Search Results (229)

Brushless DC (BLDC) motors are increasingly used in industrial applications due to their high efficiency, reliability, and low weight. However, their performance strongly depends on the electromagnetic properties of stator and rotor core materials. This study evaluates six BLDC motor configurations, employing materials such as M19 electrical steel, 1010 low-carbon steel, magnetic PLA, and ABS, and analyzes their impact using FEMM 4.2 finite element simulations. Key electromagnetic characteristics—including flux linkage, Back-EMF, torque, and torque ripple—were compared across configurations. The reference motor with M19 steel stator and 1010 steel rotor achieved ~7 mWb flux linkage, ~39 V pk–pk Back-EMF, and 1.44 Nm torque with ~49% ripple, confirming the suitability of laminated steels for high-power-density designs. Substituting M19 with 1010 steel in the stator reduced torque by less than 10%, indicating material interchangeability with minimal performance loss. By contrast, polymer-based designs exhibited drastic degradation: magnetic PLA yielded only 3.5% of the baseline torque with sixfold ripple increase, while ABS delivered nearly zero torque and >700% ripple. Hybrid configurations improved PLA-based results by 15–20%, though they remained far below ferromagnetic cores. Overall, results demonstrate a nearly linear relationship between material permeability and both flux linkage and Back-EMF, alongside a sharp rise in torque ripple at low permeability. The findings highlight the advantages of ferromagnetic and laminated steel cores for efficiency and stability, while polymer and hybrid cores are limited to lightweight demonstrator applications. Full article

The increasing use of UAVs has reshaped urban logistics, enabling sustainable alternatives to traditional deliveries. To address critical issues inherent in the system, the proposed study presents the design and evaluation of an innovative unmanned aerial vehicle (UAV) prototype that integrates advanced electronic components and artificial intelligence (AI), with the aim of reducing environmental impact and enabling autonomous navigation in complex urban environments. The UAV platform incorporates brushless DC motors, high-density LiPo batteries and perovskite solar cells to improve energy efficiency and increase flight range. The Deep Q-Network (DQN) allocates energy and selects reference points in the presence of wind and payload disturbances, while an integrated sensor system monitors motor vibration/temperature and charge status to prevent failures. In urban canyon and field scenarios (wind from 0 to 8 m/s; payload from 0.35 to 0.55 kg), the system reduces energy consumption by up to 18%, increases area coverage by 12% for the same charge, and maintains structural safety factors > 1.5 under gust loading. The approach combines sustainable materials, efficient propulsion, and real-time AI-based navigation for energy-conscious flight planning. A hybrid methodology, combining experimental design principles with finite-element-based structural modelling and AI-enhanced monitoring, has been applied to ensure structural health awareness. The study implements proven edge-AI sensor fusion architectures, balancing portability and telemonitoring with an integrated low-power design. The results confirm a reduction in energy consumption and CO₂ emissions compared to traditional delivery vehicles, confirming that the proposed system represents a scalable and intelligent solution for last-mile delivery, contributing to climate resilience and urban sustainability. The findings position the proposed UAV as a scalable reference model for integrating AI-driven navigation and renewable energy systems in sustainable logistics. Full article

Brushless DC (BLDC) motors are commonly used in electric vehicles (EVs) because of their efficiency, small size and great torque-speed performance. These motors have a few benefits such as low maintenance, increased reliability and power density. Nevertheless, BLDC motors are highly nonlinear and their dynamics are very complicated, in particular, under changing load and supply conditions. The above features require the design of strong and adaptable control methods that can ensure performance over a broad spectrum of disturbances and uncertainties. In order to overcome these issues, this paper uses a Fractional-Order Proportional-Integral-Derivative (FOPID) controller that offers better control precision, better frequency response, and an extra degree of freedom in tuning by using non-integer order terms. Although it has the benefits, there are three primary drawbacks: (i) it is not real-time adaptable, (ii) it is hard to choose appropriate initial gain values, and (iii) it is sensitive to big disturbances and parameter changes. A new control framework is suggested to address these problems. First, a Reinforcement Learning (RL) approach based on Deep Deterministic Policy Gradient (DDPG) is presented to optimize the FOPID gains online so that the controller can adjust itself continuously to the variations in the system. Second, Snake Optimization (SO) algorithm is used in fine-tuning of the FOPID parameters at the initial stages to guarantee stable convergence. Lastly, cascade control structure is adopted, where FOPID controllers are used in the inner (current) and outer (speed) loops. This construction adds robustness to the system as a whole and minimizes the effect of disturbances on the performance. In addition, the cascade design also allows more coordinated and smooth control actions thus reducing stress on the power electronic switches, which reduces switching losses and the overall efficiency of the drive system. The suggested RL-enhanced cascade FOPID controller is verified by Hardware-in-the-Loop (HIL) testing, which shows better performance in the aspects of speed regulation, robustness, and adaptability to realistic conditions of operation in EV applications. Full article

