Improved efficiency of a fan drive system without using an encoder or current sensors Tian-Hua Liu, Jyun-Jie Huang Department of Electrical Engineering, National Taiwan University of Science Technology, Taipei, Taiwan E-mail: liu@mail.ntust.edu.tw Published in The Journal of Engineering; Received on 6th January 2018; Accepted on 31st January 2018 Abstract: This study proposes a closed-loop fan drive system without using an encoder or current sensors. The cost size of the fan drive system, therefore, are significantly reduced. By using low-resolution three-phase Hall-effect position sensors, three-phase sinusoidal pulse-width modulation voltages are generated to control a permanent magnet synchronous motor (PMSM) that a fan is connected to. Two methods, including the back-emf estimation method the impedance-phase estimation method, are proposed to determine the input voltage amplitude phase of the PMSM. A digital signal processor, TMS 320F28335, is used to execute the control algorithms. The proposed impedance-phase estimation method is 25% more efficient than the back-emf estimation method at low-speed operating range below 1000 r/min. A detailed sensitivity analysis of the motor parameter variations is discussed. The implemented drive circuit, which is very compact, can be attached at the back of the PMSM fan. Several experimental results are provided. 1 Introduction Permanent magnet synchronous motor (PMSM) drive systems are becoming more more popular due to their high torque/ ampere, high-power density, high efficiency, robustness, easy control characteristics. For a fan drive system, the transient responses, load disturbance responses, tracking responses are not so important. However, the characteristics of low cost, low noise, high reliability, high efficiency are required. As a result, a lot of fan drive systems use sensorless techniques to reduce the size cost maintain required performance. Several researchers have investigated sensorless drive systems. For example, Jung et al. [1] proposed a control method to reduce the commutation torque ripple for sensorless brushless DC drive systems. Murakami et al. proposed an encoderless interior permanent magnet synchronous motor (IPMSM) drive system using a pulse-voltage injection method. By detecting the high-frequency current, the rotor position was estimated [2]. Improvement of the efficiency for motor drive systems is very important for saving energy environmental issues. Many researchers have proposed different methods to improve the efficiency of motor drive systems. For example, Consoli et al. proposed an effective energy-saving control for industrial IPMSM drive systems. The drive system was implemented without using rotor position or voltage sensors; it only used a single current sensor [3]. Uddin et al. proposed an online efficiency optimisation of a fuzzy-logic controller for IPMSM drive systems [4, 5]. Cavallaro derived a loss minimisation control algorithm for an inverter-fed PMSM that reduced power loss of the drive system but did not deteriorate its dynamic performance [6]. Sreejeth proposed a particle swarm optimisation method to improve the efficiency of a vector-controlled PMSM drive system [7]. Deng et al. improved energy efficiency by controlling the DC-bus voltage of the inverter for an electric vehicle. The overall efficiency was increased by nearly 0.5% [8]. Mademlis et al. proposed a loss minimisation method for a vector-controlled PMSM drive system based on his motor loss model. The optimum value of the d-axis current was derived to improve the efficiency of the drive system [9]. Fan drive systems have been widely used in cooling computers rooms. Some researchers have focused on using permanent magnet motors to control fan drive systems. For example, Dunkl et al. investigated the design constraints of small single-phase permanent magnet brushless DC drives for fan applications. By using varied materials dimensions, the different electrical mechanical characteristics of the drive systems were obtained. In addition, the limitations of the drive systems were analysed [10]. Noda et al. investigated a totally enclosed fan cooled traction motor. By using this motor, the dust problem was solved the acoustic noise was reduced [11]. Lelkes et al. proposed a brushless DC motor for fan applications. By automatically adjusting a commutation angle of the motor, the power consumption of the drive system was reduced. As a result, the efficiency of the system was improved [12]. Saxena studied the weight cost of different BLDC permanent magnet motors for ceiling fans. The results showed that the weight of a ferrite motor is about 1.5 times that of a bonded NdFeB motor; however, the cost of a ferrite motor is only about 78% that of a bonded NdFeB