No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Optimization of brushless direct current motor design using an intelligent technique
Abstract
This paper presents a method for the optimal design of a slotless permanent magnet brushless DC (BLDC) motor with surface mounted magnets using an improved bee algorithm (IBA). The characteristics of the motor are expressed as functions of motor geometries. The objective function is a combination of losses, volume and cost to be minimized simultaneously. This method is based on the capability of swarm-based algorithms in finding the optimal solution. One sample case is used to illustrate the performance of the design approach and optimization technique. The IBA has a better performance and speed of convergence compared with bee algorithm (BA). Simulation results show that the proposed method has a very high/efficient performance.
Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
... Brushless direct current (BLDC) motors are electronically commutated motors that provide high efficiency, good dynamic response, high mechanical reliability, low noise and vibration, long lifetime, and easy controllability [1]. These motors are widely used nowadays in servo drives, transport systems, medical instruments, industrial, residential applications [1,2]. ...
... Brushless direct current (BLDC) motors are electronically commutated motors that provide high efficiency, good dynamic response, high mechanical reliability, low noise and vibration, long lifetime, and easy controllability [1]. These motors are widely used nowadays in servo drives, transport systems, medical instruments, industrial, residential applications [1,2]. Also, their use can be found in aerospace, military and robotic applications. ...
Abstract The snubber circuit plays an important role in motor drives. This paper deals with the detection of the inverter switch snubber circuit resistance fault (ISSCRF) in brushless direct current (BLDC) motors used for robotic applications. This has been carried out in two parts: Fast‐Fourier‐Transform‐based analysis and wavelet‐decomposition‐based analysis on the stator current of the BLDC motor. The first analysis investigates the effects of different percentages of ISSCRF on direct current (DC) component, fundamental frequency component and total harmonic distortion percentage. Next analyses consider all of kurtosis, skewness and root‐mean‐square values of wavelet coefficients of stator current harmonic spectra. Comparative learning is made to obtain a few selective parameters best fit for the detection of ISSCRF. A fault detection algorithm to detect ISSCRF has been proposed and validated by three case studies. The algorithm is again modified with best‐fit parameters. Comparative discussion and novel contributions of the work have also been presented.
... Physically, the BLDC motor has three main parts: the stator, the rotor, and the electronic components. The stator usually has a similar design as the DC brushed motor's, with some windings to generate synchronous flux [13]. Meanwhile, the rotor has several poles of permanent magnet that will be locked to the current-conducting winding to make a rotation. ...
... For designing the BLDC motor, the model can either be approached from a synchronous motor [14] or be derived from a common brushed DC motor [15]- [17]. Nowadays, the BLDC motor can also be designed as a slotted machine to optimize the air gap flux density and to achieve higher power density [12], or as a slotless one, as it can simplify its structure and minimize resulted cogging torque [13]. Nonetheless, the design process of the BLDC motor needs the desired outputs to be set at the beginning of the process [18]. ...
span lang="EN-US">This paper proposes a design of a 5 kW, 100 volts brushless direct current (DC) (BLDC) motor using an existing stator connected to an inverter and equipped with Hall sensors. The stator is a radial flux motor-type with 54 slots positioned at the outer side of the machine. In this case, the design is focused on the rotor components and winding configuration. However, the inverter aspects are also taken into account. At the same time, it considers the expected outputs: voltage, power, speed; and some limitations: maximum current and flux density. Finite element magnetic-based simulation is performed to extract the magnetic flux distribution, and analytical calculations are then conducted to obtain the output values and characteristics. The results show the BLDC motor at nominal speed produces 5.1 kW output power with 122.34 V voltages, 97.09% efficiency, and torque of 32.82 Nm. The maximum torque and rotation speeds are 51.39 Nm and 4,150 rpm respectively, while the peak-to-peak cogging force is 1.35 Nm. It can be concluded that the BLDC motor has a good performance and is compatible with the connected inverter.</span
... Input voltage and current are the two parameters fed to the PID controller to regulate the speed of the BLDC motor [3]. In literature, different conventional and intelligent techniques are proposed to regulate the speed of the BLDC motor in a smooth way [4], [5]. Lee et al. [6], proposed and effectively reduced the distortion current of 1-Φ BLDC motor by minimizing the torque ripples during high-speed operation. The proposed Reference Voltage Controlled PWM technique is affected by the undervalue of a reference voltage. ...
This paper presents dual examine algorithm (DEA) to reduce residual error as well as to provide accurate phase currents without any distortion for a closed loop brushless direct current (BLDC) motor of an electric vehicle (EV). The underlying technology of DEA is a hybrid of the tabu search optimization (TSO) method and the genetic algorithm (GA). During closed loop operation of BLDC motor residual error is introduced by the discrepancy between the actual and reference speed, and the phase current distortion lowers the efficiency of the machine as a result machine performance is degraded. To address these issues, GA algorithm calculates the necessary parameters for the controller to produce precise current without distortion based on stator phase currents, and the suggested TSO algorithm limits the repeated operations in the PID controller to reduce the residual error to the greatest degree feasible. After primary examining, dual examine process initiate the transposing operation such as TSO is used to prove and calculate the phase current controller parameters, and GA is used to correct for remaining inaccuracy. To validate the proposed DEA algorithm is compared with advanced particle swarm optimization (APSO). The results verified the superiority of proposed DEA algorithm using MATLAB/Simulink platform.
