Modeling, Analysis, and Neural Network Control of an EV Electrical Differential

Modeling, Analyi, and Neural Network Control of an EV Electrical Differential Abdelhakim Haddoun, Mohamed Benbouzid, Demba Diallo, Rachid Abdeemed, Jamel Ghouili, Kamel Srairi To cite thi verion: Abdelhakim Haddoun, Mohamed Benbouzid, Demba Diallo, Rachid Abdeemed, Jamel Ghouili, et al.. Modeling, Analyi, and Neural Network Control of an EV Electrical Differential. IEEE Tranaction on Indutrial Electronic, Intitute of Electrical and Electronic Engineer, 2008, 55 (6), pp.2286-2294. <10.1109/TIE.2008.918392>. <hal-00524624> HAL Id: hal-00524624 http://hal.archive-ouverte.fr/hal-00524624 Submitted on 8 Oct 2010 HAL i a multi-diciplinary open acce archive for the depoit and diemination of cientific reearch document, whether they are publihed or not. The document may come from teaching and reearch intitution in France or abroad, or from public or private reearch center. L archive ouverte pluridiciplinaire HAL, et detinée au dépôt et à la diffuion de document cientifique de niveau recherche, publié ou non, émanant de établiement d eneignement et de recherche françai ou étranger, de laboratoire public ou privé.

2286 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 55, NO. 6, JUNE 2008 Modeling, Analyi, and Neural Network Control of an EV Electrical Differential Abdelhakim Haddoun, Mohamed El Hachemi Benbouzid, Senior Member, IEEE, Demba Diallo, Senior Member, IEEE, Rachid Abdeemed, Jamel Ghouili, and Kamel Srairi Abtract Thi paper preent ytem modeling, analyi, and imulation of an electric vehicle (EV) with two independent rear wheel drive. The traction control ytem i deigned to guarantee the EV dynamic and tability when there are no differential gear. Uing two in-wheel electric motor make it poible to have torque andpeedcontrolineachwheel.thicontrollevelimproveev tability and afety. The propoed traction control ytem ue the vehicle peed, which i different from wheel peed characterized by a lip in the driving mode, a an input. In thi cae, a generalized neural network algorithm i propoed to etimate the vehicle peed. The analyi and imulation lead to the concluion that the propoed ytem i feaible. Simulation reult on a tet vehicle propelled by two 37-kW induction motor howed that the propoed control approach operate atifactorily. Index Term Electric vehicle (EV), induction motor, neural network, peed etimation, traction control. I. INTRODUCTION RECENTLY, electric vehicle (EV), including fuel-cell and hybrid vehicle, have been developed very rapidly a a olution to energy and environmental problem. From the point of view of control engineering, EV have much attractive potential. Since electric motor and inverter are utilized in drive ytem, they have great advantage over internal combution engine vehicle uch a quick torque repone and individual control of each wheel [1], [2]. Although everal control method have been propoed uing thee merit, their controller depend on ome immeaurable parameter, including vehicle velocity and lip angle [3]. Generally, in mot EV propulion application, an ac motor i connected to the wheel by reduction gear and mechanical differential. In ome vehicle drive arrangement, high-peed low-torque wheel motor requiring gear reduction are ued. In Manucript received April 17, 2007; revied November 8, 2007. A. Haddoun i with the Laboratoire Bretoi de Mécanique et de Sytème, Univerity of Wetern Brittany, 29238 Bret, France, and alo with the Univerity of Oum El Bouaghi, 04000 Oum El Bouaghi, Algeria. M. E. H. Benbouzid i with the Laboratoire Bretoi de Mecanique et de Syteme, Univerity of Wetern Brittany, 29238 Bret, France (e-mail: m.benbouzid@ieee.org). D. Diallo i with the Laboratoire de Génie Electrique de Pari (LGEP/SPEE lab), CNRS UMR 8507, Supélec; Univerity Pierre and Marie Curie P6; Univerity of Pari Sud P11, 91192 Gif-Sur-Yvette, France (e-mail: ddiallo@ ieee.org). R. Abdeemed i with the Univerity of Batna, 05000 Batna, Algeria. J. Ghouili i with the GRET Reearch Group, Engineering Faculty, Univerity of Moncton, Moncton, NB