REQUIREMENTS for compact propulsion systems are

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1 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 20, NO. 3, SEPTEMBER Electric Propulsion With Sensorless Permanent Magnet Synchronous Motor: Implementation and Performance Todd D. Batzel, Member, IEEE, and Kwang Y. Lee, Fellow, IEEE Abstract There has recently been considerable interest in using the sensorless permanent magnet synchronous motor (PMSM) for vehicle propulsion systems. While many sensorless PMSM techniques have been presented in the literature, few have discussed in detail the underlying hardware and implementation issues. This work focuses on the implementation and application of a sensorless PMSM strategy that is particularly well suited for vehicle propulsion systems. The selected sensorless PMSM technique is implemented in a real-time motor control system to form a sensorless electric drive prototype. The hardware, strategy and implementation issues associated with the development of the sensorless drive are discussed. Experimental results are included in order to demonstrate the robustness of the implementation and the effectiveness of the sensorless drive under transient operating conditions such as startup and speed reversal. Index Terms Brushless machines, digital control, electric vehicles, motor control, motor drives, propulsion, torque control, underwater vehicles, variable speed drives. I. INTRODUCTION REQUIREMENTS for compact propulsion systems are pushing the need to eliminate bulky driveshafts and gearing by placing a direct-drive motor at the exact location where torque is required. In such a configuration, the rotor position sensor that is normally required for vector control of the permanent magnet synchronous motor (PMSM) or induction motor (IM) often cannot be accommodated due to reliability concerns or physical constraints. Therefore propulsion systems are one of many applications that benefit from a position sensorless motor control strategy where stator windings themselves are used as the rotor position sensor. Of the electric machine configurations available for vehicle propulsion applications, there has been considerable interest in the PMSM. This attention is primarily due to the PMSM s superior power density, outstanding efficiency, and potential for quiet operation as compared to the IM and synchronous reluctance machine [1]. Electric vehicles for personal transportation systems are quite cost sensitive leading to the IM as the electric machine of choice for such applications. However, many at-sea propulsion systems have stringent acoustic restrictions [2]. As a result, the PMSM has received considerable attention in these Manuscript received July 27, 2004; revised February 2, This work was supported in part by the Office of Naval Research under Grant N G Paper no. TEC The authors are with the Department of Electrical Engineering, Pennsylvania State Altoona, Altoona, PA USA ( tdbl120@psu.edu; kwanglee@ psu.edu). Digital Object Identifier /TEC applications since it has been shown to be capable of quiet operation [3]. Several sensorless ac motor propulsion applications have been reported in the literature. The sensorless IM has been applied to several land-based electric vehicles. A model-based approach for sensorless IM torque control is used for electric vehicle propulsion in [4] and [5]. In [6], a speed and flux sensorless IM drive is applied to the propulsion of an electric bus. The reluctance variation of the interior permanent magnet PMSM is exploited in [7] and [8] to implement a torque control across the speed range of an electric vehicle. In [9] a sensorless PMSM drive for an experimental mini-van is presented where four motors are integrated with the wheel assembly to eliminate gearing and driveshafts. Similarly, [10] and [11] describe the sensorless PMSM propulsion system that has been applied to the unmanned undersea vehicle (UUV) with integrated motor(propulsor. In this paper, the development of a sensorless PMSM drive prototype for undersea propulsion is discussed with particular attention given to the implementation details and overall drive performance under various operating conditions. The hardware and software implementation of the drive is presented in great detail, including a discussion of the voltage measurement technique and its implication on sensorless system performance. Rotor angle and speed estimation accuracy is quantified for a wide range of operating conditions with an emphasis on troublesome transients through and around zero speed. Experimental results are used to illustrate the management of sensorless operation for transients through the low-speed region and the selection of a low-speed threshold associated with the observer operation. The robustness of the system to modeling uncertainties and load torque variation that is required in vehicle propulsion systems is also demonstrated experimentally. Such a comprehensive discussion of sensorless PMSM drive implementation and performance is not forthcoming in the present literature. II. SENSORLESS DRIVE IMPLEMENTATION In this section, the implementation of a model-based rotor position estimation technique [12] is described. Sensorless PMSM control is reviewed, and the hardware implementation of the proposed control technique is discussed. A. Control Electric drives are used to control the PMSM torque and speed in a closed-loop system. A resolver or encoder attached to the rotor shaft is normally used to provide the feedback required /$ IEEE

