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232 IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 51, NO. 1, JANUARY/FEBRUARY 2015

Modeling of Permanent-Magnet SynchronousMachine Including Torque Ripple Effects

Abraham Gebregergis, Senior Member, IEEE, Mazharul Huq Chowdhury, Senior Member, IEEE,Mohammad S. Islam, Senior Member, IEEE, and Tomy Sebastian, Fellow, IEEE

Abstract—A model of permanent-magnet synchronous machine(PMSM), including the electromagnetically originated torque rip-ple, is presented in this paper. This unique representation of suchequivalent circuit is highly critical to understand the torque ripplecontent and to develop an appropriate mitigation scheme for lowtorque ripple applications requiring four quadrant operations.This research proposes a method to quantify the various sourcesof torque ripple and modifies the existing dq-model to enhancethe modeling capabilities for both surface-mount and interiorPMSMs. Finite-element (FE) analysis is used for modeling vari-ous PMSMs to verify the lumped circuit model proposed in thisresearch. The theoretical results obtained from analytical and FEanalysis are validated using experimental test.

Index Terms—Electric machines, finite-element (FE) methods,modeling, permanent-magnet (PM) motor, torque control.

I. INTRODUCTION

THE high power density and low torque ripple charac-teristics of the permanent-magnet synchronous machine(PMSM) makes it an ideal candidate for high-performanceapplications. The interior PM (IPM) synchronous machinesare getting more attention for better PM material utilizationrelative to the surface mount PM (SPM) machines [1]. IPMmachines typically use cheaper rectangular magnets insertedinside the rotor core, which improves magnet retention andmanufacturing yields [2]. The torque production mechanismin an IPM is a combination of the reaction torque generatedby the interaction between the PMs mounted on the rotor andthe stator mmf and the reluctance torque produced due to thesaliency between direct and quadrature axis in contrast SPMmotors that only have the reaction torque. Generally, due tothe simple rectangular magnet shape and use of concentratedwinding, the torque ripple developed in an IPM is higher. Inorder to minimize the sources by design or to develop an activecancellation of torque ripple, it is important to understand thebehavior and quantify the sources of torque ripple. In this paper,the existing dq-model [3]–[5] will be augmented to provideadditional capabilities for predicting the performance of PM

Manuscript received December 18, 2013; revised March 28, 2014; acceptedMay 28, 2014. Date of publication July 8, 2014; date of current versionJanuary 16, 2015. Paper 2013-EMC-0967.R1, presented at the 2013 IEEEEnergy Conversion Congress and Exposition, Denver, CO, USA, September16–20, and approved for publication in the IEEE TRANSACTIONS ON INDUS-TRY APPLICATIONS by the Electric Machines Committee of the IEEE IndustryApplications Society.

The authors are with Halla Mechatronics, Bay City, MI 48706 USA(e-mail: [email protected]; [email protected];[email protected]; [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TIA.2014.2334733

motors, including IPM motors using the analytical model.Rojas et al. [6] presented an analytical torque computationbased on an energy approach, including the torque rippleharmonic components. Reducing of the torque ripple usingdifferent methods is discussed in [7]–[12]. These methods lackmodeling of the torque ripple components.

This paper focuses on modeling and quantifying the sourceof torque ripple as part of the dq-model of PMSM. Finite-element (FE) analysis tool is used to study and quantify thesources of torque ripple contents. These sources are identifiedas due to the interaction of the PM’s originated flux densityand the rotor saliency with the mmf resulted due to the spacedistribution of the winding in the stator. The effect of eachsource is separately identified and modeled as a voltage sourceadded to the existing dq-model of PMSM. Each added elementin the enhanced dq-model is derived and validated by extensiveFE simulation, which is later validated by experimental tests.

