Artificial Intelligent Techniques for Electric and Hybrid Electric Vehicles by Chitra A P. Sanjeevikumar Jens Bo Holm-Nielsen S. Himavathi

244  AI Techniques for Electric and Hybrid Electric Vehicles Where, K = constant ω = rpm Ψ = flux From both the Equations (14.1) and (14.2), there is no relationship between torque and rpm and back EMF is totally independent of armature current. Both the armature ifslunxo(tΨgae)ttainndg fiinetledrffelurexd(Ψbyf) are perpendicular to each other. So field flux armature flux and via versa. To change the torque we need to change the armature current by keeping field current constant. 14.3 Clarke and Park Transforms Clarke and Park transforms methods are normally fit in field-oriented con- trol of three-phase AC machines. The Clarke transform transfer the time domain components of a three-phase system (in abc frame) to two compo- nents in an orthogonal stationary frame (αβ). The Park transform transfer the components in the αβ frame to an orthogonal rotating reference frame (dq). Utilizing such transforms in a consecutive way simplifies computa- tions by transferring AC voltage and current waveform into DC signals. The DQZ transform is made of the Park and Clarke transformation matrices. The Clarke transforms converts vectors in the ABC reference frame to the αβγ reference frame. The primary value of the Clarke trans- form is isolating that part of the ABC-referenced vector which is common to all three components of the vector; it isolates the common-mode com- ponent (i.e., the Z component). The Park transform converts vectors in the XYZ reference frame to the DQZ reference frame. The primary value of the Park transform is to rotate the reference frame of a vector at an arbitrary frequency. The Park trans- form shifts the frequency spectrum of the signal such that the arbitrary frequency now appears as dc and the old dc appears as the negative of the arbitrary frequency. In phasor diagram (Figure 14.2) consider stator α axis as a reference axis, this axis is perpendicular with stator β axis. Similarly q axis is perpendicular with d axis. The entire d and q axis is rotating with syn- chronous speed. By differentiating Ѳs with respect to time, it will give synchronous speed. By differentiating Ѳr with respect to time, it will give mechanical speed of rotor. The difference between d axis and rotor α axis gives slip frequency.

Vector Control of Asynchronous Induction Motor  245 rotor β axis stator β axis q Is iqs ωs Figure 14.2  Phasor diagram. d axis δ ids rotor α axis θs stator α axis θsl θr In phasor diagram Let, stator phase A axis ααα–rs ==β stator phase A axis = stator fixed reference frame d–q = synchronous rotating reference frame Let stator voltage component on direct axis of synchronous   Vs d = rotating reference frame.   Isd = stator current component on direct axis of synchronous rotat ing reference frame. Rs = stator resistance.   ωVssq==ssrytonatctaohtrirnovgnoorlteuafgseerfercenoqcmueepfnroacnmye.ne.t on quadrature axis of synchronous   Ψs q = stator flux on quadrature axis of synchronous rotating reference frame.   Ψr q = rotor flux on quadrature axis of synchronous rotating reference frame.   Ψsd  = sftraatmore flux on direct axis of synchronous rotating reference   Isq = stator current component on quadrature axis of synchronous rotating reference frame. P (rot) = total rotational power. LLsrsr = total stator inductance (leakage + mutual) = total rotor inductance (leakage + mutual)

246  AI Techniques for Electric and Hybrid Electric Vehicles ГILIrrqdm====torrioonrqttdoouurrecqdtuairnaedccertaaotxfuimrsecuautxuriraselcnputa.rrrte.nt. P = number of poles Standard equation for stator direct axis voltage is given by Vsd = R SIsd + dΨsd – ωsΨsq (14.3) dt Let Vsd (rot) = – ωs Ψsq (14.4) Similarly Standard equation for stator direct axis voltage is given by Vsq = R SIsq + dΨsq + ωsΨsd (14.5) dt Let Vsq (rot) = ωs Ψsd (14.6) Total rotational power is given by P(rot) = Vsd (rot) * Isd + Vsq (rot) Isq (14.7) Substitute Equations (14.4) and (14.6) in Equation (14.7) P(rot) = – ωs Ψsq Isd + ωs Ψsd Isq (14.8) Let Ψsd = Lss Isd+ Lm Ird (14.9) Ψsq = Lss Isq+ Lm Irq (14.10) Equations (14.9) and (14.10) in Equation (14.8) P (rot) = –ωωωωssssω[[[L−L−sm(m(LL[LssIIsssrrsIdIdsIsqIqIssqsIq+q+sd−−LL−ImLmrLqmIIIrmrqIqs)d)rIq]rII qIssdsdId+s]+d ω+(sL(LsLssssIsIsdIsds+dIs+qL+mLmLIrmdI)rdI)IrsdqI]sIqsq](14.11) = = = =