E1A 3E9, Canada. K. Srairi i with the Univerity of Bikra, 07000 Bikra, Algeria. Color verion of one or more of the figure in thi paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIE.2008.918392 thee cae, either a gear motor aembly i mounted inide the wheel or a chai-mounted motor i connected to the wheel through gear reduction. Further implification of the vehicle drive arrangement reult in elimination of the gear being interpoed between the motor and wheel. The condition above call for the ue of an electric differential (no mechanical gear) [4] [7]. Electric differential-baed EV have advantage over their claical counterpart with a central motor. Indeed, mounting the motor directly on the wheel implifie the mechanical layout. The electric differential ytem will reduce the drive line component, thu improving the overall reliability and efficiency. Thi option will alo reduce the drive line weight ince mechanical differential and gear reduction are not ued [6] [8]. However, one of the main iue in the deign of thee EV (without mechanical differential) i how to enure the vehicle tability. During normal driving condition, all drive wheel ytem require a ymmetrical ditribution of torque on both ide. Thi ymmetrical ditribution i not ufficient when the adherence coefficient of tire i changing; the wheel have different peed, hence the need for traction control ytem [4]. Thi i till an open problem a illutrated by the limited availability of literature [10] [12]. Thi paper propoe a neural network traction control approach of an electrical differential ytem for an EV propelled by two induction motor drive (one for each rear wheel) [13]. Indeed, neural network concept have become an active reearch area in power electronic and motor drive. Becaue of the neceity for adaptive abilitie in a network learning proce, applying neural network to ytem identification and control dynamic ha become a promiing alternative to proce control. Neural network can be applied to control and identify nonlinear ytem ince they approximate any deired degree of accuracy with a wide range of nonlinear model [14] [20]. The rotor peed information of an induction motor in the vector control method i obtained uing peed enor. Since thee enor are uually expenive and bulky, the cot and ize of the drive ytem are increaed. Since the 1980 the concept of rotor peed etimation ha been tudied extenively. The intantaneou tator voltage and current were ued to etimate the peed of an induction motor, uch a in model reference adaptive ytem and extended Kalman filter algorithm. However, induction motor have highly nonlinear dynamic behavior and their parameter vary with time and operating condition. Therefore, it i difficult to obtain accurate peed etimate with thee method. In thi paper, a practical peed etimation method for an induction motor i propoed where a 0278-0046/$25.00 2008 IEEE

HADDOUN et al.: MODELING, ANALYSIS, AND NEURAL NETWORK CONTROL OF AN ELECTRICAL DIFFERENTIAL 2287 recurrent neural network (RNN) with two hidden layer i ued [21] [23]. In fact, the RNN ued i called the Elman neural network (ENN) [23]. The ENN multilayer and recurrent tructure make it robut under parameter variation and ytem noie. Moreover, the propoed RNN-baed peed etimator, which replace the peed enor in the control approach cheme, take into account vehicle aerodynamic and i not applied to ole induction motor. It hould be noted that the induction motor wa adopted becaue it eem to be the candidate that bet fulfil the major requirement for EV propulion [24] [26]. II. VEHICLE MODEL A. Nomenclature v Vehicle peed. a Vehicle acceleration. m Vehicle ma. α Grade angle. F te Tractive force. F rr Rolling reitance force. F hc Hill climbing force. F la Linear acceleration force. F wa Angular acceleration force. T m Motor torque. P te Vehicle driving power. J Total inertia (rotor and load). G Gear ratio. η g Gear ytem efficiency. r Tire radiu. B. Dynamic Analyi Compared to previou work, the propoed control trategy take into account vehicle aerodynamic and i not applied to ole induction motor. Thi