Elec-tron., vol. 58, no. 3, pp. 925936, Mar. 2011.

[6] J. Bauman and M. Kazerani, An analytical optimization method forimproved fuel cell-batteryUltracapacitor powertrain, IEEE Trans.

Veh. Technol., vol. 58, no. 7, pp. 31863197, Sep. 2009.[7] W. S. Liu, J. F. Chen, T. J. Liang, and R. L. Lin, Multicascodedsources for a high-efficiency fuel-cell hybrid power system in high-voltage application, IEEE Trans. Power Electron., vol. 26, no. 3, pp.931942, Mar. 2011.

[8] M. B. Camara,B. Dakyo, and H. Gualous, Supercapacitors andbatteryenergy management based on new european driving cycle, Journal of

Energy and Power En gineering, vol. 6, no. 2, pp. 168177, Feb. 2012,1934-8975, Serial no. 51.

[9] F. Segura, J. M. Andujar, and E. Duran, Analog current control tech-niques for power control in PEM fuel-cell hybrid systems: A criticalreview and a practical application, IEEE Trans. Ind. Electron., vol.58, no. 4, pp. 11711184, Apr. 2011.

[10] M. B. Camara, H. Gualous, F. Gustin, A. Berthon, and B. Dakyo,DC/DC converter design for supercapacitor and battery power man-agement in hybrid vehicle applications-polynomial control strategy,

IEEE Trans. Ind. Electron., vol. 57, no. 2, Feb. 2010.

[11] W. Na, T. Park, T. Kim, and S. Kwak, Light fuel-cell hybrid electricvehicles based on predictive controllers, IEEE Trans. Veh. Technol.,vol. 60, no. 1, pp. 8997, Jan. 2011.

[12] H. H. Wu, A.Gilchrist, K. D.Sealy,and D. Bronson,A high efficiency5 kW inductive charger for EVs using dual side control, .IEEE Trans.

Ind. Inf., vol. 8, no. 3, pp. 585595, Aug. 2012.[13] Q. Li, W. Chen, Y. Wang, S. Liu, and J. Jia, Parameter identification

for PEM fuel-cell mechanism model based on effective informed adap-tive particle swarm optimization, IEEE Trans. Ind. Electron., vol. 58,no. 6, pp. 24102419, Jun. 2011.

[14] S. M. Sharifi Asl, S. Rowshanzamir, and M. H. Eikani, Modellingand simulation of the steady-state and dynamic behaviour of a PEMfuel cell, Energy, vol. 35, pp. 16331646, 2010.

[15] C. A. Ramos-Paja, R. Giral, L. M. Salamero, J. Romano, A. Romero,and G. Spagnuolo, A PEM fuel-cell model featuring oxygen-excess-ratio estimation and power-electronics interaction, IEEE Trans. Ind.

Electron., vol. 57, no. 6, pp. 19141928, Jun.. 2010.[16] A. Y. Sendjaja and V. Kariwala, Decentralized control of solid oxidefuel cells, .IEEE Trans. Ind.Inf., vol. 7, no.2, pp.163170, May 2011.


11/11


[17] A. M. Dhirde, N. V. Dale, H. Salehfar, M. D. Mann, and T. H. Han,Equivalent electric circuit modeling and performance analysis of aPEM fuel cell stack using impedance spectroscopy, IEEE Trans. En-ergy Convers., vol. 25, no. 3, pp. 778786, Sep. 2010.

[18] J.-H. Jung, S. Ahmed, and P. Enjeti, PEM fuel cell stack model devel-opment for real-time simulation applications, IEEE Trans. Ind. Elec-tron., vol. 58, no. 9, pp. 42174231, Sep. 2011.

[19] M. B. Camara, B. Dakyo, and H. Gualous, Polynomial controlmethod of DC/DC converters for DC-bus voltage and currentsmanagementBattery and supercapacitors, IEEE Trans. Power

Electron., vol. 27, no. 3, Mar. 2012.[20] S. Chae, Y. Song, S. Park, and H. Jeong, Digital current sharing

method for parallel interleaved DC-DC converters using input ripplevoltage, .IEEE Trans. Ind. Inf., vol. 8, no. 3, pp. 536544, Aug 2012.

[21] D. G. Holmes, B. P. McGrath, and S. G. Parker, Current regulationstrategies for vector-controlled induction motor drives, IEEE Trans.

Ind. Electron., vol. 59, no. 10, pp. 19141928, Oct. 2012.

Abdallah Tani was born in Algeria in July 1984.He received the B.S. degree in electromechanicengineering from the University of Bejaia, Bejaia,Algeria, in 2008, and the M.S. degree from Univer-sity of Le Havre, Le Havre, France, in 2009.

Since 2009, he has been working in power elec-tronics, dc distribution system and hybrid electric ve-hicle research projects, involving converter topolo-gies, FC ultracapacitors, and batteries dedicated totransport applications.

Mamadou Balo Camara (M12) was born inMamou, Guinea, in 1974. He received the B.S.degree in electrical engineering from the PolytechnicInstitute of Conakry (IPC), Conakry, Guinea, in2003, and the M.S. and Ph.D. degrees from theUniversity of FrancheComt, Belfort, France, in2004 and 2007, respectively.

He is currently an Associate Professor withthe Groupe de Recherche en Electrotechnique etAutomatique du Havre Laboratory (GREAH),University of Le Havre, Le Havre, France. Since

2004, he has been working in power electronics and electric vehicle researchprojects, involving static converter topologies, ultracapacitors, batteries, andelectrical energy management for hybrid vehicle applications.

Brayima Dakyo (M06) received the B.S. andDr.Eng. degrees from Dakar University, Dakar,Senegal, in 1984 and 1987, respectively, and thePh.D. and Habilitation degrees from the Universityof Le Havre, France, in 1988 and 1997, respectively.

He is a full Professor of Electrical Engineering,and Head of the GREAH Laboratory, University ofLe Havre. His current interests include power elec-

tronics, converter fed electrical machines, analyticalmodel, wind and solar energy systems, energy man-agement, and system design with storage.

Yacine Azzouz was born in Ferkane, Algeria. He re-ceived the B.S. degree from the Institute of Annaba,Annaba, Algeria, in 1990, the M.S. degree from theNational Polytechnic Institute of Toulouse, Toulouse,France,in 1995, and the Ph.D. degree in electrical en-gineering from the University of AixMarseille III,Marseille, France, in 2000.

He is currently an Associate Professor with theIRSEEM Laboratory, cole dingnieurs ESIG-ELEC, Saint-Etienne du Rouvray, France.

Documents

Hybrid Electric Vehicles Energy