Control, Flux and Torque Estimation, and Efficiency Optimization 3627

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 60, NO. 8, OCTOBER 2011 3625 Alternative Energy Vehicles Drive System: Control, Flux and Torque Estimation, and Efﬁciency Optimization Habib-ur Rehman and Longya Xu, Fellow, IEEE

Abstract—Indirect field-oriented control, direct field- examined for accurate and robust flux estimation. Accurate flux oriented control, and direct torque control are the most estimation is the core of optimal flux setting and regulation. widely used instantaneous torque control techniques for the Only accurate flux estimation can guarantee accurate torque drive system of alternative energy vehicles (AEVs). This paper evaluates three candidate alternating-current machines and estimation and, thus, provide torque regulation both for the investigates the suitability of different control techniques for the FOC and DTC drive systems [4]–[22]. drive systems of battery-powered electric vehicles, hybrid electric The closed-loop current model and voltage model observers vehicles, and integrated starter alternators. Accurate flux and are designed for flux estimation and are documented to perform torque estimation is the core of any opted control technique for all better, compared with open-loop observers. Flux estimation, types of AEV drive systems. Therefore, an accurate closed-loop when using current model flux observer, does not work well at voltage model flux and torque estimator that is insensitive to stator resistance variation has been designed. This paper also high speed due to its sensitivity to rotor resistance [4]. A closed- analyzes loss model control and search control (SC) techniques loop voltage model flux observer is an alternative approach for for the drive system’s efficiency optimization and proposes an overcoming the problem of rotor resistance variation. It has, offline SC efficiency optimization technique. Experimental results however, been well documented [4]–[11] that flux estimation, are presented to demonstrate the performance of the proposed when using the voltage model observer, faces the problems flux and torque estimator and efficiency optimizer. of stator resistance sensitivity and, most of the time, requires Index Terms—Direct torque control (DTC), efficiency optimiza- additional voltage sensors. One suggested alternative [5] is tion, electric vehicle, field-oriented control (FOC), flux estimation, to use the current model observer at low frequency and the hybrid electric vehicles (HEVs), starter alternator, torque estimation. voltage model observer at high frequency, thus benefiting from their advantages and overcoming the limitations of the two I. INTRODUCTION observers. Although this solution overcomes the problems of AST diminishing fossil fuel resources, increasing demand rotor time constant and stator resistance variation, it does not F for conventional personal vehicles, and concerns for envi- completely eliminate the observers’ reliance on the rotor and ronmental protection continuously promote interest in the re- stator resistances. Stator flux orientation [7], [8] is suggested search and development of alternative energy vehicles (AEVs) to reduce the effect of leakage inductance when using the [1]. An electric motor drive is a common propulsion system voltage model flux observer. Other stator resistance adaptation for all kinds of AEVs. The field-oriented control (FOC), which techniques are also presented in [9]–[11], but all of these was invented in the early 1970s [2], and the direct torque techniques overcome the problem of stator resistance variation control (DTC), which was invented around the mid-1980s by providing an estimation of the stator resistance. They neither [3], are the two most widely used techniques for almost all provide the exact value of the stator resistance nor completely types of adjustable-speed drives (ASDs), including AEVs. The eliminate the observer’s dependence on it. In addition, accurate current model flux observer is mostly used for indirect field- voltage signal information is needed most of the time, which oriented (IFO) and direct field-oriented (DFO) control, whereas requires additional voltage sensors. a voltage model flux observer is chosen for DFO and DTC Currently, sliding-mode observers are intensively investi- types of drive systems. These observers have intensively been gated [12]–[22] for their effectiveness and better performance to overcome the aforementioned problems. In our earlier work [20], a current model observer for flux and speed estimation was Manuscript received September 7, 2010; revised April 13, 2011 and June 5, designed. The unique feature of the proposed observer was that 2011; accepted June 29, 2011. Date of publication August 4, 2011; date of it was completely insensitive to the rotor time constant effect. current version October 20, 2011. The review of this paper was coordinated by Dr.K.Deng. This paper presents a closed-loop voltage model flux and torque H. Rehman is with the Department of Electrical Engineering, College estimator that does not require the stator resistance information. of Engineering, American University of Sharjah, Sharjah, U.A.E. (e-mail: In addition, voltage measurement is not required, eliminating [email protected]). L. Xu is with the Department of Electrical and Computer Engineer- the need for voltage sensors. A detailed discussion on the ing, The Ohio State University, Columbus, OH 43210 USA (e-mail: proposed observer design, including its stability analysis and [email protected]). derivation from the generalized model of induction machine Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. in the stationary frame of reference, has been presented in Digital Object Identifier 10.1109/TVT.2011.2163537 [21] and [22].