2 576 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 20, NO. 3, SEPTEMBER 2005 Fig. 1. Block diagram of sensorless PMSM drive system. by the torque and velocity control loops. In a sensorless PMSM drive, however, rotor position and velocity are supplied rotor angle and velocity estimators, respectively. The arrangement of the proposed controller for the sensorless PMSM prototype is shown in Fig. 1, where cascaded torque and speed control loops are shown. The angle and velocity estimation block shown in the figure uses sensed stator current and voltage to reconstruct the PMSM states of interest rotor angle and velocity. As shown in the figure, rotor position is required by the torque control loop and the velocity estimate is required by the speed controller. Note that although the outer speed control loop can be disabled for torque-only control algorithms, the speed estimate is still required by the angle estimation algorithm. The outer velocity loop operates on the difference between the speed reference and estimated speed to generate the torque reference (T ). The torque is controlled by the amplitude of the currents and their phase angle with respect to rotor position (ˆθ). The stator current amplitude is determined by I s = 2T 3λ m ( 2 P where P is the number of poles and λ m is the magnet flux linkage constant [13]. To minimize torque ripple and also satisfy the physical constraint that phase currents must sum to zero, balanced three-phase current references are used [13] as follows: [i a i b i c] T = Is[sin ˆθ sin(ˆθ 120 ) sin(ˆθ )] T. (2) The PWM current controller applies the required voltage, via the inverter, to the PMSM terminals to enforce the current references. Note that only two current sensors are required since just two of the phase currents are independent. A block diagram of the rotor position and velocity estimators is shown in Fig. 2. The sensed PMSM input voltage (u) is used to drive the reference model dynamics. The current predicted by the reference model (ŷ) is compared with the actual PMSM current (y) and the error (y ŷ) is operated on by the gain matrix G such that the estimated PMSM states (ˆx) converge toward the actual, but not directly measurable, PMSM states (x). The estimated PMSM states are used to determine rotor angle (ˆθ) as described in [14]. The velocity estimate (ˆω) is a required system parameter and therefore must be estimated ) (1) Fig. 2. Fig. 3. Block diagram of rotor position estimator. Block diagram of sensorless drive hardware. using the algorithm indicated in the figure. In the implementation, steady-state velocity estimation is enhanced by an adaptive velocity estimator [15]. B. Hardware Implementation In this section, the processing hardware, associated power electronics, and sensors used to realize the PMSM sensorless prototype are presented and implementation details are discussed. The hardware configuration was intended to provide high performance at low cost while maintaining the adaptability required to support extensive test and evaluation of a prototype sensorless propulsion system. A diagram of the overall hardware implementation is included in Fig. 3. 1) Computing Hardware: Current digital signal processing (DSP) hardware designed for electric drive applications uses fixed point arithmetic and does not usually provide full highlevel language software development support. As a result, software development for the fixed point DSP is a complex and time-consuming process. The floating point DSP typically provides high-level language support but lacks the essential motor control peripherals such as PWM signal generators and A/D conversion hardware. To facilitate timely software development and maintenance for the prototype sensorless drive, the computing hardware utilizes both a floating-point and a fixed-point DSP. The floating point DSP (Analog Devices Sharc) executes the rotor position and velocity estimation algorithm at 100-µs and 2-ms intervals, respectively. The fixed-point DSP (Analog

3 BATZEL AND LEE: ELECTRIC PROPULSION WITH SENSORLESS PMSM 577 Devices ADMC401) is equipped with motor control peripherals, and is used to implement the current controller, sample the stator voltages and currents, and generate PWM signals. The two DSPs communicate directly through a dual-port memory bank resident on the floating point DSP. The dualport memory allows the two processors to share data with little overhead and no additional logic. A personal computer that communicates with the floating point DSP via a serial link acts as the user interface-providing a graphical user interface for operating the drive, displaying the drive performance status, and storing relevant operational data. Despite the additional hardware cost associated with the adoption of the dual-processor architecture, the ease and efficiency of software development and maintenance is viewed as an overall cost reduction over the life-cycle of the system. However, for high-volume commercial drives, there are fixed-point motor control DSPs (such as TMS320F2812 or ADSP-992) that are capable implementing the entire control system. 