motor [13]. Recently, Sun et al. investigated a magnetic polarity self-sensing method for startup of a PMSM in a fan system. A pulsating voltage vector was injected to detect the d-axis rotor position. Therefore, the fan system can smoothly start up run [14]. However, only a few papers have investigated real applications of low-cost efficiency improvement size reduction for sensorless fan drive systems. For example, Lee et al. [15] proposed a sensorless PMSM compressor drive system that increased efficiency by nearly 2% via dual-mode operation. To help fill this void, in this paper, two efficiency improvement size reduction methods are proposed for a fan drive system without an encoder or current sensors. To the authors best knowledge, this idea is original has not been presented in previously published papers [1, 10]. Experimental results show that the proposed method is nearly 25% more efficient than the back-emf sensorless method at lowspeed operating range below 1000 r/min. 2 System description rotor estimation 2.1 Description of the proposed fan drive system The proposed fan drive system shown in Fig. 1 includes a speed controller, an advance angle controller, two mode selection switches, a six-step square wave pulse-width modulation (PWM), a voltage vector comm to an a b c voltage comm transformation, a space vector PWM, a rotor angle/speed estimator, lowresolution three-phase Hall-effect position sensors sensing circuit, an inverter, a PMSM, a fan. When the motor accelerates from 0 to 150 r/min, a six-step square-wave current comm is used. After the fan reaches 150 r/min, a three-phase sinusoidal current comm is generated based on the signals of the lowresolution three-phase Hall-effect position sensors to determine the three-phase sinusoidal voltage comms of the inverter. By
Fig. 1 Block diagram of the proposed fan drive system using the three-phase sinusoidal voltage comms, the torque pulsation noise can be reduced when compared to the six-step square-wave current comm. A closed-loop fan drive system can thus be achieved. To improve the efficiency of the fan drive system, two methods, including a back-emf method an impedance-phase method, are proposed here. The details are discussed as follows. 2.2 Rotor position estimation In this paper, only three-phase low-resolution Hall-effect position sensors are used to provide feedback position signals. To improve the resolution of the rotor position, two estimators, which include an integral estimator a polynomial approximation estimator, are compared here. First, an integral estimator is discussed as follows. The speed obtained from the three-phase Hall sensor is v re (k) = p/3 T(k) where v re is the calculated electric rotor speed derived from the low-resolution Hall-effect sensors T(k) is the time interval between two Hall-effect sensors at the k sampling time. By using the rotor speed calculated in (1), it is possible to estimate the rotor position at each sampling time as follows [11]: (1) û re (k) = v re (k 1)Dt + û re (k 1) (2) where û re (k) is the k estimated rotor position, k is the number of the sampling interval, v r (k 1) is the (k 1) sampling speed, Dt is the sampling interval of the speed loop, û re (k 1) is the (k 1) estimated rotor position. In the real world, the estimated rotor position is not only related to the rotor speed but also to the acceleration rate, deceleration rate, initial rotor position. After measuring the rotor position at k,(k 1), (k 2) sampling time, one can obtain the following approximation backward equation [12]: u re (k) 1 T(k) T(k) 2 u 0 (k) u re (k 1) = 1 T(k 1) T(k 1) 2 v re (k) (3) u re (k 2) 1 T(k 2) T(k 2) 2 0.5a re (k) According to (3), it is not difficult to derive û 0 (k) ˆv re (k) = 0.5â re (k) 1 T(k) T(k) 2 1 T(k 1) T(k 1) 2 1 T(k 2) T(k 2) 2 1 u re (k) u re (k 1) u re (k 2) (4) where û 0 is the estimated initial rotor position, ˆv re is the estimated speed, â re is the estimated acceleration. Finally, one can estimate rotor position at the Dt delay-time after the k sampling time as follows: û re (k + Dt) = û 0 (k) + ˆv re (k)dt + 1 2 âre (k)dt2 (5) where Dt is the delay time between the estimated time the k sampling time. 3 Efficiency enhancement methods Two efficiency improvement methods are proposed in this paper. The basic principles are discussed as follows. When the phase-current is in-phase with the back-emf, the output power reaches its maximum power the reactive power is reduced to zero. As a result, the efficiency of the drive system can be significantly improved. Although it is possible to use an encoder three-phase current sensors to achieve field orientation control, it results in increased size cost. The details of efficiency enhancement methods are discussed as follows. The d q-axis voltage of a dynamic PMSM can be expressed as follows: [ ] [ ][ ] [ ] [ ] v d r = s v re L s id 0 d i + + L d v q v re L s r s iq v re l s (6) m dt iq where v d is the d-axis voltage, v q is the q-axis voltage, r s is the stator resistance, v re is the electric frequency, L s is the self-inductance, i d is the d-axis current, i q is the q-axis current, l m is the flux linkage of the permanent magnet, (d/dt) is the differential operator. The electromagnetic torque is expressed as T e = 3 P 2 2 l m i q (7)