The FOC and DTC drive systems, which are realized in II. ALTERNATIVE ENERGY VEHICLE DRIVE AEVs and several other industrial applications, provide an SYSTEM AND CONTROL independent control of flux because of the natural decoupling The dc machines are no longer a viable option for AEV ap- of the flux- and torque-producing currents. This independent plications because of their low efficiency, low power-to-weight control of flux is utilized to maximize the machine efficiency ratio, high maintenance, and high-speed operation limitation. at various operating conditions. The loss model control (LMC) The ac machines are a better alternative for the BEV, HEV, and and search control (SC) methods [23]–[31] are two of the ISA vehicular drive systems. The induction motor, the switched most widely used techniques for the optimal flux setting of a vector-controlled drive system. As the name implies, LMC reluctance motor, and the permanent magnet motor are the three needs a system model for finding the optimal flux setting at candidate machines that are examined for these applications. any operating condition [23]–[27]. Therefore, this technique Although the switched reluctance motor has a simpler stator is dependent on the accurate motor parameters information. winding and rugged rotor structure, it has problems of torque The basic principle of the SC method is to iteratively search ripple, noise, and vibrations. The permanent-magnet synchro- online for the flux settings, which require a minimum in- nous (PMS) motor, on the other hand, has higher efficiency but put power for a given torque and speed [26]–[31]. The SC is relatively more expensive, and its magnets are sensitive to method suffers from the disadvantages of slow convergence high temperature. Nevertheless, advancement in the permanent and torque ripples. This paper proposes a simple yet very technology and ongoing reduction in the permanent magnet’s practical offline SC approach for the flux control to operate cost keep the future of PMS machines quite promising. The the machine at optimal efficiency. The machine is operated induction motor, on the other hand, is well accepted for its wide on the dynamometer in the speed control mode, and a direct- speed range, complete deenergization, ruggedness, and low current (dc) power generation command for battery charging cost. is set. The instrumentation is based on a setup that measures The 42-V ISA system has been accepted as the new standard the input mechanical power of the machine and the output power net by the automotive industry. Based on the author’s electrical power at the dc bus. The rotor flux (and, hence, experience [32], for an ISA application, a PMS motor has a the flux current ids) is iterated for minimum input mechanical better future compared with the ac induction motor because of power on the motor shaft for generating the desired output dc its higher efficiency, particularly when the ISA is powered by power. These offline optimal flux current data are collected and a smaller sized battery, compared with a BEV. An ac induction used in a table lookup, which is implemented in the machine motor seems to be a better or comparable choice for BEV and controller for various operating conditions. The advantages of HEV applications because of the aforementioned associated the proposed offline SC technique, compared with the online advantages. Interestingly, another deciding factor is the expe- SC method, are listed as follows: 1) It does not suffer from rience and technology that major automotive companies have the slow convergence problem; 2) it has fewer torque ripples, developed. Ford and General Motors are more inclined toward because it does not keep changing the flux level while searching the induction machine, whereas Toyota and Honda choose the for the optimal value; and 3) it is more accurate, because PMS machine for almost all kinds of AEV applications. the input and output power is measured offline using voltage, With regard to controlling the AEV drive system, FOC, current, torque, and speed transducers, making it insensitive which includes IFO and DFO control, and DTC are the two to any error that could occur in the online SC technique due main control techniques for AEV drive systems. Figs. 1–3 show to input or output power estimation. However, the disadvan- block diagrams for IFO, DFO, and DTC drive systems, respec- tage of the proposed method is that it does not account for tively. In case of speed control, the torque control loop will be all the operating conditions and the machine has to inten- replaced with the speed control loop for FOC. For DTC, another sively be characterized in the laboratory before putting it into block for speed regulation needs to be added, whose output operation. will be the torque command instead of the current command, This paper is organized as follows. Section II compares because DTC directly regulates torque. The IFO controller, various candidate machines for AEV drive systems. It presents as shown in Fig. 1, is realized by an accurate slip frequency the basic structure of IFO, DFO, and DTC techniques and in- control—a necessary and sufficient condition for keeping the vestigates the suitability of these control techniques for battery- machine field oriented. Accurate speed information is a must powered electric vehicle (BEV), hybrid electric vehicle (HEV), for IFO drive systems, which could come from the speed sensor and integrated starter alternator (ISA) applications. Section III or by designing a speed estimator. IFO drive systems have the presents the proposed flux and torque estimator and validates benefit of not requiring explicit stator or rotor flux estimation, its performance through simulation and experimental study. because the optimal flux setting can be realized by setting the Finally, Section IV elaborates the proposed offline SC strategy flux current ids and thus omitting the flux regulation block. In for the efficiency optimization, whereas the concluding remarks our earlier work, an IFO ac induction machine drive system are made in Section V. The work presented in this paper could was realized for ISA applications [32]. The DFO control is make a meaningful contribution to induction motor drives, preferred over the IFO control, because IFO is a feedforward because the stator flux and torque observers and the efficiency open-loop-based control system, whereas DFO is a feedback optimizer are equally useful while realizing both the FOC closed-loop control system. IFO is sensitive to rotor resistance, and DTC drive systems used for all types of drive systems, which is the core of slip calculation, whereas DFO is sensitive including AEV applications. to stator resistance, which is the core of flux and the flux angle REHMAN AND XU: AEV SYSTEM: CONTROL, FLUX AND TORQUE ESTIMATION, AND EFFICIENCY OPTIMIZATION 3627