2) Sensors: The PWM inverter makes accurate voltage and current sensing problematic due to the switching nature of the applied voltage. In the experimental drive, direct sensing of the stator voltage is used. Therefore, both electrical isolation and low-pass filtering are required in order to isolate power and signal grounds, and to remove high frequency switching components of the phase voltage. The direct stator voltage-sensing configuration is shown in Fig. 3, where a resistive divider is used to scale the motor terminal voltage. The voltage transducer (LEM CV3-1000) provides an electrically isolated output signal, which is low-pass filtered before being sampled by the fixed point DSP. The voltages are sampled synchronously at the PWM center point to reduce the effect of PWM ripple on the sampled data. The low-pass filter is a fourth-order Bessel filter with a cutoff frequency of 5 khz. Despite the use of a line filter between the inverter and PMSM, a low-pass filter is required to further remove high frequency switching noise from the voltage. The drawback, however, is that the filter introduces a time delay of approximately 68 µs to the voltage signal. The consequence of the delay is discussed in a later section. Many proposed sensorless drives employ an alternate form of voltage sensing where the dc link voltage and PWM duty cycle commands are used to reconstruct phase voltage. This indirect voltage sensing method is especially problematic in systems where a line filter is used since the filter states must also be known to accurately determine the motor terminal voltage. Furthermore, nonideal switching characteristics and deadtime effects in the inverter stage result in even greater voltage reconstruction error. This highlights the advantage of direct voltage sensing: inverter deadtime and nonideal characteristics of the inverter components are accounted for since the actual phase voltage is sensed. Experiments have demonstrated that direct sensing of stator voltage yields improved sensorless performance especially for operating conditions such as startup, reversal, and very low-speed [14]. The disadvantage of the direct voltage sensing method is the increased hardware cost associated with the isolated voltage sensors and low-pass filters. In any PMSM control system, current feedback is required to close the torque control loop. The current waveform, like the voltage signal, contains high frequency components due to the PWM inverter. The currents, like the phase voltages, are sampled synchronously with the PWM centerpoint to remove all sideband harmonics. The prototype sensorless drive uses 200-Å current transducers (LEM Model LA 205-S). 3) Inverter: The power output stage of the sensorless electric drive system serves to apply commanded voltages to the PMSM terminals. In PMSM drive applications, the most popular power stage consists of a voltage source inverter (VSI), which is a dc-to-ac converter built with IGBTs and configured to deliver bipolar current waveforms to the motor. In the prototype sensorless drive, a commercially available VSI is used (Trace Technologies model IPM1206). This inverter operates at dc bus voltages of up to 750 V, and delivers 150 kw at PWM switching frequencies up to 20 khz. The motor control DSP provides the PWM switching signals to each of the six IGBT gate drives of the power output stage through a fiber-optic interface. Similarly, the power stage provides fault enunciation for each IGBT through the interface. The fiber-optic communication is advantageous due to its immunity to electrical noise and inherent electrical isolation of power electronics and signal-level systems. 4) Filter: The fast voltage rise time delivered by modern inverter assemblies has many implications, including an increase in common-mode currents, and damaging overvoltage conditions at the motor terminals. Overvoltage can cause early breakdown of the winding insulation in the motor and common mode currents may result in increased EMI and increased bearing wear due to the introduction of shaft currents. A line filter is used in the propulsion system prototype to limit the rise time of the voltage waveform applied to the motor by the inverter assembly. The output filter is comprised of a single stage LC filter, as shown in Fig. 3. The LC filter forms a low pass filter with a 2nd order 40 d/b (decade attenuation characteristic from the cutoff frequency of 1/(2π LC) (3) where the cutoff frequency for the filter implementation is 300 Hz. The electrical frequency of the motor is 66.7 Hz at the maximum speed of 1000 r/min. Therefore, the filter has little effect on the fundamental motor voltage, but attenuates the high frequencies introduced by the switching electronics. 5) Motor: The PMSM used in the sensorless drive prototype is manufactured by Electrodynamics Corp. division of Electric Boat, and is shown in Fig. 4. The stator phase coils may be configured in series or parallel for low-speed, high-torque or high-speed, low-torque operation, respectively. The machine parameters are shown in Table I. 6) Software: In the implementation, a digital controller is constructed from the continuous-time observer equations given in [12] using the forward Euler integration method. The floating point and fixed point DSPs were programmed in C and Assembly language, respectively.

