Ahmet Orhan

This study proposes a Type‐3 fuzzy proportional–integral (T3‐FPI) controller for robust DC‐bus voltage regulation in a grid‐connected hybrid microgrid integrating photovoltaic and wind generation, a lithium‐ion battery, a proton exchange membrane fuel cell (PEMFC), and the utility grid. The controller performs online fuzzy gain scheduling of and according to the DC‐bus voltage error and its rate of change, while a rule‐based energy‐management strategy coordinates battery, grid, and PEMFC operation. For a fair assessment, T3‐FPI is compared with a reproducibly retuned PI, a Type‐1 fuzzy PI (T1‐FPI), and an interval Type‐2 fuzzy PI (T2‐FPI) under identical operating and tuning conditions. In the controller benchmark, the DC‐bus voltage returns to and remains in the vicinity of the 500‐V reference following the imposed load disturbance, confirming stable post‐disturbance regulation. Relative to the retuned PI, T3‐FPI reduces the post‐step peak DC‐bus voltage deviation by approximately 10.40% and 10.93% in Scenarios 1 and 2, respectively, while the corresponding post‐step RMSE reductions are approximately 7.74% and 7.54%. The extended severe‐uncertainty assessment further shows that T3‐FPI does not universally outperform T1‐FPI and T2‐FPI in every scalar voltage‐error metric. Instead, it maintains closely comparable voltage‐regulation performance while exhibiting smoother bounded adaptive‐gain trajectories. In a representative severe PV‐loss stress case, the total variation of decreases from 9.867 and 9.868 for T1‐FPI and T2‐FPI to 9.491 for T3‐FPI, while the corresponding total variation decreases from 10.812 and 10.806 to 10.498. A scan of the complete bounded T3‐FPI gain domain confirms local stability for all 961 evaluated – combinations, with a worst‐case maximum real eigenvalue of . The full‐microgrid results additionally demonstrate coordinated power sharing, conditional PEMFC support, balanced three‐phase operation, and adequate converter‐capacity margin under dynamic loading. These results position the proposed T3‐FPI primarily as a bounded and smoother adaptive gain‐scheduling solution rather than as a controller that is universally superior to lower‐order fuzzy alternatives in every individual voltage‐error metric.

The rising need for high efficiency and precise control has intensified research on wound rotor synchronous motors (WRSMs). Accurate torque estimation is crucial for performance optimization, energy efficiency, and reliable operation. However, traditional physics-based models often fail to capture nonlinearities such as magnetic saturation, geometric dependencies, and parameter variations, leading to errors under real conditions. To address these challenges, this study proposes a data-driven hybrid modeling approach combining physical motor parameters with machine learning algorithms. The dataset was generated through finite element analysis (FEA) simulations of a 6 kW WRSM producing 40 Nm nominal torque. It includes around 2000 geometric and electromagnetic scenarios, each subjected to thorough preprocessing. Three regression models—support vector regression (SVR), random forest, and extreme gradient boosting (XGBoost)—were used for torque prediction. Instead of standard cross-validation, a “Hold-Out Geometry Test” involving an unseen motor geometry evaluated each model’s generalization capability and robustness against overfitting. Results show that the SVR model achieved the best performance with a coefficient of determination R2 = 0.9950, mean absolute error 0.071 Nm, and mean absolute percentage error 0.178%. Shapley Additive Explanations–based interpretability analysis confirmed that the model’s decision process aligns with physical motor behavior. The proposed approach offers high accuracy, interpretability, and strong generalization for predicting torque in WRSMs, even for unseen geometries, while greatly reducing computation time compared to traditional FEA-based analyses. Overall, this study establishes a robust foundation for real-time performance prediction and intelligent control of WRSMs. Future work will extend the model to dynamic operating conditions and incorporate multiphysical parameters such as temperature and material saturation.