Fig. 1. IFO drive system.

Fig. 2. DFO drive system.

DTC drive system also inherits a sensorless structure, and it applies a bang-bang torque and flux control. However, DTC drive systems suffer from the problems of low-speed flux and torque estimation and control, high current, and torque ripples. These torque ripples create vibrations, making DTC less attractive for automotive applications. In the authors’ view, DTC could be a good candidate for ISA applications, because ISA mostly operates in the cranking mode as a motor that requires good dynamic performance. The torque ripples of DTC drive systems in the steady-state operation are not a major problem, because the ISA operation time as a starter is short, and in the steady-state, it mostly operates as an alternator. For BEVs, it is only the ac motor drive that propels the Fig. 3. DTC drive system. vehicle both in the dynamic- and steady-state operations. Thus, DTC is not an attractive choice due to its torque ripples, making estimation. In the authors’ view, both IFO and DFO are equally FOC more suitable for BEVs. HEVs generally have the follow- good for AEV applications. The major difference is between ing three basic architectures: 1) series hybrid; 2) parallel hybrid; FOC and DTC. and 3) series–parallel hybrid. The ac machine operation for DTC, shown in Fig. 3, has the features of a fast dynamic HEVs varies based on its architecture and the designed hybrid response, the simplicity of design as a result of avoiding the features in the dynamic- and steady-state operations. There- stator current, and flux regulation loops required by FOC, in fore, if the machine operation in the steady-state condition addition to robustness to the parameters’ variation. The basic significantly increases, FOC will be a better choice. Otherwise, 3628 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 60, NO. 8, OCTOBER 2011

DTC could be selected, but this approach would essentially With this choice of sliding-mode functions, the sliding surface mean more of an ISA-type of operation. is given by