II. MODELING OF PMSMs

A. Basic dq-Model of PMSM

The dynamic model of a PMSM in the dq-reference frame isgiven by (1)–(5) [3]–[5]

Vdq0 =KVabc (1)

K =2

3

⎡⎣− cos(ωet) − cos(ωet−120) − cos(ωet−240)sin(ωet) sin(ωet−120) sin(ωet−240)

12

12

12

⎤⎦

(2)

Vabc =Riabc + eabc +d(Labciabc)

dt(3)

[VdVq

]=

[R −ωeLq

ωeLd R

] [idiq

]+

[0

ωmKmq0

]

+

[LdLq

]d

dt

[idiq

](4)

Te =3

2Kqm0iq −

3P

4iqid(Lq − Ld) (5)

ωe =P

2ωm (6)

where K is the Park’s transformation matrix, Vabc is the phasevoltage, iabc is the phase current, R is the resistance of the ma-chine, Lq and Ld are the q- and d-axis inductances, respectively.iq , id, Vq , and Vd are the q- and d-axis currents and voltages,

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GEBREGERGIS et al.: MODELING OF PMSM INCLUDING TORQUE RIPPLE EFFECTS 233

respectively. Kqm0 is the motor back EMF constant, whereaswm and we are the mechanical and electrical motor speeds,respectively. Te is the developed electromagnetic torque, andP is the number of poles of the machine.

B. Enhanced dq-Model of PMSM Including Torque Ripple

The dynamic model of a PMSM described in the aforemen-tioned section is modified to include the torque ripple contents.The developed electromagnetic torque and ripple contributiondue to the interaction between the flux density produced by thePMs on the rotor and the mmf produced by current-carryingwindings distributed in space of the stator can be modeled intwo steps, namely, reaction and reluctance parts, respectively. InIPM rotor configuration, both reaction and reluctance portionscan be modeled as follows:

i) Interaction of Airgap Flux Density and Stator MMFThe torque developed due to the interaction between

airgap flux density produced by the PMs and appliedcurrent can be expressed as given in

Tm =3

2(IqKqm0 + IqKqm + IdKdm) (7)

where

Kqm =

∞∑n=6

Kqmn cos(nθ + αqmn) (8)

Kdm =∞∑

n=6

Kdmn cos(nθ + αdmn) (9)

where n = 6, 12, and 18; Kqm0 is the average torqueconstant; Kxm6, Kxm12, and Kxm18 are the amplitudesof 6th-, 12th-, and 18th-order components of motor torqueconstants; and αxw6, αxw12, and αxw18 are the phaseshifts of the 6th-, 12th-, and 18th-order harmonics of themotor torque constants; and x denotes q- or d-axis; and mis the magnet contribution. Note that (7)–(9) are adequateto express torque and ripple contents for SPM motors.

ii) Interaction of Saliency of the Rotor and Stator MMFBoth q-axis and d-axis currents contribute into the de-

veloped torque due to the interaction between the saliencyof the rotor and stator mmf, as expressed by (10). Thetorque ripple components in dq-reference frame can beexpressed as

Tw =3

2(IqKqw + IdKdw) (10)

Kqw =

∞∑n=6

Kqwn cos(nθ + αqwn + β), n = 6, 12, 18, etc.

(11)

Kdw =

∞∑n=6

Kdwn cos(nθ + αdwn + β), n = 6, 12, 18, etc.

(12)

Fig. 1. Enhanced equivalent circuit of PMSM. (a) q-axis circuit. (b) d-axiscircuit.

where Kqw and Kdw are the q- and d-axes time varyingmotor torque values; Kqw6, Kqw12, and Kqw18 are theamplitudes; and αqw6, αqw12, and αqw18 are the phaseshifts of the 6th-, 12th-, and 18th(nth)-order harmonicsof the motor torque constant in the q-axis; whereas Kdw6,Kdw12, and Kdw18 are the amplitudes; and αdw6, αdw12,and αdw18 are the phase shifts of 6th-, 12th-, and 18th-order harmonics of the motor torque constant in the d-axisdue to rotor saliency and stator winding space distribution.β is the phase angle of the applied current with respect toq-axis.

