BLDC Motor Research Papers

A New Approach to Sensorless Control Method for Brushless DC Motors 477 A New Approach to Sensorless Control Method for Brushless DC Motors

by Thierry Pasquet

This paper proposes a new position sensorless drive for brushless DC (BLDC) motors. Typical sensorless control methods such as the scheme with the back-EMF detection method show high performance only at a high speed range because the... more

Fig. 2. Waveforms of a back-EMF, a phase current and a torque of BLDC motor.

Fig. 1. Block diagram of a BLDC motor drive. Assuming that the stator resistances of all the windings are equal, and also self and mutual inductances are constant, the voltage equation of the three phases can be expressed as (1) [18]. In this equation, magnets, stainless steel retaining sleeves with high resistivity, and rotor-induced currents are neglected and no damper windings are modelled.

Fig.3. Block diagram of the proposed back-EMF observer.

Fig. 4. Proposed commutation functions. On the other hand, as shown in Fig. 6, the bigger Figs. 5 and 6 represent the proposed commutation function and the existing commutation function, respectively. In Fig. 5, the lower noise () than threshold (th) 1 dose not affect position detection of the rotor, however, the bigger noise (2) than th 1 can be regarded as commutation signal and generate error of a rotor position. Also, in case of commutation function such as @) due to variation of back-EMF, the th voltage level should be increased to achieve the exact commutation signal, as in case the noise @) exists.

Fig. 6. Commutation function using the proposed method. signal than th 1 or th 3, such as @) and @), are not regarded as commutation signal in the proposed method since it is generated in the point before negative infinity, namely, the signal after passing negative infinity (th 3) and satisfying the bigger magnitude than th 1 is regarded as the commutation signal. Finally, the existed method is sensitive to the noise, and the calculation of commutation point can be delayed because it should select the bigger voltage level th than th 2 (@). But the proposed method can generate the exact position information in the lower th voltage level as ©).

Fig. 5. Commutation function using the existing method.

Fig. 8. Overall structure of the proposed sensorless drive system.

Fig. 7. Relation between the estimated line-to-line back-EMFs and the estimated back-EMFs.

and 1.5 Nm to the motor at 2.3 s and 0.9 s while it runs at 50 rpm (Fig. 9) and 1650 rpm (Fig. 10), respectively. As shown in Figs. 9 and 10, the speed drops slightly while the phase current increases to

This paper evaluates the robustness of the sensorless algorithm under variations of speed and load. To test the dynamic behaviour of the drive under load disturbance, we assert a dynamic load of 0.5 Nm

Fig. 12. Laboratory prototype for the experimental verifications. The experimental results are depicted in Figs. 14-19. Figs. 14 and 16 show the estimated tracking ability at

Fig. 13. Experimental result of commutation func- tion. (10 ms/div.)

Fig. 18. Worst-Case commutation angle error. (Ex- perimental result)

Table 1. Ratings and parameters of BLDC motor.

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Modeling of BLDC Motor with Ideal Back-EMF for Automotive Applications

by ALIREZA TASHAKORI ABKENAR and

Automotive Industry is targeting sustainable transportation in near future. Therefore hybrid and electric vehicles are going to be popular due to their sustainability, energy saving and zero emission. Electric motors play significant role... more

Fig. | Two wheel drive train system of a commercial IFECV BLDC has more complex control algorithm compare to other motor types due to electronically commutation. Therefore accurate model of motor is required to have complete and precise control scheme of BLDC. To design of BLDC motor drive system, it is necessary to have motor model gives precise value of torque which is related to current and back-EMF [2]. Different simulation models have been presented to analyze performance of BLDC motor [3-6]. Lots of various modeling techniques according to different applications of BLDC motor have been used. Although all the previous works made a great contribution to modeling BLDC motor, but there is no simple model appropriate for in-wheel motor application. Hence in this paper model of 3 phases, 4 poles, Y connected, trapezoidal back-EMF type of BLDC motor for automotive industry application is modeled and simulated in MATLAB / SIMULINK.

MATLAB code is written to produce Ideal Back-EMF of BLDC as function of rotor position. Simulation result of Ideal back-EMF reference waveforms of all phases versus electrical angle are shown in Fig. 3. Fig. 3 Ideal back-EMF reference waveforms of all three phases

Fig. 2 Schematic system of BLDC motor drive Hence control techniques of BLDC are not objective of paper; therefore proportional plus integral (PI) controller is used in loop control algorithm to control speed. Schematic system of BLDC motor drive is shown in Fig. 2. As shown in Fig. 2 simulation model is consisting of three parts. Each part is simulated separately and integrated in overall simulation model. For decoding Hall Effect signals in PWM generator, MATLAB code is written. techniques are using for electronic commutation [8]. In this model, Hall Effect signals are produced according to rotor position for commutation. Also a 3-phase inverter using MOSFETs is used as voltage source. Different control techniques can be applied to the model.

Hall Effect signals of motor are produced according to electrical degree. Table I shows Hall Effect signal values according to electrical degree of rotor.

Fig. 5 Overall model of BLDC motor drive With implementing of a 3-phase full bridge inverter and a PI speed regulator overall model of motor is modeled. Overall model of BLDC motor drive is shown in Fig. 5.