model i baed on the principle of vehicle mechanic and aerodynamic [7]. The total tractive effort i then given by F te = F rr + F ad + F hc + F la + F wa. (1) Thi i the force propelling the vehicle forward and tranmitted to the ground through the wheel (Fig. 1). F la and F wa have been added in thi paper for a more accurate repreentation of the needed force to accelerate the vehicle. Indeed, we hould conider linear acceleration a well a rotational acceleration. The main iue here i the electric motor, not necearily becaue of it particularly high moment of inertia, but becaue of it higher angular peed [27]. It hould be noted that F la and F wa will be negative if the vehicle i lowing down and that F hc will be negative if it i going downhill. Therefore, the motor torque required for an angular acceleration will be given by T m = JG a. (2) η g r Finally, the power required to drive a vehicle at a peed v ha to compenate for counteracting force P te = vf te = v(f rr + F ad + F hc + F la + F wa ). (3) III. INDUCTION MOTOR MODELING A. Nomenclature V d (V q ) d-axi (q-axi) tator voltage. i d (i q ) d-axi (q-axi) tator current. λ dr (λ qr ) d-axi (q-axi) rotor flux linkage. T L Load torque. R (R r ) Stator (rotor) reitance. L (L r ) Stator (rotor) inductance. L m Magnetizing inductance. L σ Leakage inductance (L σ = L L 2 m/l r ). ω e (ω r ) Stator (rotor) electrical peed. Ω Rotor peed (ω r /p). ω l Slip frequency, ω l = ω e ω r. B Motor damping ratio. p Pole-pair number. { k 1 = R L σ + R rl 2 m L 2 r L σ,k 2 = R rl m k 4 = R rl m L r L m L r L σ, L 2 r L σ,k 3 =,k 5 = R r L r,k 6 = 1 L σ,k t = 3 2 p L m Lr. B. Induction Motor Dynamic Model Generally, dynamic modeling of an induction motor drive i baed on rotating reference-frame theory and a linear technique. A ytem configuration of an induction motor drive i hown in Fig. 2 (taking into account the vehicle dynamic). Thi motor drive conit of an induction motor, a bang bang currentcontrolled pulewidth modulated inverter, a field-orientation mechanim, a coordinate tranlator and a peed controller. The electrical dynamic of an induction motor in the ynchronouly rotating reference frame (d q axi) can be expreed by [28] d dt i d i q λ dr λ qr k 1 ω e k 2 ω r k 3 ω = e k 1 ω r k 3 k 2 k 4 0 k 5 ω l 0 k 4 ω l k 5 V d V + k q 6 0 0 i d i q λ dr λ qr (4) dω r dt = B J ω r 1 J (T m T L ) (5) T m = k t (λ dr i q λ qr i d ). (6) IV. NEURAL NETWORK TRACTION CONTROL A. Why Neural Network Traction Control? Recent development in artificial neural network (ANN) control technology have made it poible to train an ANN to repreent a variety of complicated nonlinear ytem [14]. ANN i a imulation of the human brain and nervou ytem built of artificial neuron and their interconnection. The ANN can be trained to olve the mot complex nonlinear problem with variable parameter imilar to the human brain. There have been

2288 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 55, NO. 6, JUNE 2008 Fig. 1. Elementary force acting on a vehicle. Fig. 2. Direct field-oriented induction motor drive. everal application of ANN to induction motor drive ytem uch a adaptive flux control, current control, peed control, and field-oriented control [15] [17]. B. Neural Network Controller The dynamic behavior of an induction motor can be decribed by voltage and current model (with decoupling control λ qr =0and λ dr = λ = contant) are derived from (4) (6) di d dt = k 1 i d + ω e i q + k 2 λ dr + k 6 V d di q dt = k 1 i q ω e i d k 2 λ dr + k 6 V q (7) dλ dr dt = k 5 λ dr + k 4 i d T e = k t λ dr i q. The RNN model-baed peed etimator replace the adaptive current model. In thi cae, each output neuron ue the linear activation function. The olution of the voltage model generate the deired flux component. Thee ignal are compared with the RNN output ignal and the weight are trained online o that the error ξ(k +1) tend to zero. It i aumed that the training peed i fat enough o that the etimated peed and actual peed can track well [23]. The current model equation can be dicretized and written a [ ] [ λ dr (k +1) 1 T λ T = r qr(k +1) ω r T ][ ] ω r T λ dr (k) 1 T λ qr(k) + T r [ Lm T T r 0 0 L m T T r ][ i d (k) i q(k) where T i the ampling time, L m the magnetizing inductance, and T r the rotor time contant. The above equation can alo be ] (8)