As electric scooters (e-scooters) continue to populate city streets and gain popularity as a key mode of micro-mobility, issues such as their energy consumption and demand from the power grid, as well as optimizing their electrical systems, become increasingly important. Improving performance requires a deep understanding of their electrical behavior and the design of smart control strategies. This paper presents a detailed analysis of the entire electrical system of commercial electric scooters, with a particular focus on the performance of key components such as the permanent magnet brushless direct current motor and the lithium-ion battery system. The study involves modeling and simulation of motor control, battery management, and DC-link voltage stabilization using MATLAB/Simulink. The simulations are complemented by laboratory measurements of the motor performance in an SXT Scooters MAX unit under various operating conditions. Additionally, a complete battery charging cycle is analyzed to evaluate charging characteristics and usable energy storage capacity. This paper presents a first step for researchers interested in studying the electrical systems of e-scooters. Additionally, it can serve as educational material for electrical engineers in the field of e-scooters. Full article

Electric vehicles are playing an important role in transport, aided by rapid advances in battery technology. The Faculty of Engineering at the University of Debrecen is also engaged research and development in the field of electric vehicles. To support the development of electric vehicle prototypes, a vehicle dynamics simulation program has been designed. The study presents the modeling and simulation of a permanent magnet synchronous motor (PMSM) in MATLAB/Simulink, which has been integrated into the existing vehicle dynamics simulation framework. The methods used to determine the motor characteristics required for the simulation are described in detail. In addition, the performance of the PMSM is compared with that of a brushless DC (BLDC) motor within the vehicle dynamics simulation program. The developed method allows the selection of the appropriate motor type for the given competition tasks. Full article

This paper presents a novel speed controller design for a brushless DC motor (BLDCM) operating under field-oriented control (FOC). The proposed speed controller is developed by integrating the zebra optimization algorithm (ZOA) with sliding mode theory (SMT). In this approach, the parameter ranges of the sliding mode dynamic trajectory control gain, exponential reaching gain, and constant speed reaching gain—three key components of the exponential reaching law-based sliding mode controller (ERLSMC)—are defined as the research space for the ZOA. The feedback speed error and its rate of change are used as features to calculate the fitness value. Subsequently, the fitness value computed by the algorithm is compared with the current best fitness value to determine the optimal position coordinates. These coordinates correspond to the optimal set of gain parameters for the sliding mode speed controller. During the operation of the BLDCM, these optimized parameters are applied to the controller in real time. This enables the system to adjust the three gain parameters dynamically under different operating conditions, thereby reducing the overshoot commonly induced by the ERLSMC. As a result, the speed response of the BLDCM drive system can more accurately and rapidly track the speed command. Therefore, the proposed control strategy is not only characterized by a small number of parameters and ease of tuning, but also does not require large datasets for training, making it highly practical and easy to implement. Finally, the proposed control strategy is simulated using Matlab/Simulink (2024b version) and applied to the BLDCM drive system for experimental testing. Its performance is compared against three types of sliding mode controllers employing different reaching laws: the constant speed reaching law, the exponential reaching law, and the exponential reaching law combined with extension theory (ET). Simulation and experimental results confirm that the proposed novel speed controller outperforms the other three sliding mode controllers based on different reaching laws, both in terms of speed command tracking and load regulation response. Full article

Accurate modeling and performance analysis of brushless DC (BLDC) motors are essential for high-efficiency control in modern drive systems. In this article, a BLDC motor was modeled using system identification techniques. In addition, experimental data were collected from the BLDC motor, including its speed response to various input signals. Using system identification tools, particularly those provided by MATLAB/Simulink R2024b, an approximation model of the BLDC motor was constructed to represent the motor’s dynamic behavior. The identified model was experimentally validated using various input signals, demonstrating its accuracy and generalizability under different operating conditions. Additionally, a series of mechanical load tests was conducted using the AVL eddy-current dynamometer to evaluate performance under practical operating conditions. Fixed load torques were applied across a range of motor speeds, and multiple torque levels were tested to assess the motor’s dynamic response. Electrical power, mechanical power, and efficiency of the entire system were computed for each case to assess overall system performance. Moreover, the real-time state of charge (SOC) of Lithium-ion (Li-ion) battery was estimated using the Coulomb counting method to analyze the impact of Li-ion battery energy level on the BLDC motor efficiency. The study offers valuable insights into the motor’s dynamic and energetic behavior, forming a foundation for robust control design and real-time application development. Full article