Implementing of final state space equations (14) and (15) will make model more complex. Although neutral point of motor is not accessible but virtually is possible to estimate it with zero crossing point of back-EMF. Also for linear and zero initial condition systems, State space to Laplace transform and vice versa can be written. Therefore final state space equation is divided to two simple and separate electrical and mechanical Laplace equations applied by phase to neutral voltages. Then state space model of BLDC motor is:

Output and Input power characteristics of motor under no load condition is shown in Fig. 10 and Fig. 11. With respect to steady state values of power, efficiency of system is 94.6%. System shows high efficiency operation of BLDC.

Fig. 10 Output power characteristics under no load condition

Fig. 11 Input power characteristics under no load condition Simulation also has been done under 10 N.m load torque. BLDC speed characteristic is shown in Fig. 12 under load

Fig. 7 Torque characteristics under no load condition Fig. 8 Back-EMF of phase A

Simulation results of BLDC motor under no load and load conditions are shown. As it can be seen in Fig. 6, dynamic response of BLDC due to its permanent magnet (low inertia) rotor is high. Pulsating torque of BLDC is shown in Fig. 7. Figure 8 shows back-EMF produced in phase A of motor. Hall Effect signals of all three phase are shown in Fig. 9 according to table I. Table 1 shows BLDC motor specification to investigate performance of advanced model.

condition. It can be seen that under load condition time for speed to reach its final value is increased. Current characteristics of phase A of motor under load condition is shown in Fig. 13. Maximum value of current is 40 amps.

Sustainable transportation is a need in near future due to reduce energy consumption and greenhouse gases emission in the world for solving global warming issue. Hybrid and electrical vehicles are the good solution in this case. Increase of safety, efficiency and controllability of a vehicle has been always one of the most important challenges in automotive industry. Design of intelligent fully electronically controlled vehicle (IFCEV) can be the solution for many of challenges in this field.

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Realization of Fuzzy Logic Controlled Brushless DC Motor Drives using Matlab/Simulink

by Ayodeji Mamudu

2010, Mathematical and Computational …

In this paper, an efficient simulation model for fuzzy logic controlled brushless direct current motor drives using Matlab/Simulink is presented. The brushless direct current (BLDC) motor is efficiently controlled by Fuzzy logic... more

Figure 4. Simulink diagram for generating back EMF from rotor positions.

Figure 6. The generation of inverter line-to-line voltages 2.3 Reference Current Generator

Figure 1. Fuzzy logic controlled BLDC motor drive system. The proposed control system, which contains two loops, is shown in Figure 1. he first loop is the current control loop that accomplishes torque control of BLDC 1otor and the second loop is the speed control loop that adjusts the speed of BLDC otor.

2.4 Current Control Block In PWM current control block, the reference phase currents (i, ref ,i,ref ,i,ref ) are Ip ES If EE II EI IIE IIE IS IID IEE EILEEN Sf ESSE EES ESE IES NI EA OM FE OF OPO ™ compared with the motor’s actual phase currents (i,,i,,i,). The current errors are calculated as shown in Equation (13). These errors are applied to inverter hysteresis band (- th,) and the switching signals of three-phase PWM inverter system are generated according to the switching states. As shown in Figure 8.b, the hysteresis current control is performed in the function block fa(s) by using the measured phase current i,, the reference current Imax, and the rotor position 0, where the function block fa(s) is a term which functio [16]. consists of Equation (13) and (14). From the hysteresis block, the switching n S_a, S_b, and S ¢c are defined to model the operation of the PWM inverter

fuzzy membership function is defined for each input and output values by seven clusters. Figure 10 illustrates the membership function used to fuzzify two input values (e,,e,) and defuziffy output (uw) of the fuzzy controller. For seven clusters in the membership functions, seven linguistic variables are defined as: Negative Big (NB), Negative Medium (NM), Negative Small (NS), Zero (Z), Positive Small (PS), Positive Medium (PM), and Positive Big (PB). Figure 10. Membership functions of fuzzy controller

Figure 11. Simulink diagram of fuzzy logic and PID controllers Realization of Fuzzy Logic Controlled Brushless DC Motor Drives

BLDC motor specifications used in simulation are shown in Table 3. The waveforms of generated back EMF according to the rotor position at 4050 rpm are EMF waveforms are demonstrated in Figure 12.a. Three-phase symmetric back | produced from the rotor position. The rotor position changes from 0 to 27 per electric cycle 0.0148s at 4050 rpm and the amplitude of the back EMF is 17.78 V. Figure 12.b shows the phase-current waveforms. The actual phase currents obtained by the hysteresis control algorithm are synchronized with their counterpart back EMF waveforms.

in Figure 13. In order to hold the currents to be switched between the hysteresis upper and lower bands, the switching function signals are only produced during the 120° conduction periods. The positive and negative values represent the upper and lower switch or diode under the conducting state. The line-to-line voltage waveforms are obtained by using the switching function S_a. The produced line-to-line voltage (vas) according to the conduction modes is demonstrated in Figure 14. Since Tl and T6 are simultaneously active as seen in Figure 3, the phase A and the phase B currents are positive and negative, respectively Figure 15 shows the dynamic responses of the speed, torque and Imax, respectively. The reference value of maximum current (Imax) is computed from the generated constant torque reference, consequently it is used in the hysteresis control block. Furthermore, the control algorithms, FLC and PID can be compared by using developed model. As shown in Figure 16 (a) and (b), if the PID controller is used, the real speed and torque reach the desired value in 6.5ms. On the other hand, if FLC is used, the real speed and torque reach the desired value in 5ms. In conclusion it can be said that unlike the classical controller, FLC is more effective in BLDC motor drives.