HADDOUN et al.: MODELING, ANALYSIS, AND NEURAL NETWORK CONTROL OF AN ELECTRICAL DIFFERENTIAL 2289 Fig. 3. Internal tructure of the RNN etimator. written in the form [ ] [ ][ ] λ dr (k +1) W11 W λ = 21 λ dr (k) qr(k +1) W 12 W 22 λ qr(k) [ W31 0 + 0 W 32 ][ ] i d (k) i q(k) where W 11 =1 T /T r, W 21 = ω r T, W 12 = ω r T, W 22 = 1 T /T r, and W 31 = W 32 = L m T /T r. The internal tructure of the deigned RNN peed etimator i hown in Fig. 3, where black circle repreent context node and white circle repreent the input, hidden and output node [12], [23]. The RNN with a linear tranfer function of unity gain atifie (9). Note that out of the ix weight in the network, only W 21 and W 12 (circled in the figure) contain the peed term. Therefore, it i ufficient if thee weight are conidered trainable, keeping the other weight contant (auming that T r and L m are contant) for peed etimation. However, if all the weight are conidered trainable, the peed a well a the rotor time contant can be tuned. V. E LECTRIC DIFFERENTIAL AND ITS IMPLEMENTATION Fig. 4 illutrate the implemented ytem (electric and mechanical component) in the Matlab-Simulink environment. It hould be noted that the two inverter hare the ame dc bu whoe voltage i uppoed to be table. Regenerative braking i not taken into account in thi paper. The propoed control ytem principle could be ummarized a follow: 1) A peed network control i ued to control each motor torque; 2) The peed of each rear wheel i controlled uing peed difference feedback. Since the two rear wheel are directly driven by two eparate motor, the peed of the outer wheel will need to be higher than the peed of the inner wheel during teering maneuver (and vice-vera). Thi condition can be eaily met if the peed etimator i ued to ene the (9) angular peed of the teering wheel. The common reference peed ω ref i then et by the accelerator pedal command. The actual reference peed for the left drive ω ref-left and the right drive ω ref-right are then obtained by adjuting the common reference peed ω ref uing the output ignal from the RNN peed etimator. If the vehicle i turning right, the left wheel peed i increaed and the right wheel peed remain equal to the common reference peed ω ref. If the vehicle i turning left, the right wheel peed i increaed and the left wheel peed remain equal to the common reference peed ω ref [7]. Uually, a driving trajectory i adequate for an analyi of the vehicle ytem model. We therefore adopted the Ackermann Jeantaud teering model, a it i widely ued a a driving trajectory. In fact, the Ackermann teering geometry i a geometric arrangement of linkage in the teering ytem of a car or other vehicle deigned to olve the problem of wheel on the inide and outide of a turn needing to trace out circle of different radii. Modern car do not ue pure Ackermann Jeantaud teering, partly becaue it ignore important dynamic and compliant effect, but the principle i ound for low peed maneuver [29]. It i illutrated in Fig. 5. From thi model, the following characteritic can be calculated: R = L (10) tan δ where δ i the teering angle. Therefore, the linear peed of each wheel drive i given by { V1 = ω V (R d/2) (11) V 2 = ω V (R + d/2) and their angular peed by { ω et1 = ω et2 = L (d/2) tan δ L ω V L+(d/2) tan δ L ω V (12) where ω V i the vehicle angular peed according to the center of turn. The difference between wheel drive angular peed i then ω = ω et1 ω et2 = d tan δ L ω V (13) and the teering angle indicate the trajectory direction { δ>0 Turn left δ =0 Straight ahead (14) δ<0 Turn right. In accordance with the above decribed equation, Fig. 6 how the electric differential ytem block diagram a ued for imulation, where K 1 =1/2 and K 2 = 1/2. VI. SIMULATION RESULTS A. RNN Control Strategy Tet Numerical imulation were carried out on an EV propelled by two 37-kW induction motor drive whoe rating are

