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30,376 Article Results

Multi-objective energy management optimization in electric vehicles using fuzzy logic and particle swarm optimization

10.11591/ijpeds.v17.i2.pp1025-1035
V. Lakshmi Devi , Damodhar Reddy , Srikanth Velpula , K. Kumar , Basi Reddy Avula
This paper proposes a hybrid energy management system (EMS) for electric vehicles by integrating fuzzy logic control (FLC) with particle swarm optimization (PSO) to improve power-split decision-making under dynamic driving conditions. The FLC is designed using state of charge (SoC) and vehicle speed as input variables and power split as the output. A set of fuzzy rules defines the EMS behavior, while PSO is employed to fine-tune decisions by maximizing an efficiency objective function defined as the closeness of the power split to an ideal reference. The simulation is implemented in Python using Colab-compatible packages such as scikit-fuzzy, DEAP, and matplotlib, ensuring accessibility and reproducibility. A test grid covering 10 SoC levels (10-100%) and 10 speed levels (10-120 km/h) is used to evaluate the system. Visualization tools, including heatmaps, 3D surface plots, and contour plots, are employed to represent the EMS behavior. The PSO-enhanced system achieved a maximum efficiency of 98.2% at an optimized SoC of 61.7% and a speed of 53.6 km/h, outperforming standalone fuzzy logic control. Tabulated results and statistical summaries validate the effectiveness of the proposed system.
Volume: 17
Issue: 2
Page: 1025-1035
Publish at: 2026-06-01

Torque ripple reduction in PMSM for FCEVs using ANFIS controller

10.11591/ijpeds.v17.i2.pp885-893
Shilpa Rao Hosabettu , Pushpa Rajesh Viswanathan
Globally, there is a growing emphasis on switching to green energy, particularly in the transportation sector, due to the effects of global warming, as seen by rising carbon footprints. Fuel cell electric vehicles (FCEVs) are one such technology that has attracted a lot of interest because of their availability, ease of use, high efficiency, and silent operation. Fuel cells are employed along with batteries to drive the vehicle much farther. Motors like permanent magnet synchronous motor (PMSM) provide the driving force for the vehicle, owing to their high torque at variable speeds and compactness. In such systems, it is necessary to have intelligent controllers that can align with the load requirement by means of a consistent and optimized power distribution. The torque ripple phenomenon, which has an impact on dynamic performance and operational stability, is one of the main limitations in the operation of PMSMs. In this work, smart control techniques, which are a combination of adaptive neuro fuzzy inference systems (ANFIS) and proportional-integral (PI) control, are employed to demonstrate the application of PMSM in conjunction with field-oriented control (FOC). Simulation results indicate that the proposed ANFIS-based FOC reduces torque ripple as compared to conventional PI control under varying load conditions.
Volume: 17
Issue: 2
Page: 885-893
Publish at: 2026-06-01

Adaptive notch filter: An alternative synchronizer for effective performance of active power filter under challenging grid conditions

10.11591/ijpeds.v17.i2.pp1221-1230
Yap Hoon , Kuew Wai Chew , Kenny Sau Kang Chu , Siti Zaliha Mohammad Noor
Harmonic distortion issues on modern power systems are becoming highly significant due to the increasing integration of renewable energy sources, electric vehicles, and smart technologies. These distortions, mainly caused by the operation of power electronics devices, potentially degrade overall system quality, increase losses, and shorten equipment lifespan if they are not properly mitigated. Shunt active power filters (SAPFs) are found to be most effective against current harmonics issues, but their performance strictly depends on accurate grid synchronization. In this paper, an alternative method developed based on the adaptive notch filter (ANF) concept is proposed for reliable grid synchronization under challenging conditions. The proposed ANF-based synchronizer is modelled in MATLAB/Simulink and benchmarked against the existing self-tuning filter (STF) method under four cases involving sinusoidal, distorted, noisy, and distortion-with-noise grid conditions. Simulation findings demonstrate that the proposed method enables the connected SAPF to effectively mitigate harmonics by providing low total harmonic distortions (2.71% to 2.82%) and minimal phase deviation (0.2° to 0.5°), while maintaining the accuracy of fundamental current between 94.48% to 97.21%. As a result, the overall power factor of the system is raised to near unity, confirming the ability of the proposed ANF-based method to serve as a better alternative for SAPF synchronization.
Volume: 17
Issue: 2
Page: 1221-1230
Publish at: 2026-06-01