Figure 15 Electromagnetic torque, speed of BLDC motor, and maximum current (Imax)

Table 1 Reference currents of BLDC motor

Table 2 Rule base of fuzzy controller MATLAB/Fuzzy Logic Toolbox is used to simulate FLC which can be integrated into simulations with Simulink. The FLC designed through the FIS editor is transferred to Matlab-Workspace by the command “E Simulink environment provides a direct access to t Workspace in BLDC motor drive simulation. Figure 11 Export to Workspace”. Then, he FLC through the Matlab- shows the simulink diagram of Fuzzy logic and PID controllers. These control algorithms can be compared by using switch block. If the mid-point of switch block is ‘-1’, FLC is selected, and in case the mid-point of switch block is ‘1’, the PID is selected.

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Modelling and Simulation Analysis of the Brushless DC Motor by using MATLAB

by G. PRASAD Goud

The BLDC motor is also referred to as an electronically commutated motor. There are no brushes on the rotor and the commutation is performed electronically at certain rotor positions. The stator phase windings are inserted in the slots (a distributed winding), or can be wound as one coil on the magnetic pole. The magnetization of the permanent magnets and their displacement on the rotor are chosen in such a way that the back-EMF shape is trapizoidal. This allows the three-phase voltage system, with a rectangular shape, to be used to create a rotational field with low torque ripples. In this respect, the BLDC motor is equivalent to an inverted DC commutator motor in that the magnets rotates while the conductors remain stationary.

Fig.2. Simulink Model of BLDC Motor The simulink model of the BLDC motor is shown in fig.2. Unlike a brushed DC motor, the commutation of a BLDC motor is controlled electronically. To rotate the BLDC motor,the stator windings should be energized in a sequence. It is important to know the rotor position in order to understand which winding will be energized following the energizing sequence. Rotor position is sensed using hall effect sensors embedded into the stator. Most BLDC motors have three hall sensors embedded into the stator on the non- driving end of the motor.

Fig.3. Detailed SIMULINK model of BLDC motor However, one electrical cycle may not correspond to a complete mechanical revolution of the rotor. The number of electrical cycles to be repeated to complete a mechanical rotation is determined by the rotor pole pairs. For each rotor pole pairs, one electrical degree is completed. The number of electrical cycles/rotations equals the rotor pole pairs.

Fig.4(a). Sinusoidal model of the back-EM The trapezoidal back-EMF is given as:

For sinusoidal simulation model FFT analysis is given as: For speed:

Fig.4(b). Trapezoidal model of the back-EMF Simulations results for sinusoidal input: For speed :

Modelling and Simulation Analysis of the Brushless DC Motor by using MATLAE For trapezoidal simulation model FFT analysis is given as: For speed:

International J ournal of Innovative Technology and Exploring Engineering (IJ ITEE) ISSN: 2278-3075, Volume-1, Issue-5, October 2012

Fig. 8(e). FFT Analysis For Torque ‘able 1: Comparison of sinusoidal and trapezoidal output

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ANN Assisted Multi Sensor Information Fusion for BLDC Motor Fault Diagnosis

by Tanvir Alam Shifat

2021, IEEE Access

Multiple sensor data fusion is necessary for effective condition monitoring as the electric machines operate in a wide range of diverse operations. This study investigates sensor acquired vibration and current signals to establish a... more

FIGURE 1. Proposed fault diagnosis architecture. method and the residual of the first computation is shown in The concept of the ANN was established by mimicking human brain operation where the neurons share information throughout the neural connection to make a decision. In ANN, there are several layers that are interconnected to map the input and output. Basically, there are three layers in an ANN architecture [30]. The first one is the input layer where the ANN takes input data. The second one is hidden layer. In hid- den layer, input data is taken and processed by some weight computation. The third layer is the output layer, where the weighted data is mapped with target neurons for decision making. Internal weights can be optimized to better capture the implicit mapping of input-output data.

FIGURE 2. Sifting process for IMF computation in EMD.

FIGURE 4. Test rig setup for motor test. distribution of the permanent magnet, which causes imbal- ance in electromagnetic induction. Imbalanced induction pro- duces several attributes in motor operation including abrupt vibration change, distorted phase currents, local heating, torque reduction, noise emission, etc. Primarily, mechanical faults are responsible for early vibration change and electrical faults produce inconsistent line currents [32].

FIGURE 5. CAD illustration of motor faults. (a) Stator winding short-circuit (WSC). (b) Rotor crack fault (CRF)

FIGURE 7. CEEMD decomposition of vibration signals. (a) Healthy signal and IMFs, (b) FFT of the healthy signal and IMFs, (c) Faulty signal and IMFs, (d) FFT of the faulty signal and IMFs.

FIGURE 6. Motor current signature analysis.

FIGURE 8. Feature selection based on monotonicity.