... The magnet depth, hanging, and stator ratio for an axial flux-based PMBLDC motor were determined using a finite element analysis in [1]. Improved Bee algorithm is used for designing a BLDC motor by minimizing loss, cost, and volume as objective functions [2]. The Pole and slot type for the outer rotor of BLDCM and BLACM is modeled using finite element analysis by reducing torque ripple conditions [3]. ...
PMBLDC motor is a type of brushless motor with a permanent magnet as a rotor material. Its main advantage is high efficiency and high lifetime due to less friction in the operation. Its efficiency can be improved further by the proper selection of BLDC motor design. Based on that, in the existing approach, the finite element analysis (FEA) is carried out using software for finding the stator material and the number of turns. This approach provides the optimal stator material with fixed turns as PMBLDC motor design. But in this, the analysis is carried out only for the two numbers of turns only. Hence, in this, a hybrid optimization approach is proposed for designing the PMBLDC motor. The hybrid optimization selects the pole pairs, thickness winding, and other PMBLDC parameters by minimizing the power loss of the motor. To perform this, here, the moth flame and Cauchy particle swarm optimization are used to determine the optimal PMBLDC parameters using MATLAB R2020b version under a windows 10 environment. The proposed method's performance will be compared with the existing in terms of torque and power loss.
... Compared with the traditional DC motor, the brushless DC motor (BLDCM) adopts permanent magnets to replace the excitation winding coils in the rotor structure, and cancels the brush and the commutation device. With the characteristics of light weight, high efficiency and long lifetime, the BLDCM has been widely applied in aerospace, transportation, industry, agriculture and other fields [1][2][3][4]. ...
To meet the application requirements of high-precision and fast-response thrust vector control from a solid rocket motor, a digital and integrated servo controller for four brushless direct current motors was designed and implemented. The controller hardware system proposed in this paper includes control circuit module and drive circuit module. The former mainly includes power conversion circuit, DSP system circuit, A/D circuit, SPI communication interface circuit, and SCI communication interface circuit. The later mainly includes three-phase full-bridge six-arm drive circuit and optocoupler isolation circuit. The three closed-loop control strategies of position, speed and current based on PID control algorithm are adopted, and the mathematical model of the motor is established in Matlab/Simulink for verification. The structure composition and working principle of the servo controller are briefly introduced, and the hardware design scheme, software workflow and control strategy are described in detail. Experimental results show that the response speed and control accuracy of the proposed controller meet the technical requirements.
... It results in a reduction of the design period and overall cost. The BLDC motor design [2] is considered an optimization problem from the detailed literature, and the benchmark model for the same is formulated and presented in [3,4]. ...
The first step in the design phase of the Brushless Direct Current (BLDC) motor is the formulation of the mathematical framework and is often used due to its analytical structure. Therefore, the BLDC motor design problem is considered to be an optimization problem. In this paper, the analytical model of the BLDC motor is presented, and it is considered to be a basis for emphasizing the optimization methods. The analytical model used for the experimentation has 78 non-linear equations, two objective functions, five design variables, and six non-linear constraints, so the BLDC motor design problem is considered as highly non-linear in electromagnetic optimization. Multi-objective optimization becomes the forefront of the current research to obtain the global best solution using metaheuristic techniques. The bio-inspired multi-objective grey wolf optimizer (MOGWO) is presented in this paper, and it is formulated based on Pareto optimality, dominance, and archiving external. The performance of the MOGWO is verified on standard multi-objective unconstraint benchmark functions and applied to the BLDC motor design problem. The results proved that the proposed MOGWO algorithm could handle nonlinear constraints in electromagnetic optimization problems. The performance comparison in terms of Generational Distance, inversion GD, Hypervolume-matrix, scattered-matrix, and coverage metrics proves that the MOGWO algorithm can provide the best solution compared to other selected algorithms. The source code of this paper is backed up with extra online support at https://premkumarmanoharan.wixsite.com/mysite and https://www.mathworks.com/matlabcentral/fileexchange/75259-multiobjective-non-sorted-grey-wolf-mogwo-nsgwo.
... Population-based metaheuristics such as evolutionary algorithms and swarm-based intelligence (SI) algorithms have gained prominence to solve different kinds of electromagnetic optimization problems [1][2][3][4][5][6][7][8][9][10] involving nonlinear, non-convex, multi-modal, and non-differentiable functions mainly due to advantages when compared with single-point search algorithms of mathematical programming in terms of no need to objective function be differentiable and continuous, and global searching capability. ...