Multi-phase brushless DC motors (BLDCMs) have broad prospects in the power propulsion systems of electric vehicles, submarines, electric ships, etc., due to their advantages of high efficiency and high power density. In the above application scenarios, accurately obtaining the rotor position information is crucial for ensuring the efficient and stable operation of multi-phase BLDCMs. Therefore, by analyzing the fault conditions of position sensors in this paper, a fault diagnosis method for position sensors in multi-phase brushless DC motor drive systems based on position signals and fault current characteristics is proposed, with the aim of improving the reliability of the system. This method utilizes the Hall state value determined by the Hall position signal and the current characteristics under the fault state to achieve rapid fault diagnosis and precise positioning of the position sensor. Its advantage lies in the fact that it does not require additional hardware support or complex calculations, and can efficiently identify the fault conditions of position sensors. To verify the effectiveness of the proposed method, this paper conducts experiments based on a nine-phase brushless DC motor equipped with nine Hall position sensors. The results of steady-state and dynamic experiments show that this method can achieve rapid fault diagnosis and location. Full article

This paper presents a novel methodology for minimizing power losses in brushless DC (BLDC) motors through the implementation of adaptive flux control techniques. Conventional motor control strategies, such as direct torque control (DTC), typically employ fixed flux values, which often result in suboptimal performance, particularly under dynamic load and speed variations. To mitigate this inherent limitation, two adaptive flux control methods are introduced: incremental conductance (IncCond) and fuzzy logic. These proposed strategies facilitate real-time dynamic adjustment of the stator flux, thereby optimizing motor performance and significantly enhancing system efficiency. Experimental validation confirms the efficacy of these adaptive techniques, demonstrating substantial improvements in power loss reduction and overall efficiency when compared to traditional fixed flux control strategies. Notably, the fuzzy logic control strategy achieves the highest efficiency, registering a system efficiency of $66.59$%, which surpasses both the incremental conductance method and conventional fixed flux control. These findings underscore the considerable potential of adaptive flux control in applications where energy efficiency is paramount, including electric vehicles and renewable energy-driven systems. Full article

Unmanned Aerial Vehicles have started to be used more and more due to the benefits they bring. Failure of Unmanned Aerial Vehicle components may result in loss of control, which may cause property damage or personal injury. In order to increase the operational safety of the Unmanned Aerial Vehicle, the implementation of a Predictive Maintenance system using the Internet of Things is required. In this paper, the authors propose a new architecture of Predictive Maintenance system for Unmanned Aerial Vehicles that is able to identify the fault type of Brushless DC electric motor and determine the Remaining Useful Life of the Li-ion batteries. In order to create the Predictive Maintenance system within the Unmanned Aerial Vehicle, an architecture based on Fog Computing was proposed and Machine Learning was used to extract knowledge from the data. The proposed architecture was practically validated. Full article

To improve commutation accuracy and effectively suppress torque ripple in brushless DC motors (BLDCMs), this paper presents a novel commutation correction strategy integrated into an enhanced direct torque control (DTC) framework. The proposed method estimates the commutation angle error in real time by analyzing the integral difference in phase currents across adjacent 30° conduction intervals, enabling dynamic and accurate commutation correction. This correction mechanism is seamlessly embedded into a modified DTC algorithm that employs a three-level torque hysteresis comparator and introduces a novel zero-voltage vector selection strategy to minimize torque ripple. Compared with conventional DTC approaches employing dual-loop control and standard zero vectors, the proposed method achieves up to a 58% reduction in torque ripple along with improved commutation precision, as demonstrated through both simulation and experimental validation. These results confirm the method’s effectiveness and its potential for application in high-performance BLDCMs drive systems. Full article