WSC fault and | as CRF fault. The largest misclassification is seen for WSC fault classification where 1731 were classified accurately, and 11 were predicted as healthy samples and 61 were predicted as CRF fault samples. On the other hand, in case of CRF fault, 1738 samples were predicted accurately, and 57 were predicted as WSC fault samples and 3 as healthy samples. It is understandable that healthy and CRF features are well oriented to be classified separately. However, WSC fault features have some overlapping with healthy and CRF fault features, making some inaccurate predictions in test data. If we look at the confusion matrix of the train data, a similar thing can be observed for WSC fault. This is due to the cyclostationarity behavior of the CRF fault signal. Due to the crack in rotor, each rotation leaves an impulsive on vibration of BLDC motor. These vibration impulses result in larger distortions and abruptly changing frequency responses which are captured as different HIs during feature extraction. To better understand the performance of the ANN model, we have computed several performance metrics based on the predicted values and actual values. Mathematical expressions of the metrics are shown in (11)- (14):

FIGURE 10. Confusion matrix for ANN predictions. (a) Train data, (b) Test data.

TABLE 1. Parameters for motor test. A major concern in designing an ANN framework is hyperparameter optimization. Selection of proper activation function, depth of the network, loss function, dropout rate, etc. largely influence the performance of the model [31]. These hyperparameters are chosen during the training stage and based on the performance of prediction, these can be optimized further. In a classification problem, several perfor- mance measures can be used to evaluate model accuracy. For example, precision, recall, Fl-score, geometric mean, etc.

TABLE 3. Features computed from vibration and current signals. for the postprocessing, vibration feature set is selected for F1 feature. As we chose a large number of neurons, model can get overfitted which is a commonly occurring phenomenon in neural nets. To avoid overfitting, we have used a regular- ization method named “Dropout”, which randomly ignores some output of the previous layer neurons by temporarily removing it from the network. The Dropout rate set for train- ing ANN was 0.15. Several values for different hyperparame- ters were tested for ANN model. Based on the computational time and accuracy, the best ones are chosen for training. A list of model parameters is presented in Table 4.

TABLE 5. Performance metrics of ANN model. point. To avoid overfitting due to a large number of neurons in the hidden layers, we have used dropout to randomly disable some neurons during the training stages. This allows the model to randomly deactivate some neurons during the forward propagation computation of model training stage. In the output layer, a SoftMax activation function is used that computes the probability of the desired class. After a satisfactory training step, the trained model is used to predict the test data and classify. Among the 1800 healthy label fea- tures, 1786 were predicted correctly and 12 were predicted as

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Adaptive Speed Control of Brushless DC (BLDC) Motor Based on Interval Type-2 Fuzzy Logic

by Mohsen Akbari

2016, 24th Iranian Conference on Electrical Engineering (ICEE)

To precisely control the speed of BLDC motors at high speed and with very good performance, an accurate motor model is required. As a result, the controller design can play an important role in the effectiveness of the system. The classic... more

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Position Sensors Fault Tolerant Control System in BLDC Motors

by ALIREZA TASHAKORI ABKENAR and

Developing of fault tolerant control systems (FTCS’s) for permanent magnet Brushless DC (BLDC) motor drives to diagnose and handle various faults have been of the research concentrations in the last decade. In this paper a novel fault... more

Fig. 1. Schematic diagram of a three phase BLDC motor drive The presented position sensor FTCS in this paper is pro- posed for three phase star connected permanent magnet BLDC motors with the inbuilt Hall Effect sensors. Four-phase, five- phase and six-phase BLDC motors are not in the scope of this paper, however they can be considered as potential research studies in future. Schematic diagram of a three phase, 4 poles, star connected BLDC motor drive is shown in Fig. 1. Hall Effect sensors detect position of the permanent magnet rotor based on Hall Effect theory. They are embedded at non-rotating end of the BLDC motor with 120 electrical

Fig. 2. Ideal waveforms of a three phase BLDC motor

Fig. 3. Experimental test rig of the BLDC motor motor under various position sensor faults through a validated simulation model. Therefore the BLDC motor drive using a closed loop digital Pulse Width Modulation (PWM) speed controller is modelled in Simulink. A low voltage three phase permanent magnet BLDC motor is used as a practical test rig to validate simulation model of the motor. Low voltage development board of microchip using PIC18F4231 micro- controller is programmed to control the BLDC motor. The control board has an in-built over current protection circuit and the inverter section consists of three half-bridge gate drivers using MOSFETs. High frequency PWM control signal is applied to all the switches of the inverter. Experimental test rig of the BLDC motor is shown in Fig. 3.

Fig. 4. Line voltage and corresponding Hall Effect signal of phase A of the BLDC motor

Fig. 5. Speed and torque characteristics of the BLDC motor for Ha = 0

and change pattern of the applied line voltage of the BLDC motor.

Fig. 7. Line voltages of the BLDC motor for Hg = 1 Although speed and torque responses of the BLDC motor for H, = 1 are similar to the ones for H, = 0 fault condition; however affected switching signals of the VSI is different. Switching signals PW My and PW Ms are constant zero for H, = 1 fault condition. Therefore applied voltages to the BLDC motor are totally different for H, = 1 fault condition compare to the H, = O fault. Line voltages of the BLDC motor for H, = 1 fault condition are shown in Fig. 7.