Equation (13) represents the enhanced dq-model of thePMSM, which is also shown in Fig. 1 as the lumpedcircuit model, where Cqwn and Cdwn are the motor torqueconstants due to rotor saliency and stator winding spacedistribution. The total torque, including the torque rippledue to the airgap flux density produced by PMs, statorwinding space distribution, rotor saliency and current, thecogging torque is expressed by (14). The inclusion ofthe additional terms in (13) and (14) compared with themodel expressed in (4) uniquely brings completeness tothe existing model in predicting torque ripple mechanismin PMSMs. The model is generally applicable to bothSPM and IPM motor configurations

[VdVq

]=

[R −ωeLq

ωeLd R

] [idiq

]+

[0

ωmKqm0

]

+

[ωmKdmnωmKqmn

]+

[ωmKdwnωmKqwn

]+

[LdLq

]d

dt

[idiq

]

(13)

Te =3

2(Kqm0 +Kqmn +Kqwn)iq

+3

2icogKqm0 +

3

2(Kdmn +Kdwn)id

− 32

P

2iqid(Lq − Ld). (14)

iqcog can be obtained by measuring the cogging torqueof the test motor Tcog and using (15), where Kmq0 is the



Fig. 2. Space harmonic distribution of 27-slot and 9-slot 6-pole IPMSM.

average back EMF constant

iqcog =2

3Tcog/Kqm0. (15)

iii) Effect of Stator Slot on MMF DistributionThe space distribution of the winding depends on the

number of slots. It is impossible to distribute the windingin sinusoidal fashion using a finite number of slots. Theconstants Kqwn and Kdwn are a function of the n± 1thstator mmf space distribution and the rotor saliency. Fig. 2shows the space harmonics in the mmf distribution of a9-slot-6-pole and 27-slot-6-pole PMSM. The magnitude ofthe space harmonics distribution of the 27-slot 6-pole de-sign is lower than the 9-slot 6-pole. Therefore, the 27-slot-6-pole has less torque ripple produced for the same rotorsaliency due to the reduction in the space distributionharmonics, as shown in Fig. 2. A 9-slot 6-pole SPMhas lower torque ripple than a 9-slot-6-pole IPM due tothe insignificant rotor saliency in SPM. The mmf spacedistribution of a 27-slot 6-pole and 9-slot 6-pole PMSMcan be computed using the (16) for a pure sinusoidalcurrent through the conductors. However, the coefficientbn is different between the two motor topologies

F =

⎧⎪⎨⎪⎩

− 32IpN∑∞

n=1 bn cos(wt+ nθ), for n = 3k + 1;k = 0, 1, 2, 3, . . .

32IpN

∑∞n=1 bn cos(wt− nθ), for n = 3k − 1;

k = 1, 2, 3, . . .(16)

where F is the total mmf, Ip is the peak current, and N isthe number of turns.

For a 27-slot 6-pole machine, the value of bn can bewritten as

bn=

{0, for n is even4πn sin

(π2n

)[sin

(π3n

)+2 sin

(4π9 n

)]otherwise.

(17)

Similarly, for a 9-slot 6-pole machine, the value of bn is

bn =

{0, for n is even4πn sin

(π2n

)sin

(π3n

)otherwise. (18)

Equations (17) and (18) are developed assuming pointconductors placed at the center of the stator slot opening.In reality, it can be assumed that the conductors aredistributed in the stator slot opening. This distribution

Fig. 3. Winding and circuit configuration to simulate electromagnetic torquesimulation.

reduces the harmonics in the mmf distribution. For ex-ample, in the case of 27-slot 6-pole machine, the realvalue of the 17th- and 19th-order harmonics will be lower.Similarly for a 9-slot 6-pole machine, the 5th- and 7th-,and 17th- and 19th-order harmonics will be reduced dueto the conductor distribution in the stator slot opening.