4 578 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 20, NO. 3, SEPTEMBER 2005 Fig. 4. The 70-HP PMSM used in sensorless electric drive studies. TABLE I MOTOR PARAMETERS Fig. 6. Angle estimation accuracy versus speed. into the appropriate PWM switching patterns and delivered to the power stage. The digitized observer equations are updated on the floating point DSP every 100 µs by a scheduled interrupt. The DSP reads the stator voltages and currents from shared memory, and operates on them to update the state variables and rotor position estimate. The estimated rotor angle, rotor velocity, and torque command are then placed into shared memory for the fixed point DSP to access. In the implementation, the PI-based velocity controller is carried out every twentyth scheduled interrupt. Therefore, the estimated velocity, torque reference, and velocitydependent observer gain matrix are updated at 2-ms intervals. Fig. 5. Program flow diagram for sensorless drive software. The software flow for the dual-processor system is shown in Fig. 5. As discussed previously, the fixed point DSP acquires the stator currents and voltages and executes the current control algorithm at the PWM frequency of 20 khz. The raw voltages and currents are transformed into the two-phase stationary reference frame using the Clarke transform [16], and the results are placed into shared memory for consumption by the floating point DSP. The torque reference T for the current controller that is generated by the velocity control loop on the floating point DSP must be fetched from shared memory to serve as the current reference. Current control is then carried out in the rotating d-q reference frame using the technique described in [17]. The current control loop yields voltage references, which are converted III. EXPERIMENTAL RESULTS In this section, the performance of the sensorless drive prototype is examined with respect to angle and speed estimation accuracy, robustness to modeling uncertainty, and external disturbance rejection. To evaluate performance, experiments are performed where the implementation described in the previous section executes the complete PMSM control algorithm in real time. The experimental system consists of the 70 HP PMSM shown in Fig. 4, whose shaft is coupled to a pump that provides an adjustable load torque. A quadrature shaft encoder with counts per mechanical revolution is attached directly to the motor shaft. The encoder is used only to quantify the accuracy of the rotor angle and velocity estimates. For this section, electrical radians will be assumed for all results unless otherwise stated. A. Steady-State Rotor Position Estimation To determine angle estimation accuracy, experiments were performed on the drive across the motor s speed range. The results for the high-speed winding configuration are shown in Fig. 6, which shows the mean and standard deviation of the rotor position estimation error. For these experiments, the PMSM was essentially unloaded. It will be demonstrated in a subsequent

5 BATZEL AND LEE: ELECTRIC PROPULSION WITH SENSORLESS PMSM 579 section that angle estimation accuracy is largely independent of the load torque, as predicted in [14]. Although the figure demonstrates excellent rotor angle estimation accuracy, the magnitude of the mean error shows an increase proportional to rotor speed. This degradation in performance with increasing angular velocity is due to delay introduced by the necessary low-pass filtering of the phase voltages and the sampling delays introduced by the discrete-time implementation of the algorithm. The net effect is a linear phase shift of the sensed phase voltages given by phase shift (rad.) = 167 µs ω (4) where the sum of the known delays is 167 µs and ω is the angular shaft speed in electrical radians per second. The expected voltage signal delay is included in Fig. 6, and is seen to correspond well with the angle estimation error shown on the same plot. This confirms the results of the error analysis conducted in [14], where stator voltage phase shift was shown to develop an angle estimation error equal to that phase shift. This result demonstrates the disadvantage of direct voltage sensing discussed in the hardware implementation. However, the angle estimation error due to the stator voltage phase shift could easily be removed since the phase shift is well characterized in (4). B. Low- and Zero-Speed Operation In sensorless PMSM drives, rotor