Single-sided amplitude spectrum of phase A line voltage of the BLDC motor model for no fault and Hall Effect sensor faults of phase A (H, = 0 and H, = 1) are shown in Fig. 8. As can be seen high amplitude harmonics are added to the line voltage of phase A under faulty conditions compare to the healthy operation condition. Therefore energy densities of the voltage signals of the BLDC motor under position sensor faults condition are not same as the healthy operating condition. Calculated SED errors of the BLDC motor line voltages for Hall Effect sensor faults of H, = 0 and H, = 1 faults are given in Table IV and Table V, respectively. Hall Effect sensor fault is detected if HFF is not zero. However identification of faulty sensor is not possible through Hall Effect sensor Fault Flag. DFT analysis of line voltages of the BLDC motor is used for fault identification. In practice, ine voltages of the BLDC motor is measured for specific interval of time and saved in memory of micro-controller. Then DFT of the saved measured line voltages are calculated by (2) and Spectral Energy Density (SED) of the computed frequency spectrum are determined by (3). SED difference of the measured line voltages between successive time intervals for constant speed and torque load are signature for identifying of faulty sensor [1].

Fig. 8. Amplitude spectrum of phase A line voltage of the BLDC motor

The proposed fault diagnosis algorithm of the BLDC motor is investigated through experimental test rig. Low voltage (LV) development board of microchip using PIC18F2431 micro- controller has been modified as shown in Fig. 9 to test the Hall Effect sensor faults of the BLDC motor. Both short circuit and open circuit faults of Hall Effect sensors have been implemented to the experimental BLDC motor and the results are analysed. Fig. 9. Modified LV development board of microchip

Open circuit fault of Hall sensor of phase A (H, = 1) is applied to the experimental test motor when the motor was running at 2000 RPM under 0.1 N.m torque load. Line voltages of the experimental BLDC motor under H, = 1 fault condition are shown in Fig. 11. As can be seen pattern of the line voltages are totally different compared to the H, = 0 fault condition. Fig. 11. Line voltages of the experimental BLDC motor during Hg = 1 fault condition

Fig. 10. Line voltages of the experimental BLDC motor during Hg = 0 fault condition

vice versa. Status of the LED lights for open circuit and short circuit faults of the Hall Effect sensor of phase A are shown in Fig. 12. As can be seen switching signals PWM], and PW Mz for open circuit fault condition and switching signals PW Mo and PW Ms; for short circuit fault condition are zero. Therefore effects of Hall Effect sensor faults on switching signals of the VSI that are presented in Table II are validated through experiment. Fig. 12. Status of switching LED lights on the control board under position sensor faults of phase A: (a) Open circuit fault (b) Short circuit fault

Fig. 13. Amplitude spectrum of phase A line voltage of the BLDC motor Single-sided amplitude spectrum of phase A line voltages of the experimental BLDC motor under no fault, Hall Effect sensor open circuit fault (HW, = 0) and Hall Effect sensor short circuit fault (7, = 1) conditions are plotted in Fig. 13. As can be seen same as the case in simulation results, high amplitude harmonics are added to the line voltage of phase A of the experimental BLDC motor under Hall Effect sensor fault conditions. Therefore spectral energy densities of the line voltages are not same for different position sensor faults in the BLDC motor drive.

Fig. 14. Speed response of the fault tolerant controlled BLDC motor drive under 0.1 N.m torque load. Speed characteristics of the BLDC motor with the implemented fault tolerant control system is shown in Fig. 14. Fault is detected, id entified through calculating HFF and HIF of each phase. Fault diagnosis time in simulation model is about 0.113 second which is quite fast and acceptable for motor drives. Faulty sensor signal are generated by the controller that maintain motor in the post-fau s the proper performance of the BLDC t condition. Simulation results show effectiveness of the pro sensors breakdown in t posed remedial strategy for Hall Effect he BLDC motors.

SPECIFICATIONS OF THE EXPERIMENTAL BLDC MOTOR

SENSORS FAULT EFFECT ON THE VSI SWITCHING SIGNALS TABLE II motor is running under healthy condition for 0.1 N.m torque load at 2000 RPM speed. Speed and torque characteristics of the BLDC motor for H, = 1 fault condition are similar tc HT, = 0 fault condition. Torque ripples amplitude is increased and the BLDC motor is not stable after fault occurrence.

PROPOSED KNOWLEDGE BASED TABLE FOR POSITION SENSOR FAULTS DIAGNOSIS TABLE VI

SED VALUES FOR EXPERIMENTAL H, = 1 FAULT CONDITION The low voltage experimental BLDC motor is also tested under open circuit and short circuit faults of the other Hall Effect sensors, corresponding sensors of phase B and C, and their experimental results are analysed. Calculated experimen- tal SED errors of the line voltages prove the fault diagnosis algorithm and validate the knowledge based fault identification table, Table VI, developed through the simulation model.

SED VALUES FOR EXPERIMENTAL Ha = 0 FAULT CONDITION

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Torque ripple minimization of PMBLDC motor using simple boost inverter

by International Journal of Power Electronics and Drive Systems

2019, International Journal of Power Electronics and Drive System (IJPEDS)

This paper proposes the implementation of simple boost circuit incorporated in inverter fed Brushless DC (BLDC) motor drive to boost the performance of torque. BLDC motor becoming subtle because of its performance. But the motor... more

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Speed Control for Four Quadrant Operation of Three Phase Bldc Motor Using Digital Controller

by IOSR Journals

This paper proposes a method that estimates rotor position by using an unknown input observer over a full speed range. In this method, a trapezoidal back emf is modeled as an unknown input and the proposed unknown input observer... more

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LQG/LTR Position Control of an BLDC Motor with Experimental Validation

by Coşku Kasnakoğlu

In this paper, position control of a BLDC motor is studied. This position control is LQG/LTR control algorithm. In addition, a system identification approach is used to obtain the nominal plant of BLDC Motor. As a consequence, proposed... more