Nature-inspired metaheuristics of the swarm intelligence field are a powerful approach to solve electromagnetic optimization problems. Ant lion optimizer (ALO) is a nature-inspired stochastic metaheuristic that mimics the hunting behavior of ant lions using steps of random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps. To extend the classical single-objective ALO, this paper proposes four multiobjective ALO (MOALO) approaches using crowding distance, dominance concept for selecting the elite, and tournament selection mechanism with different schemes to select the leader. Numerical results from a multiobjective constrained brushless direct current (DC) motor design problem show that some MOALO approaches present promising performance in terms of Pareto-optimal solutions.
... By introduction of heuristic algorithms, many studies have been done for solving engineering problems using these methods. These methods are used in a wide area of optimization problems ranging from design of electrical motors (Shabanian et al. 2015) to DNA sequencing (Blum et al. 2008). They have also shown a great capability to solve economic dispatch problems, for which CHPED problem can be also included in such problems. ...
Combined heat and power economic dispatch (CHPED) is an energy management problem that minimizes the operation cost of power and heat generation while a vast variety of operational constraints of the system should be met. The CHPED is a complicated, non-convex and non-linear problem. In this study, a new real-coded genetic algorithm with random walk-based mutation (RCGA-CRWM) is under study, which is effective in solving large-scale CHPED problem with minimum operation cost. In the presented optimization method, a simple approach is introduced to combine the positive features of different probabilistic distributions for the step size of random walk. Using the presented approach, while the genetic algorithm is speeded up, the premature convergence is also avoided. After verifying the performance of the presented method on the benchmark functions, two large-scale and two medium-scale case studies are used for determining the algorithm strength in solving the CHPED problem. Despite the fact that the complexity of the CHPED rises dramatically by increasing its dimensionality, the algorithm has solved the problems accurately. The application of RCGA-CRWM method improves the results of the CHPED problem in terms of both operation cost and convergence speed in comparison with other optimization methods.
... 24 Conventionally, leastsquares approximation method, genetic algorithm (GA), particle swarm optimization (PSO) algorithm, neural network (NN) and improved gradient descent algorithm was utilized. 25 El-samahy and Shamseldin 26 have compared the performance of two different control techniques applied to highperformance BLDC motor. The first scheme is self-tuning fuzzy PID controller and the second scheme is model reference adaptive control (MRAC) with PID compensator. ...
This paper presented the brushless direct current motor torque ripple reduction based on the speed and torque control using hybrid technique. The dynamic behavior of the brushless direct current motor is analyzed in terms of the parameters such as the speed, current, back electromotive force and torque. Based on the parameters, the motor speed is controlled and minimized the torque ripples. For controlling the speed of the brushless direct current motor is utilized the fractional-order proportional–integral–derivative controller for generating the optimal control pulses. With the use of fractional-order proportional–integral–derivative controller, the optimal gain parameters are needed to reduce the torque ripples and control the speed of brushless direct current motor. By utilizing the hybrid technique, the gain parameters are utilized to analyze the optimal gain parameters of fractional-order proportional–integral–derivative controller. The hybrid technique is the combination of adaptive neuro-fuzzy inference system with firefly algorithm. The proposed strategy is simple in structure and robust to reduce the complexities of the mathematical computations. Initially, the nature inspired optimization algorithm of firefly algorithm is analyzed for finding the error function. In addition, the efficient adaptive neuro-fuzzy inference system controller which becomes an integrated method of approach is performed to control the error functions in order to yields excellent optimized gain values. After that, the control signals are applied to the input of voltage source converter of brushless direct current motor. With this control strategy, the harmonics and torque ripples are minimized. Based on the proposed control strategy, the speed and torque performance is analyzed. The effectiveness of the proposed technique is implemented in MATLAB/Simulink platform and evaluates their performance. The performance analysis of the proposed method is demonstrated and contrasted with the existing techniques such as bat algorithm, particle swarm optimization algorithm and ant–lion optimizer algorithm with fractional-order proportional–integral–derivative controller techniques.
... Komutasi elektronik yang telah dibuat memiliki efisiensi yang tinggi, sedikit getaran, sedikit noise pada penggunaan kecepatan tinggi, sehingga menjadikan BLDC memiliki umur pemakaian yang lama dan biaya perawatan yang rendah. Sekarang motor DC Brushless banyak digunakan pada aplikasi industri, robotika, aplikasi kesehatan, dan otomotif [1], [2], [3]. Telimek telah dan sedang melakukan pengembangan robotika untuk pertahanan dan keamanan maupun untuk industri. ...
Many industrial applications use servo motor because of its accuracy and user-friendly, but by using it in an application, a higher cost is required. To make an actuator with good precision and universal purpose but with lower cost, a position control system for brushless dc motor was built. To achieve a better precision in the position control for brushless DC motor, the system is utilized with a microcontroller ATmega 2560, an absolute encoder as a position sensor, and also Proportional-Derivative closed-loop control algorithm. In the final test, we obtained that the system worked well on average angular speed about 3.88º/ms and angle tolerance about 1º.