2290 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 55, NO. 6, JUNE 2008 Fig. 4. EV propulion and control ytem chematic diagram. Fig. 5. Driving trajectory model. Fig. 6. Block diagram of the electric differential ytem. ummarized in the Appendix (Fig. 7). Electrical vehicle mechanical and aerodynamic characteritic are alo given in the Appendix. Objective of the imulation carried out were to ae the efficiency and dynamic performance of the propoed neural network control trategy. The tet cycle i the urban ECE-15 cycle (Fig. 8) [30]. A driving cycle i a erie of data point repreenting the vehicle peed veru time. It i characterized by low vehicle peed (maximum 50 km/h) and i ueful for teting electrical vehicle performance in urban area. The electric differential performance are firt illutrated by Fig. 9, which how each wheel drive peed during teering for 0 <t<1180. It i obviou that the electric differential operate atifactorily according to the complicated erie of acceleration, deceleration, and frequent top impoed by the urban ECE-15 cycle. Fig. 10 and 11 illutrate the EV dynamic, repectively, the flux (λ dr ) and the developed torque in each induction motor on the left and right wheel drive, with change in the acceleration

HADDOUN et al.: MODELING, ANALYSIS, AND NEURAL NETWORK CONTROL OF AN ELECTRICAL DIFFERENTIAL 2291 Fig. 7. Simulated ytem. Fig. 8. European urban driving chedule ECE-15. Fig. 10. Flux λ dr. Fig. 9. Vehicle wheel peed. pedal poition (Fig. 12) and a varied road profile (riing and downward portion). It hould be noticed that flux and torque variation are a large a variation of the accelerator pedal and the road profile. The RNN peed etimator performance are illutrated by Fig. 13, which how the meaured peed and the etimated value. Thi figure clearly how that the etimated peed during thi tet correctly follow the meaured one even at zero-peed. Fig. 11. Motor torque. Thi wa not the cae in [31], where the etimation failed around zero-peed epecially at no-load. Fig. 14 illutrate the power required to move the EV. To find the power taken from the battery to provide the tractive effort, we have to be able to find variou efficiencie at all operating point.

2292 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 55, NO. 6, JUNE 2008 Fig. 12. Acceleration pedal poition. Fig. 15. Vehicle wheel peed. B. Comparative Study Comparative tet with a previouly publihed control technique were conducted [7]. Thi wa done to check the effectivene of an RNN model for peed etimation of the electric differential. The electric differential RNN-baed control wa compared to a liding mode-baed control in the ame condition. Comparing the EV wheel peed reult of Fig. 9 (RNN control) to thoe of Fig. 15 (liding mode control), it i obviou that the neural network approach i effective particularly during teering at high peed. Although performance in other cae are quite the ame, the propoed control trategy i a enorle approach and therefore a cot-effective one. Fig. 13. Etimated and meaured vehicle peed. C. Experimental Validation Perpective The target vehicle for implementation of the propoed control ytem i a kart a hown by Fig. 16. Adaptation are made to introduce a two independent rear wheel propulion ytem uing two induction motor [11]. Fig. 14. Power required to propel the EV. VII. CONCLUSION In thi paper, a neural network traction control algorithm for an electrical vehicle with two eparate wheel drive wa propoed. Thi algorithm i neceary to improve EV teering and tability during trajectory change. An electrical differential wa implemented and account for the peed difference between the two wheel when cornering. Moreover, a traction control ytem impoe very precie knowledge of the vehicle dynamic, a vehicle dynamic model wa applied. Numerical imulation were carried out on an EV propelled by two 37-kW induction motor drive. The tet cycle wa, in our cae, the urban ECE-15 cycle. During traction and regenerative braking, a correlation of traction control with motor performance wa realized. The obtained reult eem to be very promiing.