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A Geometry Optimization of Fundamental Characteristics of a Brushless DC Motor Using FEM and Evolutionary Algorithms

by Ata Yadghar

This paper describes a design procedure that optimizes the three principle functions of a brushless DC (BLDC) motor. The weight, average torque and cogging torque of a 3phase, 6 slot BLDC motor with 4 surface mounted permanent magnet has... more

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Analysis of Sliding Mode Observer based SVPWM Inverter Fed BLDC Motor

by IJSTE - International Journal of Science Technology and Engineering

International Journal of Science Technology & Engineering

This paper proposes a sliding mode control scheme for effective speed tracking by obtaining ripple free torque through the inner loop current control for a BLDC motor. Sliding mode observer is a parameter for estimating the phase to phase... more

Three phase AC voltage is given to the controlled rectifier which converts it into DC voltage. This DC voltage is provided for VSI which in turn converts it into again Ac voltage for providing square wave to BLDC motor.

After providing square wave, BLDC motor rotates with parameters such as speed and torque. for controlling BLDC motor speed its parameters are to be estimated which can be done by converting three phase into two phase using park transformation. The estimated speed and torque values are provided to controller block along with reference speed and torque values denoted as “Nref” and “Tref” respectively for calculating error signal. Followed by converting two phase into three phase using inverse park transformation which is provided as input to space vector pulse width modulation(SV PWM) generator. This SVPWM generator converts input error signals into gate signals for VSI. These gate signals controls the speed of BLDC motor.

V. HARDWARE IMPLEMENTATION AND ITS RESULTS

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Performance enhancement of BLDC motor using PID controller

by International Journal of Power Electronics and Drive Systems

2021, International Journal of Power Electronics and Drive Systems (IJPEDS)

Mainly the DC motors are employed in most of the application. The main objective is to Regulate the DC motor system. A motor which displays the appearances of a DC motor but there is no commutator and brushes is called as brushless DC... more

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DESIGN OF ELECTRIC BICYCLE WITH FLYWHEEL AND BLDC MOTOR USING SOLAR ENERGY SYSTEM

by IAEME Publication

2021, IAEME PUBLICATION

In the modern world, population is increasing day by day and energy demand is linearly increasing it'll be very dif icult or simply impossible to full fill the energy requirements without renewable energy. Every vehicles has own emission... more

The solar bicycle is operated by solar power. The lead acid battery is charged with voltage generated due to solar energy with the assistance of a solar cell. Solar cells convert the solar power directly into electricity using photovoltaic effect. The photovoltaic effect involves conversion of electromagnetic wave into electrical energy. The photoelectric and photovoltaic effects are associated with sunlight, but are different therein electrons are ejected from a material’s surface upon exposure to electromagnetic wave of sufficient energy in photoelectric, and thus the electrons that are generated are transferred to different bands of valence to conduction within the fabric. Thus this results into build-up of voltage between two electrodes in photovoltaic. Solar cells are connected electrically and manufactured as a module with a sheet of glass on top to allow light to pass and protect the semiconductor from the weather. To get the voltage of 14V,50W solar panel is used. The dimension of 50W solar panel is 2.2feet. x 1.4feet. (length x width) and weight is around 3kg.

3.3. Flywheel When riding a bicycle, an excellent amount of Kinetic Energy is lost while breaking. To use this energy, we are employing a flywheel to store the energy which is generally lost during breaking and reuse it to assist propel the rider when starting.

3.4. Battery Lead acid battery has lower energy density than the lithium-ion batteries but since is extremely safe to use lead acid battery with proper precautions taken. It has many advantages like low cost, frequently available, and is additionally explosion free thus is that the most often used battery in solar hybrid bicycles. Here we need 24V,14Amps Battery for storage, so we use two 12V,7Amps Lead Acid batteries connected in series.

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Dynamic Optimization Self-adaptive AI Controller for a Four-wheel Independent Drive Electric Rover

by Hasitha Jayetileke

2021, Advances in Technology (ISSN 2773-7098)

In this paper, a dynamic optimization self-adaptive controller for a four-wheel independent drive electric rover has been investigated to enhance the dynamic stability. The proposed self-adaptive AI controller is based on dynamic Fuzzy... more

Figure 1. Schematic of the rover model Rover system model

Figure 2. Acting forces on each tire of the rover in the same direction. The F(¢,iction) Could be expressed as Equation 5:

Figure 5. Membership functions of the input fuzzy variable “Throttle signal” Figure 4. Membership functions of the input fuzzy variable “Steering signal”

Design and development of a fuzzy rule-base for the differential FLC (D-FLC) Table B1 shows the developed seventeen fuzzy rules-based for D-FLC. These fuzzy rules show the relationship between the fuzzy inputs and fuzzy outputs, with “if”

'LC has maintained the wheel slip ratio of all the wheels within a range less than 0.35. shows that, the wheel slip ratio of all the wheels is positive and the proposed PSO-

Figure 14. The actual Yaw angle, Pitch angle and the Roll angle vs. time of the rover Conclusion

Figure C1. The straight road test on a slippery wet grass surface (captured when the speed of the rover is around 42.8 km/hr.)