position estimation at and around zero speed is problematic due to the unobservability of rotor angle under those operating conditions [12]. Several starting methods have been suggested where a rotor angle dependent stator inductance is exploited to obtain rotor position [18], [19]. However, this technique is ineffective for the PMSM with nonsalient rotor that is used in the targeted propulsion application. In the prototype system, very low speed operation is managed by setting the observer gain matrix G equal to zero whenever angular velocity falls below an experimentally determined low-speed threshold. With the observer feedback disabled, the internal PMSM model runs without correctional feedback, and the estimated rotor angle is driven solely by the angular velocity estimate: ˆθ(t) = ˆωdt + ˆθ 0 (5) where ˆθ 0 is the initial angle estimate at t =0. For the speed estimation algorithm described in [12] and shown in Fig. 2, it is difficult to determine the direction of rotation at low speed due to the poor signal-to-noise ratio of the stator voltage. As a result, the estimated angular velocity exhibits occasional polarity reversals at very low-speed. When the velocity estimate is of incorrect polarity with observer feedback enabled, the system is unstable since the time-varying feedback matrix G depends on the estimated velocity for accurate eigenvalue placement [14]. This undesirable scenario causes the angle estimate to diverge rapidly from the actual rotor angle. However, if observer feedback is disabled at low speeds, short and infrequent errors in the estimated speed polarity will not produce severe deviation in the angle estimate given in (5) over short periods of time since the magnitude of ˆω is very low. Fig. 7. Low-speed velocity estimating, low-speed winding. Fig. 7 shows the operation of the sensorless drive at a low speed slightly higher in magnitude than the low-speed threshold. In the figure, the estimated velocity tracks the actual velocity except for occasional velocity polarity errors, which are seen to generate a rapid divergence of the estimated rotor angle from the actual angle. In this case, the duration and frequency of occurrence of the velocity polarity error are short enough such that the angle estimate remains close to the actual value despite the occasional divergences. In general, however, operation in this mode is not recommended. The low-speed threshold is determined experimentally by observing the estimated velocity. It is advantageous to choose the low speed threshold such that polarity reversals rarely occur at operating speeds above that threshold. A threshold whose magnitude is as small as possible is advantageous since it reduces the time during which the observer feedback is disabled during startup and speed reversals. However, a threshold set too low will result in failure at startup or speed reversal if velocity estimation polarity errors occur regularly while the observer is enabled. This emphasizes the importance of reducing measurement noise, since higher signal-to-noise permits the use of lower speed threshold levels. In the experimental system, the low speed threshold is 7.5 rad/s, which corresponds to 1.8% of the rated motor speed. However, it has been observed that a low-speed threshold of less than 1% of rated motor speed is achievable. C. Velocity Estimation For certain electric propulsion systems such as undersea vehicles, it is critical that accurate velocity feedback is available to permit precise tracking of the desired vehicle speed. In the absence of a position or velocity sensor, estimated velocity must be used to generate the feedback required for speed control. In the sensorless drive prototype, the stator voltage and current are used to estimate velocity, and an adaptive estimator is employed to ensure steady-state velocity estimation accuracy [11], [15]. As demonstrated in Fig. 7, velocity estimation is challenging at extremely low speeds, but performance is enhanced at higher