Brush-less DC motors with permanent magnets are widely used in robotics, electric vehicles, and other industrial applications. Enhancing the performance of a BLDC motor can indeed be challenging due to the presence of non-linearities and complex design considerations. The present research aims to increase the effectiveness and performance of BLDC motors by using a feed-forward neural network (FNN). It is possible to quickly determine the FNN’s appropriate weight and bias settings by using the cutting-edge African Vulture Optimisation Algorithm (AVOA). Combining biological inspiration, machine learning, and motor technology, it develops a distinctive and promising way to improve the performance of BLDC motors. This study also includes the sensitive analysis of controlling parameters of the AVOA to investigate its effect on the statistical variables of the fitness function. This aids in optimizing the algorithm for better outcomes. The proposed method is then contrasted with various FNN learning algorithms based on Practical Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and PSO-GSA. Considerations like convergence rate and the capacity to steer clear of local minima are probably evaluated throughout the comparison. According to the results, AVOA-based optimization performs better than the other methods (PSO, GSA, and PSO-GSA) in terms of convergence speed and its ability to avoid becoming stuck in local minima. This suggests that the AVOA is useful for training FNNs to optimize the design parameters of BLDC motors.
This paper presents soft computing-based optimization techniques for the cogging torque reduction and thermal characterization by finite element analysis in a permanent magnet brushless DC motor (BLDC). Stator and rotor structure of BLDC motor are optimized to reduce the cogging torque, noise, and vibration by using the design parameters namely: length of magnet, length of air gap and opening in the stator slot which are selected by performing variance-based sensitivity analysis. The proposed method is suitable in the preliminary design phase of the motor to determine the optimal structure to improve the efficiency. The comparison of results obtained using firefly algorithm , ant colony optimization algorithm and Bat algorithm indicate that Firefly-based optimization algorithm is capable of giving improved design parameter output. Cogging torque is created due to the interaction of magnets in the rotor and the stator slot of the motor. Thorough thermal analysis is also conceded out to investigate the thermal characteristics at dissimilar portions of the motor namely: stator core, stator slot, rotor core and permanent magnet at different operating environments in the continuous operation mode. Thermal investigation is required for the various high speed e-vehicle applications. The usefulness of the designed machine simulation is compared with the results obtained from hardware analysis. The outcomes attained from software simulation studies are validated through experimental hardware setup.
In order to meet the requirements of multi-degree-of-freedom (multi-DOF) driving units for omnidirectional vehicle wheels, a multi-DOF spherical induction motor (SIM) is proposed with composite rotor through the conversion of design concept of linear induction motor (LIM), which compensates for the insufficiency of traditional multi-DOF driving scheme with structural complexity, control difficulties and poor dynamic performance. The SIM realizes two DOF driving and has the characteristics of direct output on rotor surface. Firstly, the overall design of SIM is drafted, as well as finite element model and control system are established. Subsequently, in order to optimize SIM performance, the multi-objective optimization design of composite rotor structure is proposed by adopting non-dominated sorting genetic algorithm-II (NSGA-II). The results show that the reasonable design of rotor structure can effectively improve output torque and reduce fluctuation. And the proposed design idea based on theory of LIM can provide a reference for SIM design.
A prominent and realistic problem in magnetics is the optimal design of a brushless direct current (BLDC) motor. A key challenge is designing a BLDC motor to function efficiently with a minimum cost of materials to achieve maximum efficiency. Recently, a new metaheuristic optimization algorithm called the Coronavirus Herd Immunity Optimizer (CHIO) is reported for solving global optimization problems. The inspiration for this technique derives from the idea of herd immunity as a way of combating the coronavirus pandemic. A variant of CHIO called Multi-Objective Coronavirus Herd Immunity Optimizer (MOCHIO) is proposed in this paper, and it is applied to optimize the BLDC motor design optimization problem. A static penalty constraint handling is introduced to handle the constraints, and a fuzzy-based membership function has been introduced to find the best compromise results. The BLDC motor design problem has two main objectives: minimizing the motor mass and maximizing the efficiency with five constraints and five decision/design variables. First, MOCHIO is tested with benchmark functions and then applied to the BLDC motor design problem. The experimental results are compared with other competitors are presented to confirm the viability and dominance of the MOCHIO. Further, six performance metrics are calculated for all algorithms to assess the performances.
Purpose
The brushless direct current motor (BLDCM) is widely accepted and adopted by many industries instead of direct current motors due to high reliability during operation. Brushless direct current (BLDC) has outstanding efficiency as losses that arise out of voltage drops at brushes and friction losses are eliminated. The main factor that affects the performance is temperature introduced in the internal copper core windings. The control of motor speed generates high temperature in BLDC operation. The high temperature is due to presence of ripples in the operational current. The purpose is to present an effective controlling mechanism for speed management and to improve the performance of BLDCM to activate effective management of speed.