Vector Control of Asynchronous Induction Motor  247 Let Ψrd = Lrr Ird+ Lm Isd (14.12) Ird = Ψrd  − Lm Isd  Lrr  Ψrq = Lrr Irq+ Lm Isq (14.13) Irq = + Ψrq  − Lm Isq  Lrr  Substituting back in equation (14.11) ( )Prot = ω sL m[ Ψrd  − Lm Isd  I −   Ψrq  − Lm Isq   I ] Lrr  Lrr  Lm ωs   ( ) Prot = Lrr   Ψrd I − LmIsqIsd − ΨrqIsd + LmIsqIsd  ( ) P rot = Lm ωs    Ψ rd I − ΨrqIsd  (14.14) Lrr  We know that P (rot) = Г*ωs (mech) (14.15) IPn order to get mechanical speed, the synchronous speed will be divided E2quating Equations (14.14) and (14.15) by Γ ∗ ωs = Lm ωs   ΨrdIsq − ΨrqIsd  p/2 Lrr  Torque is given by, p/ 2   Lm ωs    ωs  Lrr   ( ) Γ = ΨrdIsq − ΨrqIsd

248  AI Techniques for Electric and Hybrid Electric Vehicles ( ) P   Γ = 2  ΨrdIsq − ΨrqIsd  (14.16) As 3 phases is converted to 2 phase, then for power balance, 3/2 is mul- tiplied and torque Equation (14.16) is converted as ( ) 3 P   Γ = 2   2  ΨrdIsq − ΨrqIsd  (14.17) Now let us assume 3   P   Lm  = K 2 2 lrr We know vector control is nothing but to align instantaneous rotor flux with the direct axis of synchronous rotating reference frame which makes rotor quadrature axis flux to zero. Ψrq = 0 SLoettuhsataГss=umke(ΨΨrrddI=sq)Ψf and Isq = Ia Г = k (Ψf Ia) which is analogous to Equation (14.1) Rotor voltage on direct axis of synchronous reference frame is given by, Vrd = R rIrd + d Ψrd − (ωs − ωr)Ψrq (14.18) dt In the Figure 14.3, reference flux amnadkiesd is compared to zoebrtoa.inInvsrdo. tToor establish vector control, we need to Ψrq is equal to construction of synchronous induction machine, the rotor bars are short circuited through short circuit ring at both the end. So as it is short cir- cuited, voltage Vrd should be zero. Reference flaux + comp Vsd _ isd Figure 14.3  Flux loop.

Vector Control of Asynchronous Induction Motor  249 Therefore, R rIrd + d Ψrd = 0 (14.19) dt (14.20) ( )Assuming d  Lm imr + RrLrd = 0 dt (Rotor getting magnetized by mutual part) Ψrd = Lrr Ird+ Lm isd = Lm imr Let Ird = Lm (imr − isd) (14.21) Lrr Using Equation (14.21) in Equation (14.20) ( )d  Lm imr +Rr Lm (i mr − isd ) = 0 dt Lrr d imr 1 dt + Γr (i mr − isd ) = 0 (14.22) (14.23) Where Гr = rotor time constant. In Laplace domain  s + 1  imr = isd  Γr  Γr So in steady state imr = isd + Isq ref _ PI ref speed PI + Vsq _ Speed fb isq Figure 14.4  Speed loop and torque loop.