T Sn =[Sαs Sβs] . (7) III. FLUX AND TORQUE ESTIMATION In this paper, subscripts α and β are used for the stationary An accurate flux and torque estimation is the core of both frame of reference, whereas notations d and q represent the DFO- and DTC-types of drive systems. DFO and DTC may synchronous frame of reference. Equations (1) and (2) are or may not require the motor speed information, depending on the current and flux estimator equations, respectively, which the type of flux observer incorporated. Mostly, for DFO and include a sliding-mode function ∆αβs, as described by (4)–(6). DTC drive systems, a voltage model flux observer is used, The sliding-mode function, which is designed around the stator but for a DFO drive system, sometimes, a current model flux flux terms in the current estimation equation, will drive the esti- observer is also used. An open-loop voltage model flux observer mated current to the measured current. When the sliding-mode does not require the machine speed information, whereas for an function drives the estimated current to the measured current, open-loop current model observer, the speed signal information the function itself gives the estimate of the stator flux, as given is needed. This condition makes torque-controlled DTC and by (2). However, ∆αs and ∆βs will take the extreme values of DFO drive systems inherently sensorless when using the open- u0 and −u0 at a high frequency and will oscillate around their loop voltage model flux observer. However, both the current actual values. To define the control action, which maintains the and voltage model open-loop observers are sensitive to offset motion on the sliding manifold, an equivalent-control concept and drift problems and are, with no feedback, necessary for • [12] is used. Solving Sn =0for ∆αs and ∆βs will yield the convergence. Therefore, for both DFO and DTC drive systems, equivalent-control action. In practice, the discontinuous control closed-loop observers are preferred over open-loop observers. can be considered a combination of an equivalent-control term In this paper, a closed-loop voltage model flux observer that is and a high-frequency switching term. Therefore, the equivalent- well suited for both DFO and DTC drive systems is designed, control term can be found by isolating the continuous term developed, and implemented. using a low-pass filter, which is implemented as The proposed closed-loop flux observer is designed based on the estimated and measured stator currents. The current eq 1 ∆αβs = ∆αβs (8) estimation error is defined as the difference between the current µs +1 that is measured through the current sensor and the current that where µ is the time constant of the filter. It should sufficiently is estimated through the machine model. The equations for the be small to preserve the slow component undistorted but large proposed current and flux estimator [21] in the stationary frame enough to eliminate the high-frequency components. When the of reference are formulated as follows: trajectories of the system reach the sliding surface Sn =0,the 1 Rr observed currents îαs, îβs match the actual currents iαs, iβs. pîαs = (∆αs) − iαs − ωriβs σLs σLr Then, using (2) and (8), we obtain eq Rr ωr ∆αs = vαs − Rsiαs + λˆαs + λˆβs σLsLr σLs eq ∆βs = vβs − Rsiβs. (9)

1 Rr pˆiβs = (∆βs)+ωriαs − iβs Thus, the stator ﬂux can be estimated based on (2) and (9) as σLs σLr pλˆ eq ω R αs =∆αs r ˆ r ˆ − λαs + λβs (1) ˆ eq σLs σLsLr pλβs =∆βs. (10)

pλˆαs = vαs − Rsiαs =∆αs The estimated stator flux can be used to estimate the rotor flux and flux angle as follows: ˆ pλβs = vβs − Rsiβs =∆βs (2) L λˆ r λ − σL i αr = L ( αs s αs) where m Lr L2 λˆβr = (λβs − σLsiβs) (11) σ =1− m (3) Lm LsLr −1 λˆβr θe = tan . (12) αβs −u0 Sαβs ∆ = sign( ) (4) λˆαr   +1, if Sαβ > 1 The proposed approach thus requires the current sensors S S , |S |≤ sign( αβ)= αβ if αβ 1 (5) and speed sensor or observer in the drive system, because −1, if Sαβ < −1 the currents estimation equation (1) needs the motor speed information. In the real implementation of any induction motor Sαβs =îαβs − iαβs. (6) drive system, two current sensors are always in place to measure REHMAN AND XU: AEV SYSTEM: CONTROL, FLUX AND TORQUE ESTIMATION, AND EFFICIENCY OPTIMIZATION 3629