This study presents the design, manufacturing, and prototype-level evaluation of a high-efficiency interior permanent magnet synchronous motor (IPMSM) developed for compact electric vehicle traction applications. The proposed motor employs a 12-slot/10-pole spoke-type rotor topology and was evaluated in terms of electromagnetic performance, mechanical integrity, and thermal behavior. The slot–pole and winding configuration was assessed as part of the design evaluation, and the manufactured prototype was experimentally tested under different operating conditions. The experimental results were compared with numerical simulations using line-to-line back-EMF, efficiency maps, phase current–torque characteristics, and output power variation. At the nominal operating point of 7000 rpm and 3.5 Nm, the prototype delivered 2.5 kW output power with an experimental efficiency of 90.7%. The deviations between experimental and simulation results were 1.17% for phase current, 0.48% for line-to-line back-EMF, 1.18% for input power, and 1.20% for efficiency. Mechanical static structural finite element analysis indicated a rotor safety factor of 3.61 under the maximum centrifugal loading condition, while the resulting structural deformation remained sufficiently low to avoid adverse effects on air-gap alignment. In addition, the rotor incorporated an adhesive-free, mechanically disassemblable magnet-retention structure, which was mechanically evaluated under centrifugal loading and showed no magnet displacement, structural damage, or bolt-preload loss after testing. Thermal analysis and continuous-load experimental testing showed that the winding temperature remained around 80 °C under passive cooling conditions. Overall, the results demonstrate that the manufactured IPMSM prototype provides consistent electromagnetic performance, adequate mechanical reliability, and thermally safe operation for compact electric vehicle applications.

In today’s world, the demand for compact, high-efficiency, and low-cost motors plays a significant role in the design of low-power electric machines. In combi fan applications, single-phase brushless direct current (BLDC) motors are generally preferred. Although these motors offer efficient and compact solutions, the occurrence of dead points at certain rotor positions creates a serious disadvantage that may prevent the motor from initiating motion. In this study, an asymmetric air gap design is proposed for a single-phase BLDC motor to eliminate the dead point problem and increase starting torque. The motor’s performance has been evaluated through analytical calculations and two-dimensional finite element analysis (FEA) conducted using ANSYS Electronics Desktop 2020 R2 (Maxwell) software. The results show that the asymmetric air gap effectively eliminates the dead point and improves the motor’s starting performance. However, torque ripple is still identified as a design parameter that must be considered. The scope of this study is not limited to single-phase BLDC motors; it also provides analytical approaches that can be applied to different electric motor designs, contributing to engineering applications in this field.

Electric vehicles (EVs) have increasingly become central to modern transportation systems due to their advantages in environmental sustainability and energy efficiency. In this development process, electric drive motors play a decisive role in key criteria such as performance, range, energy consumption, and production costs. In this study, three primary motor types commonly used in electric vehicles the Wound Rotor Synchronous Motor (WRSM), Permanent Magnet Synchronous Motor (PMSM), and Induction Motor (IM) are evaluated in a multidimensional manner in terms of their technical performance, energy efficiency, and application segments. Comparative analyses reveal that PMSM motors, in particular, provide an optimal power-to-weight ratio for mid-range vehicles due to their high torque generation and low energy consumption. The compact structure and high efficiency levels of these motors have made them the standard choice for many manufacturers today. On the other hand, WRSM motors offer a strong alternative for long-term sustainability goals by eliminating dependence on rare-earth elements and providing field weakening capability. The high-temperature tolerance, low maintenance requirements, and wide speed range operating capability of WRSM motors indicate their potential for broader adoption in the future. Segment-based evaluations have determined that vehicles in the economic class offer longer range per unit of energy due to lower weight and limited power requirements, representing a significant advantage for compact electric vehicles intended for urban use in terms of energy savings. Conversely, luxury and high-performance segment vehicles demonstrate higher energy consumption despite larger battery capacities; in these segments, factors such as acceleration, traction power, and driving comfort are prioritized over efficiency. The general findings indicate that motor power plays a critical role not only in performance but also in energy consumption, range capacity, and segment-based product positioning. Furthermore, the increasing research and development efforts on alternative motor architectures such as WRSM suggest that these motors may become more attractive both economically and environmentally in the future. In this context, it is recommended that interdisciplinary research on motor technologies be intensified in order to achieve sustainable electric transportation objectives.