250  AI Techniques for Electric and Hybrid Electric Vehicles Figure 14.4, consist of two loops namely speed loop and torque loop. sTbpeheeceodsmpfeepeeaddrbeldoacowkpisathnprteohrderouarccestsuigatnloarilsqq.iusWegcehonemenrparteoefndeer.neTnthscieis.ese.prerIsoeqdrrseiisfgecnroeanml cipseactrohemdatpwweintihl-l sated using PI regulator acnomd pita’srepdrowdiuthciancgtuIasql reference current. iTshaigsaIinsq reference current again is machine isq which regulated through PI regulator and then it produces Vsq. ( ) Let Vrq R rirq dΨrq = + dt + ωs − ωr  Ψrd (14.24) ω = − Rr  irq (14.25) Ψrd refeWΨrehrnqecree=fωr0asl=m=Lesr)lriiprq speed. (as it is aligned with direct axis of syn. Rotating + Lm isq Let irq = − Lm  isq (14.26) Lrr Equation (14.26) in Equation (14.25) ωsl = − Rr *  −   Lm   isq  Lrr Ψrd            = Rr isq  Lrr Imr Slip speed (ωsl) = 1    isq     (14.27) Γr isd ω sl + ωr = ωs = dθs dt ( ) θs = ∫  ωsl + ωr dt (14.28)

Vector Control of Asynchronous Induction Motor  251 14.4 Model Explanation In Figure 14.5, simulation circuit mainly consists of inverter, current feed- back loop, first order low pass filter, rpm sensor and ABC to DQ transfor- Immq oacttoioomrn.pFowonrheednriterse.ѲcAtsaicxsoisrnelsoqtoaunpirt,ettdhoertqosuebteemoefasg0tni.m0e2taiztNeadmtioainnsdcaupwrprhleiinecdthitsgocivotehmsepoianurdteudIdcwtaiinotnhd aanctduatilmIde. The output from PI regulator consisting of corresponding gain constant is feed to the limiter. The limiter is always better, which will not allow the loop to shot up and used for anti-wind up protection. The output from this limiter is feed to the ABC to DQ transformation. The sensed speed compared with set speed, after compensator, feed to the lim- iter, so the torque reference current may not shoot up more than 40 amp aApnoBdnCetnhttoe. nODinQt’csetargtahaneisnfmocroommdauptliaaotrniendigswsciiotghmnaIpqlatir.oeed.gVewna,ietVrhabtc,eoamqnudpaadVrracatiotsurrgteeonvgeoerlntaateegrdeatcferootmhme- switching pulses. tQhe1, iQnv3earntedr QIG5 BarTes.the generated pulse form comparator which is given to Vdc A IM A T A V RPM nm V ad V Id V Iq bq Id co Iq Q1 Q3 Q5 theta PI Id_cmd Vd_cmdV Vd Q1 nm_cmd PI d a Q3 Vq q b Id o c Iq_cmd Vq_cmd Q5 PI 10kHz K Iq V K theta K V RPMc slip sT K K nm Figure 14.5  Simulation circuit.

252  AI Techniques for Electric and Hybrid Electric Vehicles Inverse rotor time constant is required froersiѲstsaensctiems/a(lteioankage + mutual) Inverse rotor time constant = rotor  inductance = 0.156/(0.00074 + 0.041) Inverse rotor time constant  = 3.73 14.5 Motor Parameters it(FshIiqgde)uowdrmiierteih1nc4Xat.t6aiaxn,xiigisssocaivunpertlrroiemqtnuoetafgdddeortiamrsteueactrittnelaeaixdnxisivssec.cruIunyrrrtfreahensintsttbf(i(IugIdqt)u)fra.oeInrmtdhqmqeuuaeddadirdrieaarcttaeuttlurayexraeicfsutacexrurriserthrnceetuntsrtatra(keIrnedts)t, slightly more time to settle down, at around 0.6 s. Figure 14.7 is a plot of tthharetethpehainserussehquceunrcreenctuirsrewnetllISrae, sItSrbiacnteddIStcoinotcicmuer domain in s which infer the soft start of the machine. Figure 14.8 is a plot of speed in rpm verse time in s. In this plot, at begin- ning the speed increases and becomes constant at set value which shows an improved dynamic response. dagoemFFii(iggVnuuqa)rrteeiinn11g44ti..o69mvieseshrdaoqowpumlasodatthrionaetfuiidnnriesrsteaa.cxnItintsaaxnvthioesiolstvuaofgsilgetdau(igrrVeeeqc)(tt.Vhaedn) ddainrqdeucqat duaraxaditsruavrteoulratexagiasexvi(soVlvtdoa) lgties- after the rotor flux is perfectly aligned with the direct axis of synchronous rotating reference frame which in turn converted to abc reference frame to generate the modulating signals for the PWM generation. Id Iq 20 0 –20 –40 –60 1 0 0.2 0.4 0.6 0.8 Time (s) Figure 14.6  Plot of direct axis current (Id) and quadrature axis current (Iq).