6 580 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 20, NO. 3, SEPTEMBER 2005 Fig. 8. Velocity estimation accuracy at 300 rev/min. speeds due to an improved signal-to-noise ratio so that the direction of rotation is easily determined. Velocity estimation performance at higher speed is depicted in Fig. 8, which shows that the velocity estimation is accurate to within 1.4 r/min of the actual speed of 300 r/min. A 20-Hz ripple in the velocity estimate corresponds to the fundamental electrical frequency of the stator voltage and current. This perceived ripple in the velocity feedback forces the actual rotor speed to deviate from commanded speed at the same frequency of 20 Hz. It should be noted that the velocity estimate could be filtered to remove the undesirable ripple at the expense of transient system performance. Fig. 9. Startup from zero speed with load of 110 Nm at 200 r/min. D. Transient Operation A significant challenge associated with sensorless drives is maintaining control during startup or speed reversal transients, where the rotor velocity passes through the problematic zero speed range. In this section, system performance under these transient conditions is examined experimentally. 1) Startup: At startup, the sensorless controller cannot guarantee proper torque polarity and smooth acceleration unless the initial rotor angle is known with sufficient accuracy. This requirement is addressed by the adoption of the so-called forcedalignment technique [20]. The forced alignment technique applies a dc current to the stator before startup. The dc current acts to align the permanent magnet field with the magnetic field generated by the stator excitation, and forces the rotor to a known initial position ˆθ 0. Since ˆθ 0 is known, torque of known polarity and magnitude can be applied to the PMSM at startup and the rotor position estimator operated temporarily in open loop according to (5). The initial torque must be sufficient to produce rotational velocity that exceeds the low-speed threshold before the angle estimation error becomes excessive. When the angular velocity surpasses the low-speed threshold, observer feedback is established, and the angle estimation error quickly converges toward zero. In the prototype drive system, a very reliable sensorless startup has been accomplished. To illustrate the performance after forced alignment, consider the startup depicted in Fig. 9, where a speed of 30 r/min is commanded at 0.4 s, and the initial angle estimation error after forced alignment is approximately 5 electrical degrees. Initially, torque is applied to the rotor and the angle estimator operates without observer feedback at the subthreshold angular velocity. The low-speed threshold of 7.5 rad/s is exceeded at 0.6 s, enabling feedback correction so that the estimated rotor angle begins to converge toward zero. Just after the low-speed threshold is exceeded, a brief speed estimate polarity reversal temporarily interrupts the rotor angle estimate s convergence toward zero. Following the polarity reversal, the angle estimation error again converges toward zero and final steadystate velocity just above the low-speed threshold is reached. 2) Speed Reversal: During a speed reversal, the rotor position observer temporarily operates without feedback while the estimated speed is below the preset threshold. Such open-loop operation where the angle estimate is updated by (5) has been shown through experimentation to be acceptable for short time intervals, such as the speed reversal shown in Fig. 10. In the figure, the estimated velocity magnitude drops below the threshold at 1 s, and the observer operates without feedback until 2.25 s, where feedback is restored. From the figure, it is seen that the open-loop model produces sufficient angle estimation accuracy, or ride-through capability, for short-term operation below the low-speed threshold. In the figure, the angle estimation error

7 BATZEL AND LEE: ELECTRIC PROPULSION WITH SENSORLESS PMSM 581 Fig. 11. Estimation accuracy versus load torque at constant speed: low speed winding configuration. Fig. 10. Speed reversal of PMSM. that appears while operating without observer feedback is attributed to the difficulty in determining rotor velocity at such low speed. In the prototype sensorless drive, speed reversals were found to be extremely reliable. E. Parameter Uncertainty and Disturbance Rejection In this section, the robustness of the developed sensorless PMSM drive is examined with respect to parameter uncertainty and external disturbances. In practical applications, machine parameters vary due to temperature effects and external load torque varies considerably with the operating environment. An effective rotor position estimator must be robust to such variability in the system. It was shown in [14] that variations in the load torque are not expected to affect rotor position estimation accuracy