III. FE MODELING OF PMSM

A 9-slot 6-pole three-phase IPMSM shown in Fig. 3 is usedduring this research. The winding of each phase has threeparallel paths. A 2-D FE model is developed and used for theanalysis to calculate the open-circuit voltage and to evaluate theelectromagnetic torque when excited with balanced three-phasecurrents, whereas the motor is running at a constant speed. Inorder to separate the torque ripple due to the rotor magnet andthe rotor saliency, the electromagnetic torque is calculated withthe magnet remanence Br set to zero and with the Br set to theactual value. This approach is done to study the effect of magnetand saliency issues separately on the torque ripple and to createan equivalent circuit model.

A. Open-Circuit Test

The open-circuit simulation is done at 1000 r/min mechan-ical, with the terminals of the motor disconnected from thesource. The flux linkage through each phase is captured andis used to calculate the induced voltage. The instantaneousvalue of the motor back EMF constants Keph of each phaseis calculated by dividing the induced voltage (vind) by themotor speed (ωm) as given in (19). These per phase motor backEMF constants in abc-frame are transformed into dq-axis usingPark’s transformation. Fig. 4 shows the harmonic contents ofthe motor back EMF constant in the abc- and dq-referenceframe

Keph =vindωm

. (19)

B. Electromagnetic Torque Simulation WithoutMagnet (Br = 0)

This simulation is performed setting the magnet Br to zero tostudy the torque ripple effect due to the interaction of winding



Fig. 4. Back EMF motor constant. (a) abc-frame. (b) dq-axis.

space distribution with the rotor saliency, whereas the motoris fed by balanced sinusoidal currents. The simulation is doneat various current magnitudes and phases. The first set ofsimulations involves a peak current of 25, 50, 75, 100, 125, and133 A and zero phase shift with respect to the back EMF. In thisparticular scenario, we will vary the current in q-axis, whereasthe current in d-axis is zero. Developed electromagnetic torqueis captured from the FE simulation under these conditions. Thepeak value of the motor constant Kqw6 in q-axis is calculatedfrom the torque and applied q-axis current. Fig. 5 shows thepeak of sixth-order torque ripple and peak of sixth-order motorconstant Kqw6. The sixth-order peak value of the motor con-stant Kqw6 is linearly proportional to the applied q-axis current.Therefore, the sixth-order peak motor constant Kqw6 and peaktorque ripple Tqw6 as a function of q-axis current (iq) can bewritten as

Kqw6 = cq6iq (20)

Tqw6 =3

2cq6i

2q. (21)

The d-axis current effect can be studied similar to the q-axiscurrent for the following peak currents of 25, 50, 75, 100, 125,and 133 A, whereas the current phase angle is shifted to 90◦.In this particular scenario, we will vary the current in d-axis,whereas the current in q-axis is zero. The peak value of themotor constant Kdw6 is calculated from the developed torqueand d-axis current. Fig. 6 shows the sixth-order harmonic peaktorque ripple and sixth-order harmonic peak motor constantKdw6. The peak value of motor constant Kdw6 is linearlyproportional to the applied d-axis current id. Therefore, thesixth-order peak motor constant Kdw6 and peak torque rippleTdw6 as a function of the d-axis current id can be written as

Kdw6 = cd6id (22)

Tdw6 =3

2cd6i

2d. (23)

Fig. 5. (a) Developed electromagnetic torque by varying q-axis current andId = 0 A. (b) Sixth-order torque ripple magnitude. (c) Sixth-order motorconstant magnitude.

In both conditions, the average developed electromagnetictorque is zero. A similar approach can be used to derive therelationship between the 12th-order torque ripple and appliedcurrent. In general, the nth order (where n = 6, 12, 18, etc.)torque ripple and motor torque constants can be expressed as

Kqwn = cqniq (24)

Tqwn =3

2cqni

2q (25)

Kdwn = cdnid (26)

Tqwn =3

2cdni

2d. (27)

In Figs. 5(a) and 6(a), the phase of the torque ripple is shiftedby 180◦ as seen in the plots when the applied current waveformis shifted by 90◦. This phase reversal is due to the torque andcurrent relation as defined by a square function. The phase shiftis independent of the peak current magnitude.

