Brushless DC Motor Research Articles

Design, control and performance comparison of PI and ANFIS controllers for BLDC motor driven electric vehicles

The research and usage of electric vehicles (EVs), including two and four-wheeler vehicles, are rapidly increasing worldwide as alternatives to oil/gas-based vehicles. Brushless direct current (BLDC) motors are popular for industrial and traction applications due to their inherent advantages. In EVs, achieving low error in steady-state and transient responses is crucial for smooth acceleration at the wheel. This paper presents the design and control of a BLDC motor for speed control during acceleration and deceleration, considering error as a key factor in the MATLAB/Simulink environment. Proportional-integral (PI) and fuzzy controllers are commonly used for motor control to improve steady-state and transient performance, thereby reducing error. In this study, the PI and adaptive neuro-fuzzy inference system (ANFIS) controllers are designed and compared for a 5-kW, 48-V, and 100-Amp BLDC motor in EV applications. The results demonstrate that the ANFIS controller enhances the dynamic performance of the BLDC motor and improves other operating characteristics such as rise time, settling time, peak overshoot percentage and the vehicle response in terms of speed and distance.

Observer-Based Nonlinear Proportional–Integral–Integral Speed Control for Servo Drive Applications via Order Reduction Technique

This study designs an advanced single-loop output feedback system for speed servo drive applications, in which a simple proportional–integral–integral (PII) controller equipped with nonlinear feedback and feed-forward gains is formed. The resultant feedback system shows the desired critically damped performance for wide-operating regions by actively handling the system parameter and load uncertainties. There are three contributions: first, the third-order observer estimates, independent from the system model, where the speed and acceleration are obtained using the position measurement with the order reduction property; second, the observer-based PII controller is compensated by active damping with a nonlinearly structured feedback and feed-forward gains; and, third, a guarantee is achieved on the desired critically damped performance through a closed-loop analysis. A hardware testbed that adopts a 500 W brushless DC motor is used to experimentally demonstrate performance improvements over certain constant torque regions under various scenarios.

Sensorless speed and position control of permanent magnet BLDC motor using particle swarm optimization and ANFIS

This paper describes the operation of a Permanent Magnet Brushless Direct Current (PMBLDC) motor without a position sensor. In this case, the sensorless operation is enhanced by an effective hybrid technique that detects the back electromotive force (Back EMF) of the zero crossing point (ZCP) from the terminal voltages. The Adaptive Neuro Fuzzy Interference System (ANFIS) controller, which is based on Particle Swarm Optimization (PSO) and uses PSO to train its operation, is combined in the proposed hybrid analysis. The PMBLDC motor's ANFIS controller receives the line voltages as input, and it uses this information to estimate the sample signals that are then sent to the ZCP detection circuit. Appropriate commutation control of the inverter is generated by the ZCP detecting circuit. By varying the ANFIS consequent parameters, the PSO algorithm iterates until the error between the sample output and the real training data reaches a low value. The MATLAB/Simulink platform is utilized to implement the suggested sensorless controller action. To verify the controller's performance, a comparison with the other soft computing methods is also carried out.

Analyzing E-Bike Driving Conditions on Indian Roads Using Two Different Drive Cycles

Recent technological strides have caused electric vehicles (EVs) to reshape the transportation landscape, boasting environmental advantages alongside positive impacts on human health. Crafting electric vehicles involves intricate motor and battery considerations due to multifaceted design requisites. This study leverages the MATLAB Simulink platform to model an electric bike equipped with a 2 kW Brushless DC motor and a 48 V, 50 Ah battery. The comprehensive model encapsulates the battery, driver controller, power converter, motor, and vehicle subsystems, with a dynamic drive cycle-based representation. An H-bridge converter is adopted to engineer the power converter. The efficacy of the EV is assessed through the Indian driving cycle and the World Harmonized Test Cycle, gauging motor speed and power output. Additionally, the State of Charge (SoC) and Battery Current consumption parameters are quantified for both test drive cycles. This endeavor unveils a meticulous exploration of EV dynamics, motor efficiency, and battery performance, reinforcing the potential of EVs as sustainable and health-beneficial mobility solutions.