HADDOUN et al.: MODELING, ANALYSIS, AND NEURAL NETWORK CONTROL OF AN ELECTRICAL DIFFERENTIAL 2293 Fig. 16. Experimental electrical vehicle. (a) Quart front view. (b) Quart rear view. The RNN peed etimator eliminate the need for an expenive peed tranducer with reaonable accuracy. It i hown that the propoed method etimate the peed accurately over the entire range from zero to full peed. Moreover, it ha robut peed etimation performance even with tep load change or under variable peed operation. APPENDIX I RATED DATA OF THE SIMULATED INDUCTION MOTOR 37 kw, 50 Hz, 400/230 V 64/111 A, 24.17 N m, 2960 r/min R =85.1 mω,r r =65.8 mω L =31.4 mh,l r =29.1 mh,l m =29.1 mh J =0.23 kg m 2. APPENDIX II EV MECHANICAL AND AERODYNAMIC PARAMETERS m=1540 kg(two 70 kg paenger),a=1.8 m 2,r=0.3 m µ rr1 =0.0055,µ rr2 =0.056,C ad =0.19,G= 104,η g =0.95 T =57.2 N m (tall torque),v 0 =4.155 m/ g =9.81 m/ 2,ρ=0.23 kg/m 3. REFERENCES [1] C. C.Chanet al., Electric vehicle charge forward, IEEE Power Energy Mag., vol. 2, no. 6, pp. 24 33, Nov./Dec. 2004. [2] C. C. Chan, The tate of the art of electric and hybrid vehicle, Proc. IEEE, vol. 90, no. 2, pp. 247 275, Feb. 2002. [3] M. E. H. Benbouzid et al., Advanced fault-tolerant control of inductionmotor drive for EV/HEV traction application: From conventional to modern and intelligent control technique, IEEE Tran. Veh. Technol., vol. 56, no. 2, pp. 519 528, Mar. 2007. [4] N. Mutoh et al., Electric braking control method for electric vehicle with independently driven front and rear wheel, IEEE Tran. Ind. Electron., vol. 54, no. 2, pp. 1168 1176, Apr. 2007. [5] N.Mutoh et al., Driving characteritic of an electric vehicle ytem with independently driven front and rear wheel, IEEE Tran. Ind. Electron., vol. 53, no. 3, pp. 803 813, Jun. 2006. [6] K. M. Rahman et al., Application of direct-drive wheel motor for fuel cell electric and hybrid electric vehicle propulion ytem, IEEE Tran. Ind. Appl., vol. 42, no. 5, pp. 1185 1192, Sep./Oct. 2006. [7] A. Haddoun et al., Sliding mode control of EV electric differential ytem, in Proc. ICEM, Chania, Greece, Sep. 2006. [8] S. Gair et al., Electronic differential with liding mode controller for a direct wheel drive electric vehicle, in Proc. IEEE ICM, Itanbul, Turkey, Jun. 2004, pp. 98 103. [9] Y. Hori, Future vehicle driven by electricity and control-reearch on four-wheel-motored UOT electric march II, IEEE Tran. Ind. Electron., vol. 51, no. 5, pp. 954 962, Oct. 2004. [10] G. Tao et al., A novel driving and control ytem for direct-wheeldriven electric vehicle, IEEE Tran. Magn., vol. 41, no. 1, pp. 497 500, Jan. 2005. [11] R. X. Chen et al., Sytem deign conideration for digital wheelchair controller, IEEE Tran. Ind. Electron., vol. 47, no. 4, pp. 898 907, Aug. 2000. [12] L. Ju-Sang et al., A neural network model of electric differential ytem for electric vehicle, in Proc. IEEE IECON, Oct. 2000, vol. 1, pp. 83 88. [13] A. Haddoun et al., Analyi, modeling and neural network traction control of an electric vehicle without differential gear, in Proc. IEEE IEMDC, Antalya, Turkey, May 2007, pp. 854 859. [14] B. K. Boe, Neural network application in power electronic and motor drive An introduction and perpective, IEEE Tran. Ind. Electron., vol. 54, no. 1, pp. 14 33, Feb. 2007. [15] M. Cirrincione et al., Control of induction machine by a new neural algorithm: The TLS EXIN neuron, IEEE Tran. Ind. Electron., vol. 54, no. 1, pp. 127 149, Feb. 2007. [16] M. Cirrincione et al., Senorle control of induction motor by reduced order oberver with MCA EXIN + baed adaptive peed etimation, IEEE Tran. Ind. Electron., vol. 54, no. 1, pp. 150 166, Feb. 2007. [17] B. Karanayil et al., Online tator and rotor reitance etimation cheme uing artificial neural network for vector controlled peed enorle induction motor drive, IEEE Tran. Ind. Electron., vol. 54, no. 1, pp. 167 176, Feb. 2007. [18] T. Pajchrowki et al., Application of artificial neural network to robut peed control of ervodrive, IEEE Tran. Ind. Electron., vol. 54, no. 1, pp. 200 207, Feb. 2007. [19] A.Rubaai et al., Implementation of artificial neural network-baed tracking controller for high-performance tepper motor drive, IEEE Tran. Ind. Electron., vol. 54, no. 1, pp. 218 227, Feb. 2007. [20] S. Jung et al., Hardware implementation of a real-time neural network controller with a DSP and an FPGA for nonlinear ytem, IEEE Tran. Ind. Electron., vol. 54, no. 1, pp. 265 271, Feb. 2007. [21] F. J. Lin et al., Recurrent-fuzzy-neural-network-controlled linear induction motor ervo drive uing genetic algorithm, IEEE Tran. Ind. Electron., vol. 54, no. 3, pp. 1449 1461, Jun. 2007.