C. Electromagnetic Torque Simulation With PMs (Br �= 0)The combined effect of flux density produced by PMs, rotor

saliency, and the winding space distribution on the developedtorque is studied by placing the PMs on the rotor and running



Fig. 6. (a) Developed electromagnetic torque by varying d-axis current andIq = 0 A. (b) Sixth-order torque ripple magnitude. (c) Sixth-order motorconstant magnitude.

Fig. 7. (a) Electromagnetic torque. (b) Sixth-order torque ripple magnitude asa function if iq .

the simulation at various phase current magnitudes and phases.The same set of simulation parameters as used in the previoussection is repeated here. The results of the simulation are shownin Figs. 7 and 8.

Fig. 8. (a) Electromagnetic torque. (b) Sixth-order torque ripple magnitude asa function of id.

Fig. 9. Sixth harmonic peak torque ripple due to PM and iq .

The effect introduced by the PM on the developed torque rip-ple can be deduced by vector subtraction of the results obtainedfrom the torque simulation using with and without magnets.Fig. 9 shows the peak of the sixth-order torque ripple developeddue to the interaction of the phase current and the PMs. Alinearly increasing sixth-order peak torque ripple is observed asa function of the q-axis current, as shown in Fig. 9. This resultshows that the motor constant Kqm6 is a constant value, whichis the same as found during the open-circuit simulation of backEMF constant (Kqm6 = 0.000505 V · s/rad). The phase shiftof the sixth-order torque ripple remains unchanged as a functionof the q-axis current. The interaction of direct axis current andthe PM have negligible effect for this particular design.

IV. SIMULATION AND EXPERIMENTAL RESULTS

A. Simulation Results of 9-Slot 6-Pole IPMSM

The proposed enhanced dynamic dq-model that includes theeffect of torque ripple is implemented in MATLAB/Simulink.The PM is unmagnetized (Br = 0) in this model. The simula-tion is setup to run at constant peak current (75 A) with varyingphase angles from 0◦ to 90◦ with respect to the q-axis. The



Fig. 10. (a) Applied q- and d-axes currents. (b) Simulation result of dq-modeland FE analysis (torque developed). (c) Zoomed portion of Fig. 10(b).

same simulation parameters are used in the FE analysis. Theresult of the simulation is shown in Fig. 10. Fig. 10(b) shows theeffect of the applied currents (q- and d-axis currents). Fig. 10(c)shows simulated torque of the dq-model and FE analysis atIq = 66.8 A and Id = 34 A. The simulation results of theproposed dq-model and the FE analysis shows good agreementfor all combination of q- and d-axis current shown in Fig. 10(a).

B. Experimental Result of 9-Slot 6 Pole IPMSM

A 9-slot 6-pole three-phase IPMSM shown in Fig. 3 is builtand tested. Some of the test conditions previously describedduring the model development has been tested to validate themodeling of the torque ripple. Torque ripple tests at differentphase currents are performed. First, the motor was tested at25, 50, 75, 100, 125, −25, −50, −75, −100, and −125 A,respectively with a phase angle of zero with respect to theq-axis. This particular setup forces iq only while the d-axiscurrent Id is zero. Fig. 11 shows the effect of q-axis current onthe developed peak torque ripple and phase. The torque ripple

Fig. 11. (a) Sixth-order peak torque ripple due to Iq . (b) Phase of sixth-ordertorque ripple due to Iq .

magnitude depends on the magnitude of q-axis current only, asshown in Fig. 11(a). However, the torque ripple phase shift is afunction of both magnitude and polarity of the q-axis current, asshown in Fig. 11(b). This effect can be explained using (25) and(26). The winding space distribution torque ripple component isindependent of q-axis current polarity, as shown in (21). Hence,the phase of the reluctance ripple does not change with thepolarity of the iq current. On the other hand, the phase of thetorque ripple component due to the PM shifts by 180◦ whenthe q-axis current polarity changes from positive to negative, asexpressed in (9). This understanding is important for successfulimplementation of active torque ripple cancellation, whereasthe drive undergoes four quadrant operations.