The demand for high efficiency and precise control in electric drive systems has led to the widespread adoption of Interior Permanent Magnet Synchronous Motors (IPMSMs). The performance of these motors is significantly influenced by rotor geometry. Traditionally, rotor shape analysis has been conducted using the finite element method (FEM), which involves high computational costs. This study aims to classify the rotor shape (2D type, V type, Nabla type) of IPMSMs using electromagnetic parameters through machine learning-based methods and to demonstrate the applicability of this approach as an alternative to classical methods. In this context, a custom deep learning model, KS-Net, developed by the user, was comparatively evaluated against Cubic SVM, Quadratic SVM, Fine KNN, Cosine KNN, and Fine Tree algorithms. The balanced dataset, consisting of 9,000 samples, was tested using 10-fold cross-validation, and performance metrics such as accuracy, precision, recall, and F1-score were employed. The results indicate that the Cubic SVM and Quadratic SVM algorithms classified all samples flawlessly, achieving 100% accuracy, while the KS-Net model achieved 99.98% accuracy with only two misclassifications, demonstrating competitiveness with classical methods. This study shows that the rotor shape of IPMSMs can be predicted with high accuracy using data-driven approaches, offering a fast and cost-effective alternative to FEM-based analyses. The findings provide a solid foundation for accelerating motor design processes, developing automated rotor identification systems, and enabling data-driven fault diagnosis in engineering applications.

An asymmetric air gap is required for the self-starting capability of BLDC motors; however, this condition causes an increase in cogging torque, which negatively affects motor performance. In this study, a parametric analysis of notches opened on the stator legs was conducted to reduce cogging torque in inner rotor single-phase brushless DC (BLDC) motors. In the finite element analyses performed using Ansys Maxwell 2D software, notches with different positions and geometries were modeled on the stator legs in the air gap region. Within the scope of the parametric studies, the effects of the notches were examined in regions where the air gap is both at its minimum and maximum, and the optimum position was determined to be the region with the minimum air gap. During the production phase, a notched stator was manufactured, and the impact of these notches on the production processes and manufacturability was evaluated. According to the simulation results, the newly created notched stator model achieved approximately a 28.5% reduction in cogging torque.

This paper presents a comparative analysis of bearing and rotating component fault classification based on different time–frequency representations using vision transformer (ViT). Four different time–frequency transformation techniques—short-time Fourier transform (STFT), continuous wavelet transform (CWT), Hilbert–Huang transform (HHT), and Wigner–Ville distribution (WVD)—were applied to convert the signals into 2D images. A pretrained ViT-Base architecture was fine-tuned on the resulting images for classification tasks. The model was evaluated on two separate scenarios: (i) eight-class rotating component fault classification and (ii) four-class bearing fault classification. Importantly, in each task, the samples were collected under varying conditions of the other component (i.e., different rotating conditions in bearing classification and vice versa). This design allowed for an independent assessment of the model’s ability to generalize across fault domains. The experimental results demonstrate that the ViT-based approach achieves high classification performance across various time–frequency representations, highlighting its potential for mechanical fault diagnosis in rotating machinery. Notably, the model achieved higher accuracy in bearing fault classification compared to rotating component faults, suggesting higher sensitivity to bearing-related anomalies.

This study presents a comparative analysis of bearing and rotating component fault classification based on different time–frequency representations using ViT-base model. Four different time–frequency transformation techniques—Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), Hilbert-Huang Transform (HHT), and Wigner-Ville Distribution (WVD)—were applied to convert the signals into 2D images. A pre-trained ViT-Base architecture was fine-tuned on the resulting images for classification tasks. The model was evaluated on two separate scenarios: (i) eight-class rotating component fault classification and (ii) four-class bearing fault classification. Importantly, in each task, the samples were collected under varying conditions of the other component (i.e., different rotating conditions in bearing classification and vice versa). This design allowed for an independent assessment of the model’s ability to generalize across fault domains. The experimental results demonstrate that the ViT-based approach achieves high classification performance across various time–frequency representations, highlighting its potential for mechanical fault diagnosis in rotating machinery. Notably, the model achieved higher accuracy in bearing fault classification compared to rotating component faults, suggesting a higher sensitivity to bearing-related anomalies.

The design and performance analysis of relay lenses that provide high-performance image transmission for target acquisition and tracking in military optical systems. Relay lenses are critical components for clear and lossless image transmission over long distances. In this study, the optical performance of a relay lens system designed and optimized using ZEMAX software is investigated in detail. The analysis focuses on important optical properties such as modulation transfer function (MTF), spot diagrams, Seidel diagram, field curvature and distortion. The results show that the lens has significant potential in military applications for target detection and tracking with high resolution and low aberration.