since the model used in the observer does not contain mechanical variables such as torque, friction, and inertia in the state equations. The predicted immunity of rotor position estimation accuracy to load torque is verified in Fig. 11, where little variation in angle estimation accuracy is seen over a wide load torque range. The robustness of the sensorless drive performance to load torque represents a tremendous advantage in propulsion applications, since it removes the need for estimation or direct measurement of the load torque. Fig. 12. Estimation accuracy versus load at constant speed: low speed windings with stator inductance detuned by 50%. In [14], it was concluded that inductance modeling uncertainty introduces an angle estimation error proportional to the product of inductance uncertainty and the stator current amplitude. In the experiment depicted in Fig. 12, the stator inductance used by the observer PMSM model is intentionally detuned by 50% from its actual value. Comparison of Figs. 11 and 12 show that rotor position estimation accuracy is adversely affected by the changing stator current when inductance modeling error is present. Thus, the experimental result is consistent with the predicted effect of the inductance modeling uncertainty. Stator resistance is a parameter used in the PMSM model to help estimate the magnitude of the airgap flux. However, uncertainty in the modeling accuracy of stator resistance was found to have little effect on angle estimation accuracy under normal

8 582 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 20, NO. 3, SEPTEMBER 2005 that the lack of a velocity sensor presents a serious challenge in achieving very high accuracy velocity tracking. ACKNOWLEDGMENT The authors gratefully acknowledge the contributions of B. Kline, J. Mickey, and M. Turner for their efforts in this program. REFERENCES Fig. 13. values. Estimation accuracy with (top) actual and (bottom) detuned resistance operating conditions [14]. To experimentally verify this prediction, angle estimation error measurements were performed at low-speed with the PMSM model stator resistance parameter intentionally detuned. The results of this experiment are shown in Fig. 13, where the top plots show performance with a well-modeled stator resistance. The bottom plots show system performance when the PMSM model uses a resistance parameter that is increased by 50% from its known value. In both cases, the motor was operating at 25 r/min with a load of 15 Nm. From a comparison of the results in Fig. 13, the prediction set forth in the estimation error analysis [14] is confirmed. IV. CONCLUSION This work has presented the implementation details and experimental results associated with a sensorless electric drive for the PMSM. The selected sensorless strategy is implemented in a real-time motor control system. The underlying hardware and software for the motor controller are presented in detail, and implementation issues are discussed. Extensive experimentation is then used to evaluate the effectiveness of the proposed sensorless algorithm as well as the implementation. Startup and speed reversal transient operation are also discussed, and experimental results demonstrate the effectiveness of the sensorless drive in managing these troublesome operating conditions. Results show that the sensorless drive is robust to modeling uncertainty and external disturbances while providing extremely accurate rotor position estimation. In summary, it is the immunity of the sensorless system performance to variations in electrical and mechanical parameters and external disturbances that make it ideal for many electric propulsion applications which must operate satisfactorily in a wide range of environments. A potential area for future improvement of the sensorless drive is associated with the velocity controller. Although the velocity control performance shown in this study is adequate for many propulsion applications, experimental results show [1] L. Chang, Comparison of AC drives for electric vehicles A report on expert s opinion survey, IEEE Aerosp. Electron. Syst. Mag.,vol.9,no.8, pp. 7 11, Aug [2] C. J. Egan and C. M. Orndorff, Electric propulsion: Fleet readiness at affordable costs, IEEE Aerosp. Electron. Syst. Mag., vol. 11, no. 5, pp , May [3] R. McConnell, Acoustic analyses and tests of various 2-HP motor configurations, in Proc. Naval Symp. Electric Machines,Jul.1997,pp [4] B. Asaii, D. F. Gosdon, and