TABLE I SPECIFICATIONS OF THE LABORATORY PROTOTYPE INDUCTION MOTOR

the current for the current regulation in the case of FOC and for flux estimation and motor protection for both FOC and DTC. DTC is inherently sensorless, but for an internal combustion engine (ICE), a speed sensor is always in place for measuring the engine speed, which makes the proposed observer equally useful for both HEV and ISA applications. With regard to the pure BEV, a sensor can be added on the machine shaft, or because the sensorless technology is well developed, a speed estimator can be designed to avoid an encoder on the machine shaft. The measured current and estimated flux in the stationary frame of reference are used to calculate the torque in a station- Fig. 4. Simulation results for a trapezoidal-wave reference of ±10 Nm. ary frame of reference as (a) Reference and estimated torque tracking. (b) Stator resistance variation. N L (c) Actual and estimated stator current. (d) Actual and estimated rotor flux. 3 P m (e) Sliding-mode function. Têαβ = (λˆαriβs − λˆβriαs). (13) 2 Lr The torque in a synchronous frame of reference can be calculated by converting the measured current and estimated flux from a stationary to a synchronous frame of reference as

3NP Lm Tê = (λˆdriqs − λˆqrids). (14) 2 Lr Equation (14) indicates that a given torque level can be achieved by adjusting the current iqs, whereas the flux setting is a degree of freedom and can be set at different values to obtain the same level of torque. This degree of freedom is used to optimize the machine efficiency. The proposed algorithm is validated on a prototype 3.75-kW induction machine through the simulation and experimental study. The parameters of the machine are given in Table I. Fig. 5. Simulation results for a trapezoidal-wave reference of ±10 nm, zoomed from 0 s to 1 s. (a) Torque tracking. (b) Stator current. (c) Rotor flux estimation. A. Simulation Results that the proposed model can give accurate estimation, even Fig. 4 shows the simulation results for a trapezoidal-wave when the stator resistance is not known. The estimated flux is reference torque tracking. These results are zoomed between 0 then used for the torque estimation and regulation presented in and 1 s and are plotted in Fig. 5 to show a clearer performance Fig. 4(a). Thus, an accurate torque estimation and regulation of the proposed algorithm. Fig. 4(a) shows the reference and is performed, and the stator current and rotor flux estimation actual (estimated) torque plotted together. To test the parameter are realized, requiring no knowledge of the stator resistance sensitivity of the observer, the stator resistance of the machine is and voltage signal information. The fact that the proposed drive ± changed in a magnitude of 50%, as shown in Fig. 4(b). When system does not require any stator resistance and voltage signal these changes are applied, the actual machine model is used information is the major advantage of the proposed scheme over to calculate the current, flux, and torque, whereas the proposed the schemes presented in the past [7]–[11]. observer is used to estimate the same quantities as in the actual model. The results of the actual and estimated stator current B. Experimental Results for Flux and Torque Estimation are plotted together in Fig. 4(c). The actual and estimated rotor fluxes are shown in Fig. 4(d), and the sliding-mode function The proposed observer, after testing through a series of (derivatives of the stator flux) is plotted in Fig. 4(e). These intensive simulation studies, is validated in the laboratory on results show that the estimated machine current and flux very an experimental test setup. The setup includes a 3.75-kW quickly converge to real values and are not affected by the induction motor, whose parameters are given in Table I, an stator resistance variation. The accuracy of the proposed model insulated-gate bipolar transistor (IGBT) inverter, and a flexible has quantitatively been assessed and is about 97%, showing high performance advanced controller for electric machines 3630 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 60, NO. 8, OCTOBER 2011

Fig. 8. Experimental results for ±5-nm square-wave torque tracking. ± Fig. 6. Experimental results for 5-nm triangular-wave torque tracking. (a) Measured and estimated stator current. (b) Estimated stator flux. (a) Measured and estimated stator current. (b) Estimated stator flux. (c) Estimated rotor flux. (d) Current estimation error. (c) Estimated rotor flux. (d) Current estimation error.