C. Test Procedure for Computing EquivalentCircuit Coefficients

The coefficients that depend on the airgap flux density (effectof the back EMF) can be obtained by performing an “open-circuit test” and measuring the voltage at the terminal of themotor and the speed of the motor, and use (19) to calculate theinstantaneous value of the back EMF constant. This back EMFconstant has all the harmonic contents needed for the equiv-alent circuit. This approach does not depend on the differentslot–pole combination.

The components due to the interaction of the rotor saliencyand winding space distribution are obtained by performingloaded condition test. A known Iq current keeping Id = 0 ora known Id current keeping Iq = 0 is applied and the torqueripple of the test is measured. The torque ripple contribution ofthe interaction between the rotor saliency and the winding spacedistribution is the vector subtraction of the measured torqueripple and the torque ripple due to the back EMF harmonics



TABLE ITEST MOTOR SPECIFICATION

and cogging torque of the test motor. This approach does notdepend on the different slot–pole combination.

V. CONCLUSION

An enhanced dq-model for a PMSM capable of predictingaverage and ripple component of torque has been presentedin this paper. A detailed analysis based on FE simulation hasbeen presented. Simulation results obtained from FE analysisare used to calculate the model parameters. An actual motor isbuilt to perform experimental tests to correlate the theoreticalfindings. The proposed model is unique in terms of predictingmotor performances, including torque ripple. The understand-ing of torque ripple mechanism will further help to developproper torque ripple mitigation schemes for such drives. Thesaturation effects were neglected in this study, which mightaffect the accuracy of the torque ripple depending on the designof the machine.

APPENDIX

See Table I.

REFERENCES

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[3] T. Sebastian, G. Slemon, and M. Rahman, “Modeling of permanent mag-net synchronous motors,” IEEE Trans. Magn., vol. 22, no. 5, pp. 1069–1071, Sep. 1986.

[4] P. Pillay and R. Krishnan, “Modeling, simulation, analysis of permanent-magnet motor drives part I: The permanent-magnet synchronous mo-tor drive,” IEEE Trans. Ind. Appl., vol. 25, no. 2, pp. 265–273,Mar./Apr. 1989.

[5] P. Pillay and R. Krishnan, “Modeling, simulation, analysis of permanent-magnet motor drives part II: The brushless motor drive,” IEEE Trans. Ind.Appl., vol. 25, no. 2, pp. 274–279, Mar./Apr. 1989.

[6] S. Rojas, M. A. Perez, J. Rodriguez, and H. Zelaya, “Torque ripple mod-eling of a permanent magnet synchronous motor,” in Proc. IEEE ICIT ,Mar. 14–17, 2010, pp. 433–438.

[7] T. Liu, I. Husain, and M. Elbuluk, “Torque ripple minimization withonline parameter estimation using neural networks in permanent magnetsynchronous motors,” in Conf. Rec. IEEE IAS Annu. Meeting, Oct. 1998,vol. 1, pp. 35–40.

[8] J. F. Gieras, “Analytical approach to cogging torque calculation inPM brushless motors,” in Proc. IEEE IEMDC, Jun. 1–4, 2003, vol. 2,pp. 815–819.

[9] D. Min, A. Keyhani, and T. Sebastian, “Torque ripple analysis of a PMbrushless DC motor using finite element method,” IEEE Trans. EnergyConvers., vol. 19, no. 1, pp. 40, 45, Mar. 2004.

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Abraham Gebregergis (S’12–M’08–SM’06)received the B.Sc. degree from the Universityof Asmara (UOA), Asmara, Eritrea, in 2001, theM.Sc. degree from the University of Stellenbosch,Stellenbosch, South Africa, in 2004, and the Ph.D.degree from Clarkson University, Potsdam, NY,USA, in 2007, all in electrical engineering.