where T e is the electromagnetic torque, P is the pole number of the PMSM. The motor speed is d dt v r = 1 J (T e T L Bv r ) (8) increased. Fig. 2b shows the situation when the current vector î s leads the q-axis. In this case, the q-axis voltage v q is smaller than the back-emf voltage E q, which is equal to l m v re. To make the current vector î s move closer to the q-axis, u adv is decreased. where (d/dt) is the differential operator, J is the inertia of the motor load, T L is the external load, B is the viscous coefficient of the motor load. To obtain maximum torque/ampere, the d-axis current is set to zero then (7) is derived. As a result, the copper loss of the motor is reduced the efficiency is improved. To achieve this goal, the entire amplitude of the current vector î s is located in the q-axis expressed as follows: î s = i 2 q + i 2 d = i q (9) According to (9), an encoder is required to detect the q-axis position. To exclude the encoder current sensors, a new method to estimate the back-emf is proposed as follows. 3.1 Back-EMF estimation method Generally speaking, the resistance voltage drop is insignificant when compared to the back-emf voltage. Fig. 2a shows a phasor diagram when the current vector î s lags behind the q-axis. In this case, both L d L q are equal to L s the q-axis voltage v q is higher than the back-emf voltage E q, which is equal to l m v re. To make the current vector î s move closer to the q-axis, u adv is Fig. 3 Phasor diagram when the stator current vector is located in the q-axis Fig. 2 Phasor control diagrams a Current vector lags behind q-axis b Current vector leads q-axis c Control diagram Fig. 4 Phasor diagrams of the impedance-phase method a Current lagging b Current leading c Control diagram
According to above, it is feasible to adjust the desired comm of the advance angle u adv, which is shown in Fig. 2c. By comparing the q-axis voltage vq the back-emf voltage Eq, a control algorithm that adjusts the u adv is proposed here. First, it is feasible to define the error as e1 = vq i q rs Eq = vq _ k vre lm (10) where e1 is the estimated error, i q is the estimated q-axis current, k is the estimated stator resistance voltage drop. Next, by _ using the error signal, it is possible to determine the advance angle u adv as follows: u adv (k) = KP e1 (k) + u z = tan 1 vre Lq rs (14) Comparing (13) (14), we can conclude that the angle u z is related to the motor parameters speed. As a result, the estimation of the resistance voltage drop is not required. According to experimental results, this impedance-phase estimation method not only simplifies the control algorithm but also improves the estimation accuracy. Fig. 4a shows the condition when the current vector i s lags behind the q-axis Fig. 4b shows the condition when the k e1 (k) + uadv (0) KI (11) k=1 where u adv is the advance angle comm of the voltage vector, k is the kth sampling interval, e1 is the error signal, KP is the proportional gain, KI is the integral gain, uadv (0) is the initial value of the advance angle comm. According to (11), it is possible to adjust the uadv to force the error between the computed back-emf vq i q rs the desired back-emf Eq to decrease quickly. 3.2 Impedance-phase estimation method iq rs is unknown. To In the real world, the resistance voltage drop _ improve the back-emf estimation method, the impedance-phase estimation method is proposed here. Fig. 3 shows the desired voltage vector v s when the stator current vector i s is located in the q-axis. According to Fig. 3, it is not difficult to derive the desired advance angle u adv as follows: vre Lq iq 1 uadv = tan (12) rs iq + vre lm In (12), vre lm is equal to Eq. Next, it is not difficult to obtain v z = rs iq + jvre Lq iq Fig. 5 Equivalent d- axis q-axis circuits a d-axis b q-axis (13) Fig. 6 Implemented fan drive system a Block diagram b Hardware c Fan PMSM