2294 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 55, NO. 6, JUNE 2008 [22] C. M. Lin et al., Recurrent-neural-network-baed adaptive backtepping control for induction ervomotor, IEEE Tran. Ind. Electron., vol. 52, no. 6, pp. 1677 1684, Dec. 2005. [23] M. Wla et al., Artificial-neural-network-baed enorle nonlinear control of induction motor, IEEE Tran. Energy Conver., vol. 20, no. 3, pp. 520 528, Sep. 2005. [24] Z. Zhu et al., Electrical machine and drive for electric, hybrid, and fuel cell vehicle, Proc. IEEE, vol. 95, no. 4, pp. 764 765, Apr. 2007. [25] M. Zeraoulia et al., Electric motor drive election iue for HEV propulion ytem: A comparative tudy, IEEE Tran. Veh. Technol., vol. 55, no. 6, pp. 1756 1764, Nov. 2006. [26] F. Khoucha et al., A minimization of peed ripple of enorle DTC for controlled induction motor ued in electric vehicle, in Proc. IEEE IECON, Pari, France, Nov. 2006, pp. 1339 1344. [27] A. Haddoun et al., A lo-minimization DTC cheme for EV induction motor, IEEE Tran. Veh. Technol., vol. 56, no. 1, pp. 81 88, Jan. 2007. [28] A. B. Proca et al., Sliding-mode flux oberver with online rotor parameter etimation for induction motor, IEEE Tran. Ind. Electron., vol. 54, no. 2, pp. 716 723, Apr. 2007. [29] R.E. Colyeret al., Comparion of teering geometrie for multi-wheeled vehicle by modelling and imulation, in Proc. IEEE CDC, Dec. 1998, vol. 3, pp. 3131 3133. [30] M. André et al., inproc. Driving Cycle Emiion Mea. Under Eur. Condition, 1995, pp. 193 205. SAE Paper No. 950926. [31] A. Cordeiroet al., Senorle peed control ytem for an electric vehicle without mechanical differential gear, in Proc. IEEE MELECON, Malaga, Spain, May 2006, pp. 1174 1177. Demba Diallo (M 99 SM 05) wa born in Dakar, Senegal, in 1966. He received the M.Sc. and Ph.D. degree in electrical and computer engineering from the National Polytechnic Intitute of Grenoble, Grenoble, France, in 1990 and 1993, repectively, and the Habilitation á Diriger de Recherche degree from the Univerity of Pari Sud P11, Gif-Sur-Yvette, France, in 2005. From 1994 to 1999, he wa a Reearch Engineer with the Laboratoire d Electrotechnique de Grenoble, Grenoble, where he worked on electrical drive and active filter (hardware and oftware). In 1999, he wa with the Univerity of Picardie Jule Verne, Amien, France, a an Aociate Profeor of electrical engineering. In September 2004, he wa with the Univerity Intitute of Technology of Cachan, Univerity of Pari Sud P11, a an Aociate Profeor of electrical engineering. He i currently with the Laboratoire de Génie Electrique de Pari, Ecole Superieure d Electricite, Univerity Pierre and Marie Curie P6 and Univerity of Pari Sud P11, Gif-Sur-Yvette, France. Hi current reearch interet include advanced control technique and diagnoi in the field of ac drive. Dr. Diallo i a Senior Member of the IEEE Indutry Application, Vehicular Technology and Control Sytem Societie. He i an Aociate Editor for the IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY. Abdelhakim Haddoun wa born in Contantine, Algeria, in 1967. He received the B.Sc. and M.Sc. degree in electrical engineering from the Univerity of Batna, Batna, Algeria, in 1993 and 1999, repectively. He i currently working toward the Ph.D. degree, focuing on electric vehicle control and power management, in