Browsing by Author "Abouseda, Ayman Ibrahim"

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Modeling, Dynamic Characterization, and Performance Analysis of a 2.2 kW BLDC Motor Under Fixed Load Torque Levels and Variable Speed Inputs: An Experimental Study
(MDPI, 2025) Abouseda, Ayman Ibrahim; Doruk, Resat; Emin, Ali; Akdeniz, Ozgur; Automotive Engineering
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.
Parameter Identification and Speed Control of a Small-Scale BLDC Motor: Experimental Validation and Real-Time PI Control with Low-Pass Filtering
(MDPI, 2025) Abouseda, Ayman Ibrahim; Doruk, Resat Ozgur; Amini, Ali; Electrical-Electronics Engineering
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.