Dual mode control of an integrated on-board charger powered BLDC drive

10.11591/ijpeds.v17.i2.pp1058-1068
Caroline Ann Sam , Varghese Jegathesan
The high adoption of electric vehicles in transportation has created a demand for compact, efficient, and cost-effective charging solutions for them. Conventional onboard chargers are often bulky, which adds to the overall cost of the drive system, whereas off-board charging infrastructure remains limited. In order to address these issues, this work illustrates the design and modelling of an active power factor corrected integrated onboard charger which gets reconfigured from the electric vehicle drive train components. The proposed circuit setup is designed to work in dual mode, i.e., in the role of a DC-DC converter while charging the vehicle battery and as a three-phase inverter while driving the vehicle. The front-end power factor correction circuit, in addition to the reconfigured DC-DC converter, charges the 24 V, 20 Ah lead acid battery under constant current constant voltage (CC-CV) mode, achieving a power factor close to unity. Modelling and control of the proposed 200 W reconfigurable converter-fed 24 V, 180 W brushless direct current (BLDC) drive is validated using MATLAB/ Simulink Software. Simulation results demonstrate a power factor of 0.996 in grid-connected operation with a total harmonic distortion (THD) of 4.96%. The proposed architecture achieves a compact structure with only 8 switches enabling charging, propulsion and regenerative braking operation. The proposed converter thus contributes to a cost-effective electric vehicle and provides the scope of future extension to vehicle to home (V2H), vehicle to load (V2L), and vehicle to vehicle (V2V) applications as well.
Volume: 17
Issue: 2
Page: 1058-1068
Publish at: 2026-06-01

Design simulation and analysis of an MPPT technique using ANNs integral backstepping and SMC for PV systems

10.11591/ijpeds.v17.i2.pp1288-1303
Naoufal Zhani , Hassane Mahmoudi
This paper introduces the design of an innovative hybrid MPPT method called artificial neural networks-integral backstepping sliding mode control (ANN-IBSMC). This approach combines artificial neural networks (ANNs), which output the maximum power point voltage using inputs such as irradiance and temperature, with a robust control strategy. The designed controller aims to track the reference voltage with high accuracy and responsiveness by modifying the pulse width modulation of the DC-DC converter in the photovoltaic system. The IBSMC integrates the advantages of two control methods: the stability and accuracy of integral backstepping, and the robustness and fast response of sliding mode control (SMC). This combination enables improved precision, high convergence speed, enhanced robustness, and strong stability, the latter being ensured by the Lyapunov function. To evaluate the performance of the proposed controller, a comparative study is performed against other hybrid control techniques, such as the ANN-backstepping controller, the ANN-integral sliding mode controller, and the ANN-backstepping sliding mode controller, using MATLAB/ Simulink. A sensitivity and robustness analysis was carried out.
Volume: 17
Issue: 2
Page: 1288-1303
Publish at: 2026-06-01

Advanced soft-switching high-gain Re Boost Luo converter for enhanced efficiency in photovoltaic systems

10.11591/ijpeds.v17.i2.pp1177-1187
Vendoti Suresh , Dondapati Ravi Kishore , T. Vijay Muni , P. Hari Krishna Prasad , Pydi Bala Krishna , A. V. G. A. Marthanda
This work presents an innovative approach to improving efficiency and performance in photovoltaic (PV) systems through the development of a soft-switching high-gain Re Boost Luo converter. This converter integrates advanced soft-switching techniques to minimize switching losses, thereby enhancing overall system efficiency, which is crucial for applications requiring substantial voltage amplification from PV sources. The Re Boost Luo converter, with its inherent high-gain capability, facilitates superior voltage conversion ratios, enabling optimal energy extraction from PV panels across varying environmental conditions. The presented converter's design focuses on reducing electromagnetic interference (EMI) and alleviating stress on switching components, thereby extending their operational lifespan and reliability. Detailed modeling and performance analysis were carried out using the MATLAB/Simulink simulation environment, which allowed for comprehensive evaluation of the converter's functionality. Simulation results confirm that the converter achieves significant improvements in voltage gain, energy conversion efficiency, and system reliability, effectively addressing common challenges associated with high-voltage PV applications. This study underscores the converter's potential to advance renewable energy technologies by providing a robust solution for high-efficiency energy conversion in PV systems.
Volume: 17
Issue: 2
Page: 1177-1187
Publish at: 2026-06-01