Vector Control of Asynchronous Induction Motor  253 Isa Isb Isc 0.2 0.4 Time (s) 0.6 0.8 1 60 40 20 0 –20 –40 –60 0 Figure 14.7  Phase currents plot. RPM 0.2 0.4 Time (s) 0.6 0.8 1 1200 1000 800 600 400 200 0 –200 0 Figure 14.8  Speed plot. Vd Vq 0.2 0.002 0.004 0.006 0.008 0.01 0.012 0.1 Time (s) 0 –0.1 –0.2 –0.3 –0.4 0 Figure 14.9  Plot of direct axis voltage (Vd) and quadrature axis voltage (Vq). be WdievikdneodwanfrdomshoEuqludatbieonm(u1l4ti.p27li)e,dtowoitbhtaiinnvtehrseeslrioptospr eteimd,eiscq oannsdtaisndtw. Iinll the model, the bottom most theta estimation loop, there is a gain block of value 3.73 (Inverse rotor time constant) just after the division. Now the slip speed is to be added with rotor speed to get synchronous speed. Entire control is established in stator side, hence the rotor speed is to be taken in stator side and then is to be added with sleep speed. Hence the RPM is con- verted to radian/s by multiplying (π/30) and then by (P/2 for this model it is 3) to take it in stator side. In the model, RPM (marked as nm) is passed

254  AI Techniques for Electric and Hybrid Electric Vehicles through a gain block of value π/10 (which is(π/30)*3) and then added with slip speed to obtain synchronous speed. Finally it integrated to get rotor flux position (θs, marked as ‘theta’ in the model) which is fed to dq-abc block to generate modulating signal for inverter switching and to abc-dq block to convert three phase current to id and iq. 14.6 PI Regulators Tuning PI regulator tuning is mostly done by trial and error method but it needs more practical experience and proper knowledge of process control. In this model, we use second order tuning method. Three loops namely torque loop, flux loop and speed loop which has different natural frequencies but we need to tune both together to obtain high performance dynamic response from the motor. In tuning, each loop transfer function is simplified to a first order system using some fair approximation. After that the closed loop transfer function with compensator is established which consist of motor passive parameters like gwinaiditFnhuoc(srtktaaib)nnecodtefat,ertrrdheedssieyscctnoaomannmcdpeieocnarnsrdaedetsrotpircmo.onTneshtecreoonanlnssdydteasnntseottmasm,bplieenrqoauptpaoeotrrirfotiosnirosmn(isma2aln+pgcla2iefiξi,neωad(nlkslap+tn)hdaωenncld2oo).mionpptesagrareradel considered as critically damped with ζ = 1, please refer Figure 14.10, how 2.0 ζ=0 0.1 0.5 0.2 0.6 0.3 0.7 0.4 1.0 0.8 1.0 2.0 0 1 2 3 4 5 6 7 8 9 10 11 12 Figure 14.10  Second order response with respect to damping ratio.