Fig. 9. Experimental results for ±5-nm square-wave torque tracking. (a) Reference and estimated torque tracking. (b) Torque command current. Fig. 7. Experimental results for ±5-nm triangular-wave torque tracking. (c) Rotor speed. (d) Torque error. (a) Reference and estimated torque tracking. (b) Torque command current. (c) Rotor speed. (d) Torque error. [see Figs. 6(a) and 8(a)] and current error [see Figs. 6(d) and 8(d)] plots that the sliding-mode function drives the estimated (ACE) [33]. The digital signal processor (DSP) on the central current to the measured current, and hence, the function itself processing unit (CPU) board performs all real-time control provides the estimate of stator flux according to (2) and (10). functions, whereas a microprocessor performs downloading, The mean square error (MSE) between the measured and data logging, and data communication functions. estimated current for two cases is 0.09 and 0.10, proving that the The proposed algorithm has been realized for the motor proposed current observer can accurately estimate the current. drive that operates under rotor flux direct-field orientation, and An accurate current estimation guarantees an accurate estimate tests are conducted to characterize its performance. Figs. 6 and of the stator flux [see Figs. 6(b) and 8(b)] and the rotor flux [see 7 show the observers’ performance for torque tracking for a Figs. 6(c) and 8(c)]. These estimated fluxes are finally used for triangular-wave reference of ±5 nm, whereas Figs. 8 and 9 torque estimation and tracking. show torque tracking for a square-wave reference of ±5nm. The reference and estimated torques are plotted together A proportional–integral (PI) controller is used for torque reg- in Figs. 7(a) and 9(a). To realize this torque tracking, a ulation in both cases. These references are chosen so that the torque command current is generated by the PI controller [see proposed observers’ performance can be validated at sharp Figs. 7(b) and 9(b)]. The error between the command and changes in the torque reference and the variable and constant the estimated torque is also plotted in Figs. 7(d) and 9(d) for torque while running in both the motoring and generating triangular- and square-wave references of ±5 nm, respectively. modes of operation. The MSE for the two torque tracking is 0.01 and 0.09, respec- The current and flux estimation for two torque references are tively. The difference in the MSE for torque tracking could plotted in Figs. 6 and 8. It can be observed through the current come from the step change that occurs for square-wave torque REHMAN AND XU: AEV SYSTEM: CONTROL, FLUX AND TORQUE ESTIMATION, AND EFFICIENCY OPTIMIZATION 3631

TABLE II SPECIFICATIONS OF THE INDUCTION MOTOR

Fig. 11. Steady-state rotor-flux-oriented model of the induction motor. relatively high. This motor in the low-speed range, up to about 700 r/min, will mostly operate in the motoring mode. Above the base speed in the higher speed range, it will work in the generating mode only for ISA applications, whereas for HEVs, it could work in both the motoring and generating modes of operation. Therefore, the efficiency optimization is mainly required for motor operations above the speed of about 700 r/min. Below this speed, the machine could be operated at the rated flux.

B. Drive System Efficiency Optimization The induction motor operates at different torque and speed, depending on the vehicle operating conditions. An indirect rotor-flux-oriented drive system is realized in this paper. The steady-state model of this drive system is shown in Fig. 11. The Fig. 10. Torque speed characteristics of the HEV/ISA machine. motor steady-state stator copper losses, rotor copper losses, and core losses are, respectively, given by [27] tracking, or it could be due to the tuning of the PI controller’s 2 2 gains. However, the MSE for the current estimation for the two Ps = Rs ids + iqs cases is about the same, validating the observer performance. ω L 2 The speed signal is also included for completeness and readers’ P R i − s m i r = r qs R ds information [see Figs. 7(c) and 9(c)]. The proposed observer fe 2 1 2 proved to effectively regulate the torque while estimating the Pfe =(ωsLm) ids. (15) stator and rotor fluxes of the machine. Rfe For any given speed and load torque, a flux level exists, at IV. MOTOR DESIGN CONSIDERATIONS which the copper and core losses will be the same. This flux AND EFFICIENCY OPTIMIZATION level will maximize the motor efficiency. Thus, the problem of This paper takes the HEV/ISA drive system as a case study energy optimization nails down to finding and then controlling to demonstrate the proposed offline SC technique for efficiency the motor at this optimal flux level. LMC and SC are two of optimization. The motor design for HEV/ISA is relatively dif- the most widely used techniques for the optimal flux control. ferent, compared with other variable-speed drive applications. In addition, a hybrid technique has been developed, which uses Therefore, first, the HEV/ISA motor design considerations are a motor model to search for the minimum power loss and then discussed, and then, the efficiency optimization is presented in applies the SC method for further optimization. LMC uses the this section. motor model to achieve minimum losses. Various minimization variables are suggested in the literature for the model-based technique. One of these methods is to transform the stator A. Motor Design Considerations copper losses, rotor copper losses, and core losses given by (15) The HEV/ISA machine, most of the time, operates in the into the following d and q components: generation mode and needs to supply a constant voltage over R a wide range of speed for battery charging. This requirement P ω L 2 1 R ω L 2 r i2 loss_d = ( s m) R + s +( s m) R2 ds minimizes the constant torque region and extends the constant fe fe power region over a ration of 1–6. The motor rating for a typical 2 Ploss_q =(Rr + Rs)iqs. (16) prototype HEV/ISA application is shown in Table II, and the desired torque speed characteristics are shown in Fig. 10. The optimal flux level can be achieved by equating the losses Typically, the motor efficiency is quite low for small-size motor depending on the current direct with the rotor flux equal to drives of less than 10 kW, whereas the converter efficiency is the losses depending on the current in quadrature to the rotor 3632 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 60, NO. 8, OCTOBER 2011