Figure C4. The side view of the Electric Rover Figure C3. The front view of the Electric Rover rigure Uz. lop recorded speed while running on a gravel soil surface (as shown in Figure C4 the top recorded speed is around 90 km/hr)

Figure C5. Top recorded speed of the rover (have not discussed in this paper)

Table 1. Steady state error percentage (Ess%) of each independent wheel velocity of the rover is 1024.4 r.p.m. and 25.09 km/hr. respectively. longitudinal’, “AccY: lateral” and “AccZ: radial”) and the three gyro-angles (“Gy Yaw

Table C1: The physical parameters of the electric rover

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A new energy regeneration system for a BLDC motor driven electric vehicle

by International Journal of Electrical and Computer Engineering (IJECE)

2021, International Journal of Electrical and Computer Engineering (IJECE)

Amidst the ever-increasing advancements in the technological realm-the electrical vehicle industry too has seen several leaps. This particularly owes to three primary factors one, the fact that we are running out of conventional resources... more

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ALAT KENDALI KECEPATAN MOTOR PADA PENGGERAK DEPAN SEPEDA LISTRIK DI POLITEKNIK NEGERI BATAM

by Qoriatul Fitriyah and

2020, Jurnal Integrasi

Abstrak Motor Brushless DC (Motor BLDC) sudah banyak diaplikasikan diberbagai bidang industri, salah satunya di bidang transportasi. Pengoperasian motor BLDC memerlukan sistem kontrol kecepatan sebagai proses komutasinya dikarenakan motor... more

Abstrak Motor Brushless DC (Motor BLDC) sudah banyak diaplikasikan diberbagai bidang industri, salah satunya di bidang transportasi. Pengoperasian motor BLDC memerlukan sistem kontrol kecepatan sebagai proses komutasinya dikarenakan motor ini tidak menggunakan sikat (brush). Penulis membuat kendali kecepatan motor brushless ini menggunakan arduino nano sebagai mikrokontroller untuk mengatur switching driver mosfet dengan tipe IR2103. Mosfet driver ini yang digunakan untuk mentrigger pensaklaran mosfet pada rangkaian inverter 3 fasa agar daya dari baterai dapat mengalir ke motor BLDC. Mosfet yang digunakan dalam inverter 3 fasa dengan tipe STP75NF75 dengan Vds 75 Volt dan Ids 80 Ampere dan jenis motor yang digunakan BLDC 350 Watt 36 Volt. Motor BLDC ini memiliki hall effect sensor sebagai pendeteksi posisi rotor. Untuk proses komutasi digunakan PWM sebagai nilai pulsa yang direpresentasikan untuk mengatur kecepatan motor dan frekuensi yang digunakan masih default dari pin Arduino. Kecepatan yang dihasilkan dari 224-340 rpm dengan konsumsi daya 20 Watt-21 Watt. Abstract Brushless DC motor is applied in many field of industries, one of which transportation sector. The operation of BLDC motor need a speed control system as commutation process since it does not applied any brush. This research using an Arduino nano as its microcontroller to regulate the switching driver mosfet with IR2103 type. This mosfet driver is used to trigger mosfet switching at iverter 3 phase circuit so that power from battery can flow to BLDC motor. Mosfet type is STP75NF75 with Vds 75 Volt and Ids 80 Amps, while type of motor used is BLDC 350 watt and 36 Volt. The BLDC motor has Hall Effect sensor as rotor positioning detection. As for the commutation process, PWM is used as pulse modulation which is represented the motor speed and frequency of PIN default of Arduino. Speed earned is 224-340 rpm with power consumption of 20-21 Watts. PENDAHULUAN Pada era sekarang teknologi sudah banyak berkembang untuk memenuhi kebutuhan manusia diberbagai bidang seperti transportasi, informasi dan komunikasi serta bidang lainnya. Pengembangan teknologi salah satunya ditujukan untuk mengoptimalkan penggunaan energi bahan bakar minyak (BBM) yang semakin menipis. Menurut BPS tahun 2015 penggunaan BBM jenis Premium mencapai 71733 barel [1]. Oleh karena itu dibutuhkan sumber energi alternatif untuk membantu mengatasi krisis energi. Salah satu bentuk energi adalah energi listrik. Energi listrik adalah energi pembawa yang penggunaannya saat luas. Energi ini hampir digunakan pada setiap kegiatan manusia, mulai dari pemakaian industri, rumah tangga, pertanian, dan

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Smart and Efficient BLDC Motor based Aquaponics System with MPPT

by GRD JOURNALS

2020, GRD Journals

Aquaponics system needs very high electricity for the aeration of the system moreover as pumping of water. The proposed system using alternative energy as a source of energy for designing and developing standalone Aquaponics system with... more

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Design and Simulation study of Electro-Mechanical Actuator for Missile Maneuvering

by Siddharth Ram Mantri and

2020, International Journal of Scientific and Research Publications

Electro-Mechanical Actuator (EMA) is the key component in the guidance systems of missiles to convert electrical power into mechanical power. EMAs have shown significant improvement in response times and are more reliable compared to... more

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Solar Powered Speed Control of Brushless DC Motor

by IJRASET Publication

2020, international journal for research in applied science and engineering technology ijraset

Brushless DC (BLDC) motors are widely used for many industrial applications because of their high efficiency, high torque and low volume. In this project we will implement a controller to control speed of Brushless DC motor Using PWM... more