Design/methodology/approach
The purpose is to present an optimal algorithm based on modified moth-flame optimization algorithm over recurrent neural network (MMFO-RNN) for speed management to improve the performance. The core objective of the presented work is to achieve improvement in performance without affecting the design of the system with no additional circuitry. The management of speed in BLDCM has been achieved through reduction or minimization of ripples encircled with torque of the motor. The implementation ends in two stages, namely, controlling the loop of torque and controlling the loop of speed. The MMFO-RNN starts with error optimization, which arises from both the loops, and most effective values have been achieved through MMFO-RNN protocol.
Findings
The parameters are enriched with Multi Resolution Proportional Integral and Derivative (MRPID) controller operation to achieve minimal ripples for the torque of BLDC and manage the speed of the motor. The performance is increased by adopting this technique approximately 12% in comparison with the existing methodology, which is the main contributions of the presented work. The outcomes are analyzed with the existing methodologies through MATLAB Simulink tool, and the comparative analyses suggest that better performance of the proposed system produces over existing techniques, and proto type model is developed and cross verifies the proposed system.
Originality/value
The MMFO-RNN starts with error optimization, which arises from both the loops, and most effective values have been achieved through MMFO-RNN protocol. The parameters are enriched with MRPID controller operation to achieve nil or minimal ripples and to encircle the torque of Brushless Direct Current and manage the speed.
Robust dynamic speed response with less maintenance and efficient operation makes the Brushless Direct Current (BLDC) motors as an oblivious choice for many motoring applications which needs instantaneous speed control while developing high torque. This paper proposes Jaya optimization Algorithm on Xilinx platform for BLDC Motor and Fractional Order Proportional Integral Derivative controller to improve its efficiency through minimizing the ripples present in the Torque of the motor. The control strategy of torque controller works with the combinations of Xilinx and optimization algorithms. In this proposed controller, the strategy of control is achieved after deriving the BLDC motor's dynamic mode there after the electronic interaction with BLDC motor is enrooted with the Math lab/Simulink model while tactically utilizing Xilinx tools for all kind of co-simulation in the same model. Initially the dynamic model of a BLDC motor drive is derived and the control topology is designed with the help of the proposed controller. The controlling mechanism established in this paper is effective and efficient in minimizing the ripples present in the torque of the motor. The proposed controller is utilized for possible low-cost and high-performance industrial applications. The results are obtained and analyzed with existing methodologies and it shows that the proposed technique outperforms the existing models.
In this article, the design of brushless DC (BLDC) motor is performed using multiobjective optimization algorithm (MOOA) by satisfying multiple objectives. Initially, sensitivity analysis is carried out to find the most influencing parameters that affect the performance of BLDC motor. MOOAs such as Pareto envelope‐based selection algorithm (PESA), Pareto archived evolution strategy (PAES) and nondominated sorting genetic algorithm‐II (NSGA‐II) is employed in the optimal design of the BLDC motor. The proposed MOOA have three objectives namely: output torque maximization, volume minimization, and minimization of total losses. MOOAs are analyzed using performance metrics and qualitative comparison is provided to select the best algorithm. Later, finite element method (FEM) is used to investigate the transient and thermal characterization on the BLDC motor designed using NSGA‐II. The thermal results thus obtained using NSGA‐II for the above motor under different operating conditions is also compared with the existing single objective optimization algorithm. From the comparisons, it is observed that NSGA‐II algorithm significantly outperforms the existing single objective optimization algorithm. Finally, the usefulness of the designed machine based on NSGA‐II is compared with the results obtained from simulation and hardware analysis. In this article, design of brushless DC (BLDC) motor has been optimized using multi objective optimization (MOO) algorithms. The thermal analysis and transient analysis has been carried out on the BLDC designed used nondominated sorted genetic algorithm‐II (NSGA‐II).The usefulness of the designed machine based on NSGA‐II is compared with the results obtained from simulation and hardware analysis.
This paper compares the different permanent magnet materials for a wiper application, especially front wiper system. Simulation-based study on electromagnetic performance and thermal predictions are discussed. Initially, the design of a brushless direct current (BLDC) motor is made for the required power of 52 Watts within a pack size of 56 * 35 mm. Results obtained by using different types of permanent magnets in the same pack size are carefully studied and compared. Finally, the thermal characteristics of the designed motors are predicted and compared.
Due to the expanded industrialization, the necessity of variable speed machines/drives keeps on expanding. The vast majority of computerized Brushless Direct Current (BLDC) motor frame-works are utilized because of their speedier reaction and high stablity. In this paper, an innovative technique, i.e. Adaptive Neuro-Fuzzy Inference System (ANFIS) with Fractional-Order PID (FOPID) controllers for controlling a portion of the parameters, for example, speed, and torque of the BLDC motor are exhibited. With a specific end goal being the performance of the proposed controller under outrageous working conditions, for example, varying load and set speed conditions, simulation results are taken for deliberation. An Opposition-based Elephant Herding Optimization (OEHO) optimization algorithm is utilized to improve the tuning parameters of FOPID controller. At that point, the ANFIS is gladly proposed to adequately control the speed and torque of the motor. The simulation result exhibited that the composed FOPID controller understands a decent dynamic behavior of the BLDC, an immaculate speed tracking with less ascent and gives better execution. The performance investigation of the proposed strategy lessened the error signal contrasted with the existing strategies, for example, FOPID-based Elephant Herding Optimization (EHO), Proportional-Integral-Derivative BAT (PID-BAT), and PID-ANFIS.