Autotuning of PID controller using the modified relay feedback test and optimization under uncertainty principle for robotic manipulators

AbstractThe development of an autotuning mechanism for robotic manipulators, inspired by the success of autotuners in industrial systems, aims to reduce the time and cost of tuning robotic manipulators. This paper introduces an auto‐tuning method for PID controllers in robotic manipulators using the Modified Relay Feedback Test (MRFT) and the Optimization Under Uncertainty (OUU) principle. The mechanism is versatile, suitable for manipulators of varying scales and motor power capacities. It incorporates actuator dynamics and a combined model of Brushless DC motors with an n‐link manipulator for TR development. Various manipulator models, including commercial and open‐source types were used to refine and test the TR. An algorithm is also proposed for tuning commercial manipulators' controllers without changing the existing closed‐loop system structure. Experimental and simulation tests demonstrate the method's effectiveness, showing better performance than traditional approaches, particularly in reducing overshoot and settling time.

An examination of the impact of the selected error criterion on the ACO performance in BLDC drives

Brushless direct current (BLDC) motor drives are becoming increasingly popular in motion control applications. As a result, a low-cost but effective BLDC motor speed controller is required. They are used in industrial and home appliances with conventional motor drive technology, such as robots, refrigerators and air conditioning systems. Brushless direct current (BLDC) drives are well known for their high efficiency and low maintenance. This work aims to design a non-conventional PID controller via Ant Colony Optimisers (ACO) in order to obtain an efficient speed performance for a BLDC motor "namely, Maxon EC flat". Moreover, when ACO is used for tuning of PID control, the objective function is required. Therefore, in this work, four different objective functions which are MSE, ISE, IAE, and ITAE are applied and their performance was compared in terms of motor' speed stability. In a simulation environment, the PID controller is used to control the speed of BLDC motors under three testing conditions which are Small, medium, and high inertia loads. The simulation results demonstrate that MSE offers the best performance under low and high inertial loading cases. Whereas, IAE has been superior compared with other criterions when medium loads are applied. Moreover, this paper shows a stability speed- power consumption trade-off problem. In other words, achieving a fast stability via any type of error indices doesn’t guarantee a low amount of consuming energy.

Using Multi-objective Optimization and Finite Element Method to Reduce Cogging Torque in a Brushless DC Motor

A significant issue in the design of the Brushless Direct Current (BLDC) motors is the cogging torque reduction that leads to negative effects on the BLDC motor's performance such as vibration and noise. However, most methods proposed to reduce cogging torque influence the output torque. Hence, this research aims to reduce cogging torque without having a significant effect on the output torque. For achieving the desired aim which is minimizing the cogging torque concerning the value of output torque, multi-objective optimization is a reliable approach. In this paper, some well-known multi-objective optimization including Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Simulated Annealing (MOSA), and Multi-Objective Red Deer Algorithm (MORDA), are employed to obtain the optimal design of a BLDC motor. In all used optimization algorithms, Simulation results are satisfying and display a significant reduction in the cogging torque, as well as the output torque increases.

Fault diagnosis of brushless DC motor based on Stack Sparse Autoencoder

Because of their simple structure, long service life, high efficiency, etc., brushless DC (BLDC) motors have been widely applied in many fields. In some applications, high requirements for BLDC continuous use of motors, so often to BLDC running state monitoring of motors, realizes the early fault diagnosis, to improve the reliability, safety, and prolonged use. To solve this problem, a BLDC fault diagnosis method based on Fast Fourier Transform (FFT), Stacked Sparse Auto-encoder (SSAE), and soft classifier was proposed. In the laboratory, a BLDC model R-3525 was used as the experimental equipment, and the data of 9 kinds of fault conditions such as bearing damage, cage damage, inner and outer ring damage, and the normal condition of the motor were collected. The collected data are transformed into 28 × 28 two-dimensional data sets by FFT. The feature expressions of various faults are adaptively learned from many data, and the intelligent diagnosis of the motor is realized by the feature function expression. The experimental results demonstrate that, in comparison to the single neural network approach, the stacked method can significantly enhance the precision of fault classification and achieve fault diagnosis for brushless DC motors. This method carries valuable implications for other varieties of BLDC motors.