Evaluating gamified learning strategies in internet of things-based software engineering education

10.11591/ijere.v15i3.39041
Amneh Shaban , Arar Al Tawil
This study examines the effectiveness of gamified formative assessment in undergraduate internet of things (IoT) education, focusing on how content complexity and question format influence student performance. A quasi-experimental comparative design was employed, administering two gamified quizzes to 75 undergraduate students enrolled in two IoT-related courses DevOps for IoT (n=41) and human computer interaction in IoT (n=34) during spring 2025. The gamified platform incorporated visual feedback, progress indicators, and interactive components. Results revealed statistically significant differences in student performance between the two quiz conditions, with human–computer interface (HCI) students substantially outperforming DevOps students. Question-level analysis further indicated that fill-in-the-blank formats impaired performance more than multiple-choice formats, and a pronounced ceiling effect was observed in the HCI assessment. These findings suggest that gamification effectiveness is contingent on alignment between content complexity, question format, and students’ prior knowledge. Educators are advised to calibrate assessment difficulty and question types carefully when designing gamified learning experiences in technical education.
Volume: 15
Issue: 3
Page: 2249-2260
Publish at: 2026-06-01

Using Python programming to foster students’ scientific thinking

10.11591/ijere.v15i3.35421
Nadezhda Gallini , Anatoliy Kazak , Nataliya Gorbunova , Victor Gallini , Anisa Atik , Elena Sergeeva
This paper explores the role of programming not only as a technical skill but also as a method for developing students’ scientific thinking. The study is based on an experiment involving 258 university students who completed a course designed to foster logical, analytical, and project-based cognitive strategies. The experiment was conducted at V.I. Vernadsky Crimean Federal University, Humanities and Education Science Branch, Yalta, Russia. The proposed model integrates elements of the technological pedagogical content knowledge (TPACK) framework with a custom-designed pedagogical approach, treating programming as a universal structure for processing and representing information. The data suggest that systematic engagement with programming enhances students’ ability to solve complex problems, conceptualize, decompose tasks, and apply reflective design. The findings emphasize the need to rethink the role of programming in higher education curricula across disciplines, beyond purely technical training.
Volume: 15
Issue: 3
Page: 2410-2418
Publish at: 2026-06-01

Asegmentation based optical character recognition system for Bangla printed text

10.12928/telkomnika.v24i3.26961
Mahir; Bangabandhu Sheikh Mujibur Rahman Digital University Mahbub , Ahmedul; University of Dhaka Kabir
Bangla ranks as the fifth most spoken language globally, catalyzing significant interest in the development of Bangla optical character recognition (OCR) sys tems. The intricate structure of the Bangla script, including compound char acters, modifiers, and headlines, complicates the formation of words. This research introduces a complete OCR system pipeline for printed Bangla text. It employs a thinning-based segmentation approach combined with a convolu tional neural network (CNN) to recognize Bangla fonts. Additionally, a part of speech (POS)-aware spell checker is proposed that automatically corrects mis spelled words while considering their context within the sentence. We intro duce semi-generalized filters that adapt to new fonts, addressing conjunct for mation challenges in Bangla OCR. This flexible design allows for adaptation to new fonts. The ResNet50 model is utilized to accurately recognize segmented characters and modifiers. We achieve a character segmentation error of 3.354% and an overall segmentation error of 2.332%. The ResNet50 recognition model achieves an accuracy of 98.345%.
Volume: 24
Issue: 3
Page: 945-956
Publish at: 2026-06-01

Voltage stress mitigation in high-gain DC-DC converters via dual Z-source DC-DC converter

10.11591/ijape.v15.i2.pp735-743
Jawahar Marimuthu , Arockiaraj Sesaiya , Bhavani Ramachandran , Ramya Hyacinth Lourdusamy
This paper presents a novel dual Z-source DC-DC converter designed to address the limitations of conventional high step-up converters used