flux [27]. The model-based method clearly requires an accurate knowledge of the motor parameters for achieving the optimal flux level. This dependence on the motor parameters could shift the machine operation from the optimal flux level. The SC method works on the principle of iteratively changing the flux level and searching for the minimum input power while keeping the output speed and load constant, i.e., operating in the steady state at a constant output power. The input variable can be a magnetizing flux, stator voltage, or direct-axis stator current. The optimization of any of these variables will min- imize the input power. Perturb and observer, fuzzy logic, and neurofuzzy have been used in the literature for the SC method. The advantage of the online SC method is that it does not need the motor parameters that are required by the model-based approach. However, this techniques suffers from the problems of slow convergence and torque ripples and needs precise power measurement. Therefore, this paper proposes a simple offline Fig. 12. Experimental results for efficiency optimization at 1000 r/min. SC method for the optimal flux control of the drive system. The rotor-flux-oriented drive system that is implemented in this paper has torque and flux commands as two independent control inputs, which can be adjusted to achieve the same level of output power. The torque command is mostly set by the vehicle operating conditions, whereas the flux command is a degree of freedom that can be adjusted at different levels while achieving the desired speed and torque. This freedom in the adjustment of the flux level is used to maximize the system efficiency. The machine is operated by the dynamometer in the speed control mode, and a dc power generation command for the battery charging is set. The rotor flux (and, hence, the flux current ids) is iterated for the minimum input mechanical power on the motor shaft to generate the desired dc output power. The instrumentation is based on a setup to measure the input mechanical power of the machine and the output Fig. 13. Experimental results for efficiency and flux current. electrical power at the dc bus. The procedure is repeated for different levels of dc power commands at different speed. These flux. The data plotted in these figures are used in the motor offline optimal flux current data are collected and used in a controller of HEV/ISA, in which, whenever the torque com- table lookup, which is implemented in the machine controller mand changes, the controller will select an appropriate flux for various operating conditions. level for the efficiency optimization. Although this proposed Fig. 12 shows the efficiency optimization at 1000 r/min. The offline SC efficiency optimization technique requires a rigorous command dc power for battery charging is varied from 250 W characterization of the machine on the dynamometer, it does to3kW,andids is iterated for efficiency optimization. not suffer from the slow-convergence problem, has fewer torque Fig. 12(a)–(c) shows the optimized values of ids, the input ripples compared with the online SC method, and is insensitive mechanical torque, and the shaft input power to achieve the to parameters variation, compared with the LMC technique. desired dc output electrical power, respectively. Finally, the efficiency of the drive system is plotted in Fig. 12(d). V. C ONCLUSION The efficiency is recorded from the motor shaft to the dc bus, which also includes the inverter efficiency. However, it still This paper has evaluated the suitability of torque control realizes the optimal flux level for the efficiency maximization techniques for different types of vehicular drive systems and de- of the machine. In Fig. 13, the flux optimization is demon- signed a flux and torque estimator and an efficiency optimizer. strated at speeds of 2000, 3000, 4000, and 4500 r/min. The Major conclusions that can be drawn from this paper are listed optimum flux current and efficiency are plotted in each trace as follows. for each speed. Fig. 14 shows the combined efficiency plots for • FOC and DTC are the two main types of control tech- the aforementioned five different speeds for a desired output niques. The DTC drive system is more suitable for ISA electrical power ranging from 250 W to 3 kW. It is shown that applications. For BEV applications, FOC is a preferable the machine efficiency is significantly low when the machine choice over DTC because of torque ripples that produced operates with light load and low speed. This difference could be by DTC. For HEV drive systems, the choice between DTC much more significant if the flux optimization is not performed and FOC depends on the motoring features of ac machine and if the machine is operated all the time at a constant/rated operation in the steady-state condition. REHMAN AND XU: AEV SYSTEM: CONTROL, FLUX AND TORQUE ESTIMATION, AND EFFICIENCY OPTIMIZATION 3633