Analysis of interior rotor for high-speed brushless DC motor using finite element method

<span lang="EN-US">The permanent magnet of brushless DC (BLDC) motor is ideally suited for high-speed application due to its superior performance and efficient as compared to other types of electrical motors. However, to operate in high-speed operation, the rotors that hold the magnet must be able to withstand high centrifugal force. Due to that, the main objective of this project is to design and develop the suitable rotor for high-speed BLDC motor through the finite element method (FEM) Ansys Maxwell modeling software. In addition, this project is also carried out to determine the ability of the modeled software to reach the high-speed performances in terms of cogging torque and induce voltage. In this project, the selected BLDC motor was fabricated and measured experimentally. The result showed that the designed rotor can achieve speed of almost 14,500 rpm.</span>

PID Controller Design of Brushless DC Motor by Bacteria Foraging and Hybrid Bacteria Foraging-PSO Optimization

Brushless DC motors are widely used in industries incomparable to DC motors. In this research work transfer function is obtained for brushless DC motor for designing controller gains. The performance is analysed by with and without controller. The design of controller gains is done by bacterial foraging and hybrid bacterial PSO optimization algorithm. The simulation was done by MATLAB /SIMULINK platform and the results are compared for with and without controller. The settling time, steady state error and peak overshoot is better when compared to without controller. Key Words: PID control, optimization, brushless dc motor, peak over shoot, settling time

Controlling the significance of BLDC motor internal faults using dual examine algorithm in electric vehicle applications

<span lang="EN-US">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.</span>

Analysis of Inter-Turn Short-Circuit Faults in Brushless DC Motors Based on Magnetic Leakage Flux and Back Propagation Neural Network

Inter-turn short-circuit (ITSC) faults are the most common fault types of brushless DC (BLDC) motors used in industry. Fault diagnosis of BLDC motors is of critical importance due to their wide spread applications. Existing diagnosis methods are based on voltage and current analysis which is useful but difficult to identify early faults and fault locations, as these suffer from external disturbances. This paper proposes a new offline method for fault detection based on magnetic leakage flux (MLF) and backpropagation neural network (BPNN) for improving the level of fault diagnosis. The ITSC and MLF are modeled and analyzed theoretically. Then they are verified by finite element analysis (FEM) and experimental tests. Hall sensors are used to form an array to collect MLF signals at different positions outside the test motor. The frequency-domain characteristic matrix of MLF signals is analyzed by BPNN models. The experimental results show that the proposed method can effectively detect ITSCs, and estimate the fault degree and the location of the fault. The method is a promising technology as it is non-intrusive and accurate.

A Fuel-Efficient BLDC Motor-Driven Light Electric Vehicle With Single-Stage Onboard Charging System

This work proposed a simple, compact, and efficient scheme for an on-board charging configuration for light plug-in electric vehicles (LPEVs) with a dual charging arrangement i.e., utility grid and solar panel. The system is targeted to develop an LPEV with a low DC bus voltage (<100V) and on-board solar support using a brushless dc motor (BLDCM) drive due to its economic controller, compact drive arrangement, and high efficiency. The designed integrated converter works well under all operating modes like charging, propulsion (PP), and regenerative braking (RB) operations. The developed converter works as a power factor correction (PFC) converter during grid charging as well as performs as maximum power point tracking (MPPT) converter when charging through solar power. A new approach of the controller in regenerative mode exploiting both the drive and proposed integrated control is unique for this system. As a result, the designed system becomes compact and efficient, making it a competitive solution for an on-board charging scheme for LEVs.

A new brushless DC motor driving resonant pole inverter optimized for batteries

&lt;span lang="EN-US"&gt;The brushless DC motor (BLDC) has gained significant popularity in industrial settings due to its notable attributes such as low inertia, rapid response, high power density, exceptional dependability, and reputation for being conservation-free. Typically, these are equipped by a tight-switching PWM inverter, which results in significant switching losses. Consequently, the dissipation of switching loss necessitates the use of sizable heat sinks, resulting in an increase in both the physical dimensions and mass of the drive system. Numerous researchers have developed soft switching inverters with the aim of minimizing switching losses. The utilization of a soft-switching circuit may give rise to additional issues, including heightened voltage stress, incomplete pulse width modulation control, and intricate control scheme or implementation. The present study introduces a basic soft switch inverter design that is suitable for employment in BLDC drive systems powered by batteries. The inverter exhibits low loss for power switching and voltage stress is less on the main switches, while also featuring a straightforward control scheme that is easily implementable. Upon conducting analytical analysis, simulation results were presented by evaluating the theoretical analysis.&lt;/span&gt;

Determination of performance characteristics using FEA-analytical for outer rotor BLDC motor

This paper presents an analytical approach to determine the performance characteristics of an outer rotor Brushless DC (BLDC) motor using finite element analysis (FEA). An accurate performance characteristic is a crucial deliverable during the early motor design process in order to minimize the cost of change while delivering a feasible design to the application. There are several popular methods to determine motor performance, including template-based software as well as FEA. However, these two methods have limitations in terms of geometry and time. Therefore, this paper proposes that the limitations can be addressed by using a combination of FEA and mathematical equations. First, the optimum motor design is determined using an FEA analytical approach. Second, the torque constant &lt;em&gt;K&lt;/em&gt;&lt;sub&gt;T&lt;/sub&gt;, is derived from the FEA results and followed by performance curve generation using the mathematical equation of speed, current, and output power in terms of torque function. Finally, the outcome results are verified against the motor requirements, including an assessment of maximum speed, stall current, and maximum output power. In conclusion, this research introduces a methodology for determining full curve motor performance prior to prototype fabrication. The leverage benefit could be gained through robust design and low-cost development.