In this paper, Multi-Objective Optimization (MOO) techniques namely: Weighted Sum Method (WSM), Multi Objective Genetic Algorithm (MOGA) and Niched Pareto Genetic Algorithm (NPGA) are proposed for the design optimization and analysis of magnetic flux distribution for a slotless permanent magnet Brushless DC (BLDC) motor. The sensitivity analysis is carried out to identify the design variables of BLDC motor which influence the objective function. The objective functions are conflicting in nature (maximization of output torque and minimization of total volume and losses), hence, are optimized simultaneously by means of MOO algorithms. The proposed MOOs account for four kinds of design variables namely: rotor radius, stator/ rotor axial length, magnet thickness and winding thickness simultaneously to maximize the output torque and to reduce the total volume and total power loss. The conventional MOO such as WSM gives single pareto optimal solution. However, the proposed MOGA and NPGA algorithms create accurate and well-distributed pareto front set with few function evaluations. The performances of the three MOOs are evaluated and compared using the four performance metrics namely: Hyper Volume (HV), Generational Distance (GD), Inverted Generational Distance (IGD) and Spread. From the comparison, it is observed that NPGA gives better results. Using the design parameter obtained from NPGA, the magnetic flux distribution analysis of the BLDC motor is carried out to analyze variation of flux distribution in the different parts of the motor. The results thus obtained are compared with those obtained through Partial differential equation and single objective GA optimization. The detailed thermal analysis is also carried out to analyze the thermal behavior at different parts of the machine for different working conditions in the continuous operation mode and hence, the results obtained are compared with single objective GA optimization. The advantages of MOOs in the design optimization of slotless permanent magnet BLDC motor are highlighted.
Energy efficiency improvement of electrical motor drives has recently become a very interesting subject. Several methods have been proposed in the literature to improve the efficiency of permanent magnet synchronous machine (PMSM) with sinusoidal back-EMF. These methods are not precise and appropriate for brushless DC machine (BLDCM) with trapezoidal back-EMF. As a unique solution, this work introduces a flux controlled based loss minimization algorithm suitable for BLDCM that considers iron loss as well as influence of back-EMF harmonics; consequently promotes efficiency of machine. In this regard, the loss model of PMSM is extended based on multiple reference frame analysis to include back-EMF harmonics of BLDCM. As an advantage, proposed modified loss model does not require any additional data about the dimensions of machine that makes it suitable for industrial motor drive applications. The Proposed loss model is validated through experimental tests in different operating conditions. Afterward, by applying direct torque and indirect flux control of BLDCM as the control technique, d-axis current in the rotor reference frame is controlled to reduce the air gap flux and consequently iron loss of the machine. Finally, a procedure is presented to determine the optimum d-axis current which maximizes the efficiency. Effectiveness of proposed control system is evaluated using simulation results in MATLAB/Simulink and experimental results on a practical prototype. It is indicated that depending on the operating conditions, about 2% to 11% of efficiency improvement would be achieved in the proposed method.
In the theory of inverse problem, the parameters identification by optimization is considered as one of its main applications. This paper presents an optimal design of a slotted permanent magnet Brushless DC (BLDC) motor with surface mounted magnets. The inverse problem method is applied by using a thriving solver afforded by the nonlinear optimization toolbox of Matlab ‘Fmincon’, this function is based on Active-Set and Sequential Quadratic Programming approaches with calculation of the Hessian from Quasi-Newton algorithm. The optimal magnetic field density considered as the main objective is obtained by picking several parameters and analyzing their effects. The proposed approach is highlighted by using the obtained parameters in the design of the motor. The Finite element method is applied on the motor for numerical analysis by using FEMM magnetic coupled with Matlab code. Effectiveness and robustness of the proposed approach are verified by a comparison between the initial and optimized design.
This article presents an overview of diagnostic principles of incipient faults in electric machines and drive systems. Applications of AI tools in automating the task of fault detection are also highlighted. Operating principles of CSI–fed PM brushless DC motors are briefly introduced. A summary of an extensive study of automatic fault diagnosis and location in CSI–fed PM brushless DC motor drives, based on adaptive neuro–fuzzy inference systems (ANFIS), is reported. A sample of ANFIS testing cases and acceptable matching between simulated and measured performances of the drive show effectiveness of the proposed methodologies.
This study presents the design of a three-phase slotless brushless DC (BLDC) motor for use as an electromagnetic actuator in robotics. To achieve the high torque-to-inertia and torque-to-weight ratios required for fast response in robotic applications, genetic algorithms (GAs) are used to optimise the initial motor design within a reasonable geometry. We examine the machine performance using 3-D finite element analysis (FEA) and validate them by experimental measurement.