current vector î s leads the q-axis. The amplitude of the voltage vector ˆv z its phase angle can be expressed as ˆv z = ˆv 2 s + Eq 2 2 ˆv s Eq cos u adv (15) ( u z = p cos 1 ˆv 2 z + Eq 2 ˆv 2 ) s 2 ˆv z Eq (16) After that, we can define the error between the comm angle u z the measured angle u z e 2 = u z u z (17) 3.3 Sensitivity analysis of motor parameter variations According to (6), it is not difficult to determine the PMSM at a steady-state condition is expressed as follows: v d = r s i d v re L s i q (19) v q = r s i q + v re L s i d + v re l m (20) The d-axis q-axis equivalent circuits are shown in Figs. 5a b. When the d-axis current is zero, the PMSM reaches maximum efficiency. Therefore, (20) can be reduced to v q = r s i q + v re l m (21) Finally, the advance angle comm u adv is obtained by using a proportional-integral control expressed as u adv (k) = K P e 2 (k) + K I k k=1 e 2 (k)+u adv (0) (18) The advantage of the impedance-phase method is that only the impedance-phase angle u z is used, which is not related to the q-axis current. In addition, the stator resistance voltage drop is not required for the impedance-phase method. As a result, the impedance-phase angle method yields better performance than the back-emf method. Fig. 4c shows the block diagram of the proposed impedance-phase control method, which forces the u z to track its comm, u z. Fig. 7 Comparison of the two estimation methods a Estimation error of integral method b Estimation error of polynomial method Fig. 8 Measured a-phase current related Hall-effect position sensor at 1000 r/min a Back-EMF method b Impedance-phase method c Current amplitudes at different speeds
( ) When the stator resistance varies to kr s, where k is the ratio between the varied resistance to the nominal resistance. The d q-axis voltage equations become ( )( ) ( ) v d = kr s id + Di d vre L s i q + Di q (22) v q = (kr s )(i q + Di q ) + v re L s (i d + Di d ) + v re l m (23) ( ) When the permanent magnetic flux varies to kl m, the d q-axis voltage equations become ( ) v d = r s (i d + Di d ) v re L s i q + Di q (24) v q = r s i q + v re L s (i d + Di d ) + v re (kl m ) (25) ( ) When the stator inductance varies to kl s, the d q-axis voltage equations become v d = r s (i d + Di d ) v re (kl s )(i q + Di q ) (26) ( ) ( )( ) v q = r s i q + Di q + v re kl s id + Di d + vre l m (27) 4 Experimental results Fig. 6a shows the configuration of the proposed fan drive system, which includes a DC power supply, an inverter, a PMSM, a fan, a DSP-type TMS320F28335, a gate driver circuit, some A/D converters, some low-resolution Hall position sensors. The inverter is implemented with six IGBT power devices with 10 khz PWM switching frequency. Fig. 6b shows a photograph of the hardware circuit, which is very compact can be attached to the PMSM. Fig. 6c shows a photograph of the fan PMSM, which is a fourpole, 375 W PMSM with r s = 7 V, L s = 56 mh, l m = 0.181 rad/s. Figs. 7a b show the estimation errors of the integral method polynomial approximation method when the motor is operated at 1000 r/min. As you can observe, the polynomial approximation method provides smaller estimated error than the integral method. Figs. 8a b show the measured a-phase current its related Fig. 9 Comparison of the two methods at different speeds a Input powers bq-axis current to d-axis current c Efficiency Fig. 10 Measured transient responses a Comm estimated speed b Angles
Hall-effect position sensor at 1000 r/min. The a-phase current amplitude is nearly 1.8 A when using the back-emf estimation method; however, it is reduced to nearly 1.1 A when using the impedance-phase estimation method. The phase angle between the Hall-effect sensor the a-phase current is nearly 31.2 when using the back-emf estimation method; however, it decreases to 7.2 when using the impedance-phase estimation method. According to Figs. 8a b, the impedance-phase estimation method is better at decreasing the current amplitude phase error. Fig. 8c shows the comparison of the current amplitudes at different speeds from 900 1500 r/min. Fig. 9a shows the measured input power of the fan at different speeds. The fan speed the input power increase proportionally. Fig. 9b shows the measured relationship between the q-axis current the d-axis current at different speeds. As you can see, the impedance-phase method has a lower d-axis current a higher q-axis current than the back-emf method. Fig. 9c shows the measured efficiency to speed curves. The efficiency is obviously improved by using the impedance-phase estimation method at 900 r/min. However, the difference becomes smaller as the fan speed increases. Figs. 10a b show the measured transient responses of the fan system from ststill to 1500 r/min. Fig. 10a shows the measured fan speed comm fan speed. Fig. 10b shows the responses of the estimated angle its comm, the advance angle. Fig. 11a shows the measured starting a-phase current the steady-state a-phase current. Fig. 11b shows the measured steady-state line voltage. Fig. 11c shows the measured steady-state a-phase current. Figs. 12a b show the d q-axis current deviations when the motor parameters are varied. The drive system is very sensitive when flux l m varies; however, it is not sensitive when resistance or inductance vary. Tables 1 2 show the results of sensitivity analysis. Fig. 12 Di d Di q to the parameter variations at 1500 r/min a Di d to variation b Di q to variation Table 1 Sensitivity of back-emf estimation at 1500 r/min Operating conditions Amplitude of current vector i s,a Results Estimating error of the advance angle ũ adv = u adv _ u adv, deg Phase anglef i, deg Fig. 11 Measured waveforms a Starting a-phase current b Steady-state line voltage c Steady-state a-phase current nominal parameters 2.8 0 0 0.8l m 3.52 3.64 5.38 0.8L s 2.8 0 0 1.2r s 2.81 2.26 4.49 0.8l m,0.8l s,1.2r s 3.51 1.03 1.8