This paper presents a structured and experimentally validated approach to the parameter identification, modeling, and real-time speed control of a brushless DC (BLDC) motor. Electrical parameters, including resistance and inductance, were measured through DC and AC testing under controlled conditions, respectively, while mechanical and electromagnetic parameters such as the back electromotive force (EMF) constant and rotor inertia were determined experimentally using an AVL dynamometer. The back EMF was obtained by operating the motor as a generator under varying speeds, and inertia was identified using a deceleration method based on the relationship between angular acceleration and torque. The identified parameters were used to construct a transfer function model of the motor, which was implemented in MATLAB/Simulink R2024b and validated against real-time experimental data using sinusoidal and exponential input signals. The comparison between simulated and measured speed responses showed strong agreement, confirming the accuracy of the model. A proportional–integral (PI) controller was developed and implemented for speed regulation, using a low-cost National Instruments (NI) USB-6009 data acquisition (DAQ) and a Kelly controller. A first-order low-pass filter was integrated into the control loop to suppress high-frequency disturbances and improve transient performance. Experimental tests using a stepwise reference speed profile demonstrated accurate tracking, minimal overshoot, and robust operation. Although the modeling and control techniques applied are well known, the novelty of this work lies in its integration of experimental parameter identification, real-time validation, and practical hardware implementation within a unified and replicable framework. This approach provides a solid foundation for further studies involving more advanced or adaptive control strategies for BLDC motors. Full article

This study presents a novel approach to the end-of-line (EoL) quality inspection of brushless DC (BLDC) motors by implementing a deep learning model that combines MEL diagrams, convolutional neural networks (CNNs) and bidirectional gated recurrent units (BiGRUs). The suggested system utilizes raw vibration and sound signals, recorded during the EoL quality inspection process at the end of an industrial manufacturing line. Recorded signals are transformed directly into Mel-frequency spectrograms (MFS) without pre-processing. To remove non-informative frequency bands and increase data relevance, a six-step data reduction procedure was implemented. Furthermore, to improve fault characterization, a reference spectrogram was generated from healthy motors. The neural network was trained on a highly imbalanced dataset, using oversampling and Bayesian hyperparameter optimization. The final classification algorithm achieved classification metrics with high accuracy (99%). Traditional EoL inspection methods often rely on threshold-based criteria and expert analysis, which can be inconsistent, time-consuming, and poorly scalable. These methods struggle to detect complex or subtle patterns associated with early-stage faults. The proposed approach addresses these issues by learning discriminative patterns directly from raw sensor data and automating the classification process. The results confirm that this approach can reduce the need for human expert engagement during commissioning, eliminate redundant inspection steps, and improve fault detection consistency, offering significant production efficiency gains. Full article

Brushless DC (BLDC) motors are widely used in applications such as Electric Vehicles (EVs) due to their high efficiency, low maintenance, and favorable torque-to-mass ratio. However, one major challenge in BLDC motors is the presence of current harmonics, which can lead to increased noise, vibration, and reduced efficiency, particularly at low speeds or light loads. These harmonics primarily arise from abrupt current transitions during phase commutation. To address this, thispaper presents an innovative approach that combines the Proposed Sigmoid Trapezoidal Current Model with hysteresis control to reduce current harmonics. The model facilitates smooth current changes by applying a sigmoid function, replacing sharp transitions with gradual ones, thus significantly minimizing harmonic distortion. Additionally, hysteresis PWM control enhances the system by precisely regulating the current and dynamically adjusting the switching frequency to maintain the current within a defined range. Simulation results confirm the effectiveness of this method, showing substantial reductions in current harmonics, speed ripple, and torque ripple. Specifically, the proposed method reduces torque ripple by 81% compared to traditional Electronic Commutation Control and improves torque ripple by 30% compared to the conventional method. Full article

Brushless DC motors are often used as traction motors in electric vehicles due to their high power density and efficiency. The dc-link electrolytic capacitor is the most vulnerable part of the brushless DC motor drive system, and it determines the reliability of the motor drive system. Therefore, it is of great importance to monitor the life of the dc-link electrolytic capacitor in the drive system. To carry out the lifetime monitoring of capacitors, a dc-link series switch circuit composed of diodes and power switching devices is introduced to calculate the capacitance value. The lifetime of the capacitor is then monitored in real time through this capacitance value. During normal steady-state operation of the motor, the control strategy of the inverter is switched. When the dc-link switch is turned off, the charging vector is used to charge the dc-link capacitor. Due to the presence of the diode and the dc-link switch, the energy charged to the dc-link by the motor can only flow into the capacitor and cannot be released immediately. Therefore, the capacitance value is calculated through the change in capacitor voltage and the capacitor current reconstructed from the three-phase currents of the motor. The feasibility of the method proposed in this paper is experimentally verified by building a brushless DC motor system. Full article