The paper presents a simplified analytical method to design a small two-pole slotless brushless dc motor. Using only a few approximations, the motor analytical model is formulated to generate a system of equations. The system can be solved analytically, producing a motor design for given specifications. The method is used to design a motor with the specifications 150 W, 10 000 rpm, and 18 V
In this paper, the design of two prototypes of the autonomous micro helicopter muFly is introduced. These prototypes have been built and flown in untethered test flights. The rotor configuration and steering principle selection of the prototypes are discussed according to the specifications, and the selection of a coaxial configuration with a swash plate steering of the lower rotor is justified based on dynamic simulation results. The two prototype designs are shown and discussed with special attention paid to their respective system integration issues, which are solved. Finally, the prototypes are compared and conclusions for future designs of autonomous micro helicopters are drawn.
This paper presents the design and implementation of a high performance position-sensorless control scheme for the extensively used brushless DC (BLDC) motors. In the proposed method, with proper PWM strategy, instead of detecting the zero-crossing point (ZCP) of the nonexcited motor back electromagnetic force (EMF) or the average motor terminal to neutral voltage, the true zero-crossing points of back EMF are extracted directly from the difference of the specific average line-to-line voltages with simple RC circuits and comparators. In contrast to conventional methods, the neutral voltage is not needed and the diode freewheeling currents in the nonconducted phase are eliminated completely; therefore, the commutation signals are more accurate and insensitive to the common-mode noise. Moreover, 100% pulse-width-modulation (PWM) duty ratio control of BLDC motors is provided with the presented method. As a result, the proposed method makes it possible to achieve good motor performance over a wide speed range and to simplify the starting procedure. The detailed circuit model is analyzed and some experimental results obtained from a sensorless prototype are shown to verify the analysis and confirm the validity of the proposed method.
Purpose
– Analytical models are often used in the first steps of the design process. They are associated with optimisation methods to find a solution that fulfil the design specifications. In this paper, the analytical model of an electric motor is built and proposed as a benchmark to highlight the optimisation methods the most fitted to analytical models.
Design/methodology/approach
– This paper studies the optimal design of a brushless DC wheel motor. First, the analytical model is presented. Each equation used for the sizing is described, including the physical phenomenon associated, the hypotheses done, and some precautions to take before computing. All equations are ordered to ease their resolution, due to a specific procedure which is then described. Secondly, three optimisation problems with an increasing number of parameters and constraints are proposed. Finally, the results found by the sequential quadratic method point out the special features of this benchmark.
Findings
– The constraint optimisation problem proposed is clearly multimodal as shown in the results of one deterministic method. Many starting points were used to initialise the optimisation methods and lead to two very different solutions.
Originality/value
– First, an analytical model for the optimal design is detailed and each equation is explained. A specific procedure is presented to order all equations in order to ease their resolution. Secondly, a multimodal benchmark is proposed to promote the development of hybrid methods and special heuristics.
Genetic algorithm (GA) based design optimization of a permanent magnet brushless dc motor is presented in this paper. A 70 W, 350 rpm, ceiling fan motor with radial-filed configuration is designed by considering the efficiency as the objective function. Temperature-rise and motor weight are the constraints and the slot electric loading, magnet-fraction, slot-fraction, airgap, and airgap flux density are the design variables. The efficiency and the phase-inductance of the motor designed using the developed CAD program are improved by using the GA based optimization technique; from 84.75% and 5.55 mH to 86.06% and 2.4 mH, respectively.
In this study, the dynamic behaviors of a BLDC motor are analyzed, when the motor undergoes mechanical and electromagnetic interaction due to an air gap variation between the stator and rotor. When considering the air gap variation caused by the translational motion of the rotor relative to the stator, the kinetic and potential energies, Rayleigh dissipation function, and the magnetic coenergy are expressed in terms of the rotor displacements and stator currents. With these energies and function, new equations of motion are derived using Lagrange’s equation. The equations for the proposed model are nonlinear equations in which the displacements and currents are coupled. The time responses for the displacements and currents are computed for the proposed and previous models. Furthermore, the effects of rotor eccentricity are also investigated. It is found that, when the air gap varies with time, the time responses for the proposed and previous models have small differences in the stator currents, electromagnetic torques, and rotating speeds. However, the time responses have large differences in the rotor displacements. Therefore, this paper claims that the proposed model describes the dynamic behaviors of the motor more accurately than the previous model. It is also shown that rotor eccentricity increases the stator current period and the electromagnetic torque, while it decreases the rotating speed of the rotor.
As the aircraft technology is moving towards more electric architecture, use of electric motors in aircraft is increasing. Axial-flux BLDC motors are becoming popular in aero application because of their ability to meet the demand of light weight, high power density, high efficiency and high reliability. Axial-flux BLDC motors in general and ironless axial flux BLDC motors in particular, come with very low inductance. Because of this they need special care to limit the magnitude of ripple current in motor winding. In most of the new more electric aircraft applications, BLDC motor needs to be driven from 300Vdc or 600Vdc bus. In such cases, particularly for operation from 600Vdc bus, IGBT based inverters are used for BLDC motor drive. IGBT based inverters have limitation on increasing the switching frequency, and hence they are not very suitable for driving BLDC motors with low winding inductance. In this paper, three-level NPC inverter is proposed to drive axial flux BLDC motors. Operation of BLDC motor driven from three-level NPC inverter is explained and experimental results are presented.