Hybrid quadratic DC-DC boost converter for fuel cell-powered electric vehicle with wide voltage gain and low voltage stress

The need to cut carbon emissions and the worry about global warming will be two of the major issues of the twenty-first century. Globally, academics are researching renewable energy sources, with the majority of their efforts focused on the converter side, which is critical to contemporary society. For energy conversion, fuel cells provide an alternative to internal combustion engines. Despite the fact that typical DC-to-DC converters experience high stress and minimal voltage rise, the bulk of research focuses on voltage lift. Researchers from all around the globe use DC-DC converters to control the voltage of fuel cells. If the targets are fulfilled, more switches will be placed, resulting in higher losses. The boost-cuk converter in this research has a high voltage gain and low switching strain. Initially, this contributed to the development of self-control. Switching losses are reduced when just one switch is used. A DC-DC converter is employed in this research to transform the electricity provided by fuel cells. The alternating supply is then sent to the brushless direct current (BLDC) motor by the PWM inverter. The work starts by building MATLAB simulation blocks. Finally, Simulink is used to display the simulated output waveforms of the intended converter.

Load Torque Observer for BLDC Motors Based on a HOSM Differentiator

An observer is proposed for a trapezoidal brushless DC motor composed of a cascade connection of a reduced-order Luenberger observer and a high-order sliding mode (HOSM) differentiator. This configuration can estimate the angular velocity and reconstruct the load torque, key elements for the control of this type of motor, under the mild assumption that the variable load torque and its k-th time derivatives are bounded. The proposed observer was tested on an experimental test bench based on Texas Instruments (TI) High Voltage Digital Motor Control (HVMTR Kit) using a Delfino F28379D micro controller. The results show that the velocity and load torque can be properly estimated, despite the presence of noise in the current measurements.

Investigation of Various Laminating Materials for Interior Permanent Magnet Brushless DC Motor for Cooling Fan Application

Investigation of Various Laminating Materials for Interior Permanent Magnet Brushless DC Motor for Cooling Fan Application

Performance Analysis Of DSMST Converter FED BLDC Motor

A positive output voltage SEPIC-type DC-DC converter called DSMST (modified SEPIC topology) without a transformer for use in electric vehicles. The DSMST converter is derived from the traditional SEPIC converter with the addition of a switching cell. Furthermore, the output diode and inductors are interchanged for the continuous output current. In the realm of electric vehicles, the utilization of Brushless DC (BLDC) motor drives coupled with a DC-DC converter has become increasingly important. Specifically in the context of electric vehicles, the use of BLDC motors offers several technical advantages compared to other types of motors such as DC, induction, and switched reluctance motors. The DSMST converter is connected to the BLDC motor through a three-phase voltage source inverter (VSI) to regulate the voltage and current supplied to the BLDC motor, ensuring optimal performance. To regulate the speed of drive, a closed loop control system is developed using hall sensor signals. The proposed system is developed in the MATLAB/Simulink to check the applicability of electric vehicle configuration and the results shown that the proposed converter is well suitable for electric vehicle applications.

Decoupling control of bearingless brushless DC motor using particle swarm optimized neural network inverse system

Neural network inverse system decoupling control can effectively solve the nonlinear strong coupling problem of bearingless brushless DC motor, but it has the problem of slow convergence and easy to fall into local extreme value. An Improved Particle Swarm Optimization (IPSO) algorithm has been employed to optimize the initial weights of the BP neural network inverse system. Comparisons between traditional inverse system decoupling control and the proposed method through simulations were conducted to confirm the efficacy and superiority of the proposed decoupling strategy. Furthermore, experiment research indicates that effective decoupling control can be achieved for both speed and radial displacement, as well as between the x and y-axis radial displacements, showcasing the good dynamic decoupling performance and stability of the proposed decoupling control strategy.