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[32] H. Rehman, “An integrated starter–alternator and low-cost high- Longya Xu (S’89–M’90–SM’93–F’04) received the performance drive for vehicular applications,” IEEE Trans. Veh. Technol., M.S. and Ph.D. degrees in electrical engineering vol. 57, no. 3, pp. 1454–1465, May 2008. from the University of Wisconsin, Madison, in 1986 [33] H. Rehman and R. J. Hampo, “A ﬂexible high-performance advanced and 1990. controller for electric machines,” in Conf. Rec. IEEE APEC, St. Louis, In 1990, he joined the Department of Electrical MO, Feb. 2000, pp. 939–943. and Computer Engineering, the Ohio State Uni- versity (OSU), Columbus, where he is currently a Professor. He has served as a Consultant to sev- Habib-ur Rehman received the B.Sc. degree in eral industrial companies, including the Raytheon electrical engineering from the University of Engi- Company, Boeing, Honeywell, GE Aviation, the U.S. neering and Technology, Lahore, Pakistan, in 1990 Wind Power Company, General Motors, Ford, and and the M.S. and Ph.D. degrees in electrical engi- Unique Mobility Inc., for various industrial concerns. He is currently the neering from the Ohio State University, Columbus, Director of the newly established Center of High Performance Power Elec- in 1995 and 2001, respectively. tronics, OSU, which is supported by the Ohio Third Frontier Program. His He has wide experience in power electronics and research and teaching interests include the dynamics and optimized design of motor drives in both industry and academia. From special electrical machines and power converters for variable-speed systems, July 1998 to December 1999, he was a Design the application of advanced control theory and digital signal processors for Engineer with the Ecostar Electric Drives and Ford motion control, and distributed power systems in super high-speed operations. Research Laboratory, where he was a Member of Over the past 20 years, he has conducted several research projects on electrical the Electric, Hybrid, and Fuel-Cell Vehicles Development Programs. From and hybrid electrical vehicles and variable-speed constant-frequency wind 2001 to 2006, he was with the Department of Electrical Engineering, United power generation systems. Arab Emirates University, Al-Ain, U.A.E., as an Assistant Professor. In 2006, Dr. Xu is currently a Member-at-Large for the IEEE Industry Applications he joined the Department of Electrical Engineering, College of Engineering, Society (IAS) Executive Board. He has served as the Chair of the Electric American University of Sharjah, Sharjah, U.A.E., where he is currently an Machine Committee of the IEEE IAS and an Associate Editor for the IEEE Associate Professor. His research interests include microprocessor/digital- TRANSACTIONS ON POWER ELECTRONICS. He received the First Prize Paper signal-processor-based adjustable-speed drives, power electronics, alternative Award from the Industry Drive Committee of the IEEE IAS in 1990, the energy vehicles, and renewable energy systems. Currently, he is investigating Research Initiation Award from the National Science Foundation in 1991 for the effective delivery of design skills in engineering education, particularly in his work on wind power generation, and the Lumley Research Award from the electrical engineering. College of Engineering, OSU, in 1995, 1999, and 2004, for his outstanding Dr. Rehman is the recipient of the Best Teacher Award from the College of research accomplishments. Engineering, UAE University, for the academic year 2002–2003.