Univerity of Batna. Since 2000, he ha been with the Department of Electrical Engineering, Univerity of Oum El Bouaghi, Oum El Bouaghi, Algeria, a a Teaching Aitant. He i alo currently with the Laboratoire Bretoi de Mécanique et de Sytème, Univerity of Wetern Brittany, Bret, France. Mohamed El Hachemi Benbouzid (S 92 M 95 SM 98) wa born in Batna, Algeria, in 1968. He received the B.Sc. degree in electrical engineering from the Univerity of Batna, Batna, Algeria, in 1990, the M.Sc. and Ph.D. degree in electrical and computer engineering from the National Polytechnic Intitute of Grenoble, Grenoble, France, in 1991 and 1994, repectively, and the Habilitation à Diriger de Recherche degree from the Univerity of Picardie Jule Verne, Amien, France, in 2000. After receiving the Ph.D. degree, he joined the Profeional Intitute of Amien, Univerity of Picardie Jule Verne, where he wa an Aociate Profeor of electrical and computer engineering. Since September 2004, he ha been with the Laboratoire Bretoi de Mecanique et de Syteme, Univerity of Wetern Brittany, Bret, France, a a Profeor of electrical engineering. Hi reearch interet and experience include analyi, deign, and control of electric machine, variable-peed drive for traction and propulion application, and fault diagnoi of electric machine. Prof. Benbouzid i a Senior Member of the IEEE Power Engineering, Indutrial Electronic, Indutry Application, Power Electronic, and Vehicular Technology Societie. He i an Aociate Editor for the IEEE TRANSACTIONS ON ENERGY CONVERSION, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, and IEEE/ASME TRANSACTIONS ON MECHATRONICS. electronic and drive. Rachid Abdeemed wa born in Batna, Algeria, in 1951. He received the M.Sc. and Ph.D. degree in electrical engineering, from Kiev Polytechnic Intitute, Kiev, Ukraine, in 1978 and 1982, repectively. He ha been working for more than eighteen year at the Univerity of Batna, Batna, Algeria, where he i a Profeor in the Electrical Engineering Department. Currently, he i the Director of the Electrical Engineering Laboratory. Hi current area of reearch include deign and control of induction machine, reliability, magnetic bearing, and renewable energy. Jamel Ghouili wa born in Ghardimaou, Tuniia, in 1962. He received the B.Sc., M.Sc., and Ph.D. degree from the Univerity of Québec at Troi-Rivière, Canada, in 1986, 1998, and 2004, repectively. He i currently Profeor at the Univerity of Moncton, Moncton, Canada. Hi main reearch interet include power converter, ac drive, DSP and field-programmable gate array control, enorle control, electric and hybrid electric vehicle drive, fuzzy logic and neural network application in power Kamel Srairi wa born in Batna, Algeria, in 1967. He received the B.Sc. degree in electrical engineering from the Univerity of Batna, Batna, Algeria, in 1991, the M.Sc. degree in electrical and computer engineering from the National Polytechnic Intitute of Grenoble, Grenoble, France, in 1992, and the Ph.D. degree in electrical and computer engineering from the Univerity of Nante, Nante, France, in 1996. After graduation, he wa with the Univerity of Bikra, Bikra, Algeria, where he i a Profeor in the Electrical Engineering Department. Hi main reearch interet include analyi, deign, and control of electric machine.