Table 2 Sensitivity of impedance-phase estimation at 1500 r/min Operating conditions 5 Conclusions In this paper, a closed-loop fan drive system has been implemented without a feedback encoder or current sensors. Only three-phase, low-resolution Hall-effect position sensors are used. A rotor estimation algorithm is developed to obtain a high-resolution rotor angle then generate three-phase sinusoidal PWM voltage comms. Two methods, including the back-emf estimation method the impedance-phase estimation method, are proposed to determine the three-phase input voltages. Experimental results show the impedance-phase estimation method is more efficient than the back-emf estimation method. The proposed fan drive system is low cost, small, robust. 6 Acknowledgment This paper was support by Ministry of Science Technology, Taiwan under grant MOST-105-2221-E-011-095-MY2 MOST-105-2221-E-011-107-MY2. 7 References Amplitude of current vector i s,a Results Estimating error of the advance angle ũ adv = u adv _ u adv, deg Phase angle f i, deg nominal parameters 2.8 0 0 0.8l m 3.54 5.47 8.93 0.8L s 2.81 2.02 4.69 1.2r s 2.81 1.89 3.78 0.8l m,0.8l s,1.2r s 3.50 0.84 1.47 [1] Jung S.Y., Kim Y.J., Jae J., ET AL.: Commutation control for the low-commutation torque ripple in the position sensorless drive of the low-voltage brushless DC motor, IEEE Trans. Power Electron., 2014, 29, (11), pp. 5983 5994 [2] Murakami S., Shiota T., Ohto M., ET AL.: Encoderless servo drive with adequately designed IPMSM for pulse-voltage-injection-based position detection, IEEE Trans. Ind. Appl., 2012, 48, (6), pp. 1922 1930 [3] Consoli A., Scelba G., Scarcella G., ET AL.: An effective energysaving scalar control for industrial IPMSM drives, IEEE Trans. Ind. Electron., 2013, 60, (9), pp. 3658 3669 [4] Uddin M.N., Rebeiro R.S.: Online efficiency optimization of a fuzzy-logic-controller-based IPMSM drive, IEEE Trans. Ind. Appl., 2011, 47, (2), pp. 1043 1050 [5] Uddin M.N., Khastoo J.: Fuzzy logic-based efficiency optimization high dynamic performance of IPMSM drive system in both transient steady-state conditions, IEEE Trans. Ind. Appl., 2014, 50, (6), pp. 4251 4259 [6] Cavallaro C., Tommaso A.O.D., Miceli R., ET AL.: Efficiency enhancement of permanent-magnet synchronous motor drives by online loss minimization approaches, IEEE Trans. Ind. Electron., 2005, 52, (4), pp. 1153 1160 [7] Sreejeth M., Singh M., Kumar P.: Particle swarm optimization in efficiency improvement of vector controlled surface mounted permanent magnet synchronous motor drive, IET Power Electron., 2015, 8, (5), pp. 760 769 [8] Deng W., Zhao Y., Wu J.: Energy efficiency improvement via bus voltage control of inverter for electric vehicles, IEEE Trans. Veh. Technol., 2017, 66, (2), pp. 1063 1073 [9] Mademlis C., Margaris N.: Loss minimization in vector-controlled interior permanent-magnet synchronous motor drives, IEEE Trans. Ind. Electron., 2002, 49, (6), pp. 1344 1347 [10] Dunkl S., Muetze A., Schoener G.: Design constraints of small single-phase permanent magnet brushlesws DC drives for fan applications, IEEE Trans. Ind. Appl., 2015, 51, (4), pp. 3178 3186 [11] Noda S., Mizuno S., Koyama T., ET AL.: Development of a totally enclosed fan cooled traction motor. 2010 IEEE Energy Conversion Congress Exposition, Atlanta GA, USA, 2010, pp. 272 277 [12] Lelkes A., Bufe M.: BLDC motor for fan application with automatically optimized commutation angle. 2004 IEEE Power Electronics Specialists Conf., Aachen, Germany, 2004, pp. 2277 2281 [13] Saxena A.: Performance cost comparison of PM BLDC motors for ceiling fan. Int. Conf. Power Electronics, Drives Energy Systems, 2014, pp. 1 5 [14] Sun W., Shen J.X., Jin M.J., ET AL.: A robust magnetic polarity self-sensing method for start up of PM synchronous machine in fanlike system, IEEE Trans. Ind. Appl., 2017, 53, (3), pp. 2169 2177 [15] Lee K.W., Park S., Jeong S.: A seamless transition control of sensorless PMSM compressor drives for improving efficiency based on a dual-mode operation, IEEE Trans. Power Electron., 2015, 30, (3), pp. 1446 1456