Vector Control of Asynchronous Induction Motor  255 Table 14.1  Motor specification values. Name Values Rated frequency 50 Hz No. of poles 6 Rated speed 1,000 rpm Rotor resistance 0.156 Ω Stator resistance 0.294 Ω Stator inductance 0.00139 H Rotor inductance 0.00074 H Magnetizing inductance 0.041 H Inertia 0.002 H Viscous friction 0.001 Nm/(rad/s) Table 14.2  Values different loops of PI controller. PI controller Kp Ki Speed controller 0.13 0.4242 Flux controller 4.65 8.94 Torque controller 13.4 197.45 the response varied with ζ. Form the practical experience, if a motor used in fan or pump application, the natural frequencies of torque, flux and speed loops are considered 100, 10 and 1 Hz respectively. After the tun- ing with simulation and lab module, the results obtained are tabulated in Tables 14.1 and 14.2. The performance again can be enhanced if PI regula- tor is replaced with fuzzy controller. Figure 14.11 presents the laboratory setup for rapid prototyping of the concept where semikron IGBTs are used and skyper32 driver circuit is installed to generate complementary switching pulses. This setup has been used to soft-start a 30kW asynchronous induction motor and all the per- formance parameters are validated w.r.t. to the concept mentioned in this chapter.

256  AI Techniques for Electric and Hybrid Electric Vehicles Figure 14.11  Laboratory setup to benchmark the model. 14.7 Future Scope to Include Fuzzy Control in Place of PI Controller Fuzzy logic control is a special class of artificial intelligent. Normally when we need very tight control over the output, we need to introduce one com- pensator, basically it will take the error signal which is nothing but the dif- ferences between the desired set point and actual output obtained from the plant. Then it is transferred in very loop interval and producing controlled exaction to the plant so that it can maintain the output at the desired level. Conventionally we use PI regulator because it is very simple to implement but the thing is, to get very accurate high performance dynamic response, the tuning of the controller is very essential. Eventually the PI control- ler only can take linear gains but some time due to some transient or due to nonlinearity of the plant if compensator required nonlinear gains, that time the PI regulator is not able to cater the requirement and we need to introduce a model free adaptive controller. The dynamic response of fuzzy controller is much better than the PI regulator and also fuzzy controller is a semi model free adaptive controller which can take non-linear gain factors. In Figure 14.12, basic construction of fuzzy controller is shown which can be used in torque, flux and speed loop as a compensator for better dynamic response. The top model in Figure 14.12 is a classical PI controller

Vector Control of Asynchronous Induction Motor  257 ++ PID(s) 0.5 den(s) in out 1 0.5 den(s) ++ du/dt Figure 14.12  Fuzzy PI controller. 1.4 0.35 1.2 0.3 1 0.25 0.8 0.2 0.6 0.15 0.4 0.1 0.2 0.05 00 Figure 14.13  Output response of PI regulator (Left), Output response of Fuzzy regulator (Right). and bottom one is the fuzzy compensator. A second order plant transfer function is considered to establish the output response of both the cases with a unit step input. Figure 14.13 shows the comparison of PI and fuzzy controller where it exhibits better dynamic response. For the above figure, it is obvious that PI regulator exhibits with over- shoot whereas fuzzy controller provides better output response. Hence it is being proposed to replace the PI regulators in vector control by fuzzy controller as future work to improve the control. 14.8 Conclusion In process and power industries, speed control is very much concerned, that time we need to use some electrical prime movers through which we can vary the speed. But the problem somewhere arises that the torque and speed is needed to be controlled in an isolated manner. For that purpose this method is more useful. Conventionally the use of PI regulator it is very simple to imple- ment but to get very accurate high performance dynamic response the tuning