S. Sathiakumar, A simple high efficient torque control for the electric vehicle induction machine drives without shaft encoder, in Proc. 26th Annu. IEEE Power Electronics Specialists Conf., vol. 2, Jun. 1995, pp [5] J. Faiz, M. B. B. Sharifian, A. Keyhani, and A. B. Proca, Sensorless direct torque control of induction motors used in electric vehicles, IEEE Trans. Energy Conversion, vol. 18, no. 1, pp. 1 10, Mar [6] F. Peng, Speed and flux sensorless field oriented control of induction motor for electric vehicles, in Proc. 15th Annu. IEEE Applied Power Electronics Conf. Exposition, vol. 1, 2000, pp [7] N. Patel, T. O Meara, J. Nagashima, and R. Lorenz, Encoderless IPM traction drive for EV/HEV s, in Proc. Conf. Rec IEEE Industry Applications Conf., vol. 3, Sep. 2001, pp [8] R. Masaki, S. Kaneko, T. Sawada, and S. Yoshihara, Development of a position sensorless control system on an electric vehicle driven by a permanent magnet synchronous motor, in Proc. Power Conversion Conf. 2002, Osaka, Japan, 2002, pp [9] C. S. Namuduri and B. U. Murty, High power density electric drive for an hybrid electric vehicle, in Proc. Conf. 13th Annu. Applied Power Electronics, vol. 1, Feb. 1998, pp [10] T. D. Batzel, D. P. Thivierge, and K. Y. Lee, Application of sensorless electric drive to unmanned undersea vehicle propulsion, in Proc. 15th IFAC World Congr. Automatic Control, vol. P, Barcelona, Spain, Jul. 2002, pp [11] T. D. Batzel and D. P. Thivierge, Electric drive for 21 UUV, in Proc. 3rd Naval Symp. Electric Machines, Philadelphia, PA. [12] T. D. Batzel and K. Y. Lee, Slotless PMSM operation without a high resolution rotor angle sensor, IEEE Trans. Energy Conversion, vol. 15, no. 4, pp , Dec [13], Commutation torque ripple minimization for permanent magnet synchronous machines with Hall effect position feedback, IEEE Trans. Energy Conversion, vol. 13, no. 3, pp , [14] T. D. Batzel, Electric propulsion using the permanent magnet synchronous motor without rotor position transducers, Ph.D. dissertation, Dept. Elect. Eng., Pennsylvania State Univ., University Park, PA, [15] J. Kim and S. Sul, New approach for high performance PMSM drives without rotational position sensors, IEEE Trans. Power Electron., vol. 12, no. 5, pp , Sep [16] E. Clarke, Circuit Analysis of AC Power Systems, Vol. I, Symmetrical and Related Components. New York: Wiley, [17] D. Y. Ohm and R. J. Oleksuk, On practical digital current regulator design for PM synchronous motor drives, in Proc. 13th Annu. Applied Power Electronics Conf. Exposition, vol. 1, Feb. 1998, pp [18] M. Schroedl, Sensorless control of AC machines at low speed and standstill, Industry Applications Society Conf. Rec.,vol.1,pt.1,pp , [19] N. Matsui and T. Takeshita, A novel starting method of sensorless salientpole brushless motor, Conf. Rec. IEEE Industry Applications Society Annu. Meeting, vol. 1, pp , Oct [20] N. Matsui and M. Shigyo, Brushless DC motor control without position and speed sensors, IEEE Trans. Ind. Applicat., vol. 28, no. 1, pp , 1992.

9 BATZEL AND LEE: ELECTRIC PROPULSION WITH SENSORLESS PMSM 583 Todd D. Batzel (M 00) received the B.S. and Ph.D. degrees in electrical engineering from Pennsylvania State University, University Park, in 1984 and 2000, respectively, and the M.S. degree in electrical engineering from the University of Pittsburgh, Pittsburgh, PA, in Currently, he is an Assistant Professor of electrical engineering at Pennsylvania State University, Altoona. His research interests include electric machines, electric motor controls, power electronics, artificial intelligence applications to control, and embedded control systems. Kwang Y. Lee (F 01) received the B.S. degree in electrical engineering from Seoul National University, Seoul, Korea, in 1964, the M.S. degree in electrical engineering from North Dakota State, Fargo, in 1968, and the Ph.D. degree in systems science from Michigan State University, East Lansing, in Currently, he is a Professor of electrical engineering and is Director of Power Systems Control Laboratory at Pennsylvania State University, University Park. He has also been with Michigan State University, Oregon State University, and the University of Houston. His interests include power systems operation and planning, expert systems, and intelligent system applications to power systems. Dr. Lee is an Associate Editor of the IEEE TRANSACTIONS ON NEURAL NETWORKS and an Editor of the IEEE TRANSACTIONS ON ENERGY CONVERSION.