From 2001 to 2002, he was with the Departmentof Electrical Engineering, UOA, as an Assistant Lec-turer. From 2008 to 2013, he was with Delphi Steer-ing and Nexteer Automotive, Saginaw, MI, USA, as

a Senior Electrical Engineer at the Innovation Center. Currently, he is a ChiefScientist with Halla Mechatronics, Bay City, MI, USA, where he is responsiblefor motor control for automotive applications. He has published several journaland conference papers. His research interests include electric machines andadjustable-speed drives.

Mazharul Huq Chowdhury (M’12–SM’11) re-ceived the B.Sc. degree from Bangladesh Universityof Engineering and Technology (BUET), Dhaka,Bangladesh, in 2006 and the M.A.Sc degree fromConcordia University, Montreal, QC, Canada, in2010, both in electrical engineering.

From 2006 to 2008, he was with Siemens asan Electrical Engineer. In 2011, he joined NexteerAutomotive, Saginaw, MI USA. Currently, he is aSenior Electromagnetic Engineer with Halla Mecha-tronics Bay City, MI, USA, where he is responsible

for designing motors and sensors for both automotive and nonautomotiveapplications. His research interests include electric machines and adjustable-speed drives.

Mohammad S. Islam (S’99–M’00–SM’04) re-ceived the B.Sc. and M.Sc. degrees from BangladeshUniversity of Engineering and Technology (BUET),Dhaka, Bangladesh, in 1994 and 1996, respec-tively, and the Ph.D. degree from the University ofAkron, Akron, OH, USA, in 2001, all in electricalengineering.

From 1994 to 1996, he was with the Departmentof Electrical and Electronic Engineering, BUET,as a Lecturer. From 2001 to 2013, he was withDelphi Steering and Nexteer Automotive, Saginaw,

MI, USA, as a Staff Research Engineer at the Innovation Center. Currently, heis a Chief Scientist with Halla Mechatronics, Bay City, MI, USA, where he isresponsible for motors, sensors, and electromagnetic actuators for automotiveapplications. He has published several journal and conference papers and cur-rently holds 20 U.S. patents with several more pending. His research interestsinclude electric machines and adjustable-speed drives.

Dr. Islam is currently serving as the Vice Chair of the Transportation SystemsCommittee and the Chair of the Awards Department of the IEEE IndustryApplications Society.



Tomy Sebastian (S’80–M’86–SM’94–F’03) re-ceived the B.Sc. (Eng.) degree from the Re-gional Engineering College, Calicut, India, in 1979,the M.S. degree from the Indian Institute of Tech-nology Madras, Chennai, India, in 1982, and theMA.Sc. and Ph.D. degrees from the University ofToronto, Toronto, ON, Canada, in 1984 and 1986,respectively.

From 1979 to 1980, he was with the Researchand Development Center of KELTRON, Trivandrum,India. From 1987 to 1992, he was with the Research

and Applied Technology Division, Black and Decker Corporation, Towson,MD, USA. From 1992 to 2013, he was with Delphi Saginaw Steering Systemsand Nexteer Automotive, Saginaw, MI, USA, where his last responsibility wasas Chief Scientist at the Innovation Center. Currently, he is the Director ofMotor Drive Systems with Halla Mechatronics, Bay City, MI, USA. His areasof interests are electrical machines and variable-speed drives. He has over 50technical publications and holds 25 U.S. patents.

Dr. Sebastian is a corecipient of an IEEE Industry Application Society(IAS) Transactions First Prize Paper Award. He is a recipient of the IEEEIndustry Applications Society Outstanding Achievement Award. He has heldseveral positions in the Industrial Drives Committee and in the Industrial PowerConversion Systems Departments of the IAS and is currently the Vice Presidentof the IAS. He was the General Chair for the first IEEE Energy ConversionCongress and Exposition held in San Jose, CA, USA, September 20–24, 2009,and Co-General Chair of the IEEE Power Electronics, Drives and EnergySystems (PEDES 2012) held in Bengaluru, India.


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232 ieee transactions on industry applications, vol. 51 ... › paper › analysis of electric...

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