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SYSTEM IDENTIFICATION OF BLDC MOTOR AND OPTIMIZATION SPEED CONTROL USING ARTIFICIAL INTELLIGENT

by IAEME Publication

2019, IAEME Publcation

BLDC motors have the main contribution in the transportation industry applications, for example, is used as the driving electric bicycles. The purpose of this paper is to explain how to improve the performance of BLDC motors with... more

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Green Two-wheeled Mobility: Electric Scooters using Brushless Motor based on Fuzzy Logic Controller

by IAES Journals

2018, International Journal of Robotics and Automation (IJRA)

In the last years electric vehicles gained importance as a more sustainable alternative to traditional vehicles. The introduction of an electric power train leads to lower air-pollution emissions. 'Electric bicycles' are sometimes more... more

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Control of BLDC Motor Using dsPIC30F Controller

by Rajanikant Metri

BLDC- Brushless Direct Current motor is permanent magnet (PM) ac motor with a trapezoidal flx distribution. This paper presents a scheme for sensored control of a brushless dc (BLDC) motor by using dsPIC30F series advanced controller.... more

The modeling of brushless dc motor involves solving many simultaneous differential equations, each depending upon the inputs to the motor. It is assumed that the BLDC motor is connected to the output of the inverter, while the inverter input terminals are connected to a constant supply voltage, as shown in Figure 1[6]. In this paper control of brushless dc motor has been done us- ing digital signal processor- dsPIC30F series, which appears to be attractive for this application. The concept of the paper is to create a speed-closed lo op BLDC driver with Hall posi- tion sensors using a PI controller [5].

The block diagram and prototype experiment for the contro! system of blushless DC motor are shown in Figure 2 and Figure 3, respectively. The block diagram in Figure 2 depicts how the BLDC motor is driven using a dsPIC30F4011. The six MCPWM outputs are connected to three MOSFET driver cir- cuits, which in turn are connected to 9 MOSFETs (IRF2807). These MOSFETs are connected in a three-phase bridge for- mat to the three motor windings. In which one leg is having 3 MOSFETs-one in high and 2 in low sides. MOSFET driver circuit also requires a higher voltage (15V) to operate, so this voltage level needs to be provided [7].

The motor is a 24V BLDC motor so the DC+ to DC- bus volt- age is 24V and regulated 5V is provided to drive the dsPIC30F. The experimental results confirm the validity of system. The back EMF waveforms of phase A during the start of the BLDC motor is shown in Figure 6. The desired speed waveform of motor is shown in Figure 7.

A closed-loop control flow chart is shown in Figure 5. Figure 5. Flow Chart for BLDC Closed-loop Control Figure 4. Closed loop control of BLDC with PI controller vi . Res u Its A simulation results of a brushless DC motor system with the PID gains calculated using the above equations was per- formed using MATLAB and the results are shown below.

Vil. Conclusion In this paper a philosophy of control of brushless dc drive us- ing dsPIC30F series, digital signal controller is implemented and tested. The duty cycle is varied and the variation in the speed is measured and the performance of drive is found to be excellent. The output speed is found to respond faster for change in the input reference speed. This entire drive can be put in a compact module and it can be used for speed control applications.

In the closed-loop control version of the firmware, the main difference is that the pot is used for setting the demand. The control loop provides Proportional and Integral (PI) control of speed. The close loop control block diagram is as shown in Figure4.

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ANALYSIS AND CONTROL OF FOUR QUADRANT OPERATION OF THREE PHASE BRUSHLESS DC (BLDC) MOTOR DRIVE

by Editor IJRET

This paper gives a control for three phase brushless DC (BLDC) motor in all four quadrant operation. The control system in this paper is capable to detect and identify the rotor position by sensor signal . In this paper behaviour of BLDC... more

Fig 1 The schematic diagram as shown in the below The dspic30F2010 digital controller is used for control and determines the motor operation for different quadrant and regenerative braking. When the regenerative braking is applied normally closed (NC) contacts opens and normally open contacts (NO) contacts closes, hence wasting kinetic heat energy wasted in the motor the back EMF voltage utilized by three phase rectifier circuit to charge the battery. In fact the wasted energy is conserved and saving in regenerative braking operation. The arrangement of schematic diagram as shown in the below figure 1.[1]

Fig 2 Three phase Inverter Circuit to BLDC Motor

The three phase brushless DC (BLDC) motor is initially made to rotate in forward direction, but when the speed command is given reverse, the control goes into the forward braking mode hence current flows from motor to battery , in this case the rotor comes to the standstill position is term named as forward braking or regenerative braking. Instead of waiting for the absolute standstill position, continuous energization of the main phase is attempted. This rapidly slows down the rotor to a standstill position. Similarly for when the motor rotates in the reverse direction, but when the speed command is given positive, the controller goes into reverse regeneration mode, in this case the current is flows from motor to source.

Fig 5 Simulink Model of Four Quadrant Drive Fig 4 Hall sensor signals and stator EMF signals

Fig 7 Battery charging during regenerative braking Fig 8 Rotor speed for reverse motoring and reverse braking

Fig 6 Rotor speed for forward motoring and forward braking

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Digital Implementation of Fuzzy Logic Controller for Real Time Position Control Applications

by IOSR Journals

Fuzzy Logic Controller (FLC) systems have emerged as one of the most promising areas for Industrial Applications. The highly growth of fuzzy logic applications led to the need of finding efficient way to hardware implementation. Field... more

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BLDC Motor

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