This paper presents a design method for a small-sized slotless brushless dc motor. Distributed hexagonal windings are employed for achieving a high torque density and for ease of manufacture. Numerical approaches with an analytic model are used to analyze the magnetic and output characteristics of the motor. The proposed motor is manufactured, and the empirical results are compared with the results of the simulation.
This paper aims to develop a high power density and high efficiency of motor for electric vehicles. The motor, which is used to replace the traditional engine-driven, is a 5-phase 22-pole square-wave brushless permanent magnet (PM) DC motor. The design and optimization of the motor is done with the aid of electromagnetic field analysis based on the finite element method.
This paper presents a method for the optimal design of a slotless permanent magnet brushless DC (BLDC) motor with surface mounted magnets using a genetic algorithm. Characteristics of the motor are expressed as functions of motor geometries. The objective function is a combination of losses, volume and cost to be minimized simultaneously. Electrical and mechanical requirements (i.e. voltage, torque and speed) and other limitations (e.g. upper and lower limits of the motor geometries) are cast into constraints of the optimization problem. One sample case is used to illustrate the design and optimization technique.
A computational study of a brushless DC motor is presented to determine the thermo-flow characteristics in the windings and bearings under the effects of heat generation. The rotation of the rotor blades drives an influx of ambient air into the rotor inlet. The predicted inflow rates were higher at the front inlet than at the rear inlet due to non-uniform pressure distribution. A recirculation zone appeared in the tiny interfaces between windings. The poor cooling performance was caused by flow separation near the groove threshold by the inclination angle of the bearing groove and by a relatively slow velocity near the bearing and between windings. Based on these results, design parameters for the inlet location and geometry, and for the bearing groove geometry, were determined and optimized to enhance the cooling performance up to 24%.
This paper presents a simulation method for brushless DC motors. The purpose of this method is to provide a solution to the initial design process, where the simulation speed is important due to the numerous variables that have to be considered. The method requires a reduced number of parameters in order to simplify it and to facilitate the design process. It is applicable to polyphase motors regardless of its connection or phase number. The simulation method can simulate the phase current control system or a DC bus current feedback system. The later control system is widely used in low-cost brushless DC motors. The simulation method is based upon the average value modelling of the inverter leg, at the PWM scale, in different modulation conditions, modelling almost all the inverter structures with transistors as active elements. The experimental results from a seven-phase brushless DC motor with one uncontrolled phase validate the modelling method. The comparison of the average torque, the current, voltage and torque waveforms in several operating conditions shows that the simulation method can predict the comportment of a brushless DC motor without an excessive computational burden, so it can be readily used as a design tool for this kind of motor.
The direct back-electromotive-force (EMF) detection method previously described in a sensorless brushless dc (BLDC) motor-drive system (Proc. IEEE APEC, 2002, pp. 33-38) synchronously samples the motor back EMF during the pulsewidth-modulation (PWM) off time without the need to sense or reconstruct the motor neutral. Since this direct back-EMF-sensing scheme requires a minimum PWM off time to sample the back-EMF signal, the duty cycle is limited to something less than 100%. In this paper, an improved direct back-EMF detection scheme that samples the motor back EMF synchronously during either the PWM on time or the PWM off time is proposed to overcome the problem. In this paper, some techniques for automotive applications, such as motor-rotation detection, and current sensing are proposed as well. Experimental results are presented
An analytical technique for predicting the instantaneous magnetic
field distribution in the airgap region of radial-field topologies of
brushless permanent-magnet DC motors, under any specified load condition
and accounting implicitly for the stator winding current waveform and
the effect of stator-slot-openings, has been developed. It is based on
the superposition of the component fields due to the permanent magnet
and the stator excitation. A 2D analytical method for predicting the
open-circuit airgap field distribution in both internal and external
rotor radial-field motor topologies is presented. It involves the
solution of the governing field equations in polar coordinates in the
annular airgap/magnet region of a multipole slotless motor in which the
magnets are assumed to have uniform radial magnetization and a constant
relative recoil permeability. Results for various radial-field motors
are compared with predictions from corresponding finite element analyses
For pt.I see ibid., vol.29, no.1, p.124-135 (1993). An analytical
technique for predicting the open-circuit magnetic field distribution in
the airgap/magnet region of a brushless permanent-magnet DC motor
equipped with a surface mounted magnet rotor and a slotless stator was
presented in Pt.I. In the present work, the analysis is extended to the
prediction of the armature reaction field produced by the three-phase
stator windings. As before, th motor model is formulated in polar
coordinates and accounts for the large effective airgap, but it is not
developed further to consider the effect of winding current harmonics on
the airgap field distribution. Predicted instantaneous armature reaction
field distributions are validated by a comparison with corresponding
finite element calculations