258  AI Techniques for Electric and Hybrid Electric Vehicles of the controller is very essential. Perfect tuning is impossible unless each loop is linearized properly. In order to overcome this limitation the fuzzy controller can be used. The dynamic response of fuzzy controller is much better the PI regulator and also fuzzy controller is and semi model free adaptive control. References 1. Vaez-Zadeh, S., and Reicy, S.H., Sensorless vector control of single-phase induc- tion motor drives. In 2005 International Conference on Electrical Machines and Systems, vol. 3, pp. 1838–1842, IEEE, 2005. 2. Bose, B.K., Power Electronics in Renewable Energy Systems and Smart Grid, John Wiley & Sons, 2019. 3. Bose, B.K., Power Electronics and Motor Drives-Advances and Trends, Elsevier/Academic Press, 2010. 4. Bose, B.K., Modern Power Electronics and AC Drives, Prentice-Hall, 416, 1986. 5. Bose, B.K., Power Electronics and AC Drives, vol. 5, Prentice-Hall, Bose, 416, 1986. 6. Bose, Bimal K., and Bimal K. Bose, eds. Power Electronics and Variable Frequency Drives, Vol. 996. Piscataway, NJ: IEEE Press, 1997. 7. Bose, Bimal K., Modern Power Electronics, IEEE Transactions on Power Electronics 7, no. 1, 2–16, 1992. 8. Bose, B.K., Microcomputer Control of Power Electronics and Drives, IEEE Press, 1987. 9. Bose, B.K., Adjustable Speed AC Drive Systems, IEEE Press Selected Reprint Series, New York: IEEE Press, 1981, edited by Bose, Bimal K. 1981. 10. Rashid, M.H., Power Electronics: Devices, Circuits and Applications. Elsevier, 2010. 11. Rashid, M.H. and Rashid, H.M., SPICE for Power Electronics and Electric Power, Second Edition, Electrical and Computer Engineering. 12. Rashid, M.H., Power Electronics: Circuits, Devices, and Applications: International Edition. In University of West Florida. Pearson Prentice Hall, 2004. 13. Liu, Shuxi, Shan Li, and Huihui Xiao. Vector control system of induction machine supplied by three-level inverter based on a fast svpwm algorithm. In 2010 International Conference on Intelligent System Design and Engineering Application, vol. 2, pp. 810–813, IEEE, 2010. 14. Reddy, Siddavatam Ravi Prakash, and Umanand Loganathan. Improving the Dynamic Response of Scalar Control of Induction Machine Drive Using Phase Angle Control. In IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society, pp. 541–546, IEEE, 2018. 15. Wang, Ding. Hybrid fuzzy vector control for single phase induction motor. In 2010 International Conference on Computing, Control and Industrial Engineering, vol. 2, pp. 122–125, IEEE, 2010. 16. Hybrid Fuzzy Vector Control for Single Phase Induction Motor, ieeexplore. ieee.org/document/5491982/

Index Acceleration, 108–109, 130 Battery-ultracapacitor model (BA-UC), Active cell balancing, 11, 196 106 Active magnetic bearing (AMB), BLDC motor speed controller 52–54, 58 with ANN-based PID Advantages of artificial intelligence in controller, 37–38 electric vehicle, 24 ANN-based on PID controller, Advantages of electric vehicle, 20–22 38–39 cost of charging electric vehicles, 21 PID controller-based on neuro energy efficiency, 20–21 action, 38 environmental, 20 grid stabilization, 21 Bluetooth communication, 13 heating of EVs, 22 Bottom up statistical method, 117 mechanical, 20 Bottom-up physics of failure range, 21 Ambient temperature sensor, 9 methods, 117 Analysis of different speed controllers, Brushless DC motor, 24–25 CAN bus, 107, 198 39–41 Cell diagnostics, 10 Artificial intelligence (AI), 22, 53, 67, Centralised BMS, 194 Charge shunting, 196 208 Closed-loop model of BLDC motor Artificial neural network (ANN), 63, drive, 30 144, 148 P-I controller & I-P controller, 31–32 Artificial neural network-based Component failure rate, 119 Controller, controller, 36–37 current controller, 56 Auto-tuning type fuzzy PID controller, position controller, 56 Converter, 107 34–35 Cruising, 132 Base failure rate, 121 Current sensor, 7 Basics of artificial intelligence, 22–23 Data bus, 198 Bathtub curve, 118 DC-DC converter, 143, 150, 156 Battery, 107 Deceleration, 109–110, 132 Battery configurations, 3 Depth of discharge, 10 Battery electric vehicle (BEV), 51 Battery management system, 127 Battery types, 139 259

260  Index Design of PI controller, 174–175 Hall effect sensors, 8 Diamagnetic materials, 55 Hill climbing, 143, 144, 146, 147 Digital controllers, 211–212 Historical background of electric Digital signal processor (DSP) vehicle, 19–20 controller, 212 Hybrid architecture, 129 Distribured BMS, 194 Hybrid electric vehicle (HEV), 51, 104 Driving cycle, 107, 109 Hybrid energy storage system (HESS), Driving mode, 106 charge depletion, 104 Hybrid management system, 127 charge sustaining, 105 Hybrid mode, 133 ECC-SOH, 195 Hybridness, 128 Efficiency, 104, 106 ICE torque, 139 Electric vehicle, 51, 104 Incremental conductance algorithm, integration, 104 Electric vehicles, 143, 145, 152, 153 design considerations of PMSM for, Induction motor, 210, 215 Interface circuit, 105 209–211 JANTX material, 123 smart infrastructure, 212–213 Magnetic susceptibility, 55 Electromagnet actuator, 54 Man to machine interface (MMI), Energy management, 104 Energy storage system (ESS), 104, 211–212 Mathematical modelling of IM, 105–106, 181 Equivalent ciruit of IM (d & q axis), 170 169–174 Evolutionary algorithm (EA), 143–146, Mathematical representation brushless 148, 154 DC motor, 25–29 Failure rate, 116 MATLAB, 60–66, 147, 162, 163 Ferromagnetic materials, 55 Maximum power point tracking Finite element analysis, 210 Firefly algorithm (FA), 143, 145–147, (MPPT), 143, 144, 162, 163 Mean time to failure rate, 116 149 Membership function, 61 Flywheel energy storage system Meta-heuristic algorithm, 146, 155 Modular BMS, 194 (FESS), 58 Noise and vibration, 210, 211 FPGA-based digital controller, 212 OCV-SOH , 195 Functional blocks of BMS, 6 Optimal battery, 140 Fuzzy control, 33–34 Parallel cell modules, 5 Fuzzy logic control (FLC), 56, 59, 60 Paramagnetic materials, 55 General packet radio service Partial shading conditions (PSC), 143, communication, 14 144, 146 Genetic algorithm, 35–36 Global system for mobile communication, 13

Index  261 Particle swarm optimization (PSO) scalar control drive, 176 algorithm, 67, 68, 70, 145, 163 vector control drive, 176 State of charge (SOC), 9, 105–111, 131 Passive cell balancing, 11 Surface mounted PM synchronous Passive magnetic bearing (PMB), 52 Photovoltaic array, 148, 149, 163 motor (SPMSM), 79–80 Photovoltaic cell, 148, 149 Taguchi method, 210 PID controller, 32–33 Thermal management for battery pack, Plug-In hybrid electric vehicle 11 (PHEV), 104–106 Top-down similarity analysis methods, Pottassium ion battery, 190 Power amplifier, 57 117 Quality factor, 120 Torque response, Regenerative action, 136 Relative permeability, 55 scalar control drive, 175 Reliability of the electronic vector control drive, 175 Torque sharing, 137 components, 119 Tracking speed, 160–162 Reliability prediction, 116 Types of batteries for HEV and EV, 5 Reliability prediction for resistor, 122 Ultra capacitor, 107, 187–189 Rotor, 54 Unsystematic, 110–111 Scalar control, 167–168 Variants of Lithium ion, Sensor, lithium cobalt oxide (LCO), 184 lithium iron phosphate (LFP), 184 current sensor, 57 lithium manganite (LMO), 184 position sensor, 56 lithium nickel manganese cobalt, Series cell modules, 4 Siemens Technology Solution, 213 184 Significance of induction motor drive, lithium polymer (LP), 184 Vector control, 166 direct field oriented control, 169 Significance of inverter, 166–167 indirect field oriented control, 169 Single diode model, 148 Voltage sensor, 7 Small signal model, 86–95 Voltage-source inverter (VSI), 81–86 Smart grid technology, 208 Wi-Fi communication, 13 Sodium ion battery, 190 Zigbee communication, 13 Solar panel, 146–148, 150, 159, 160, 163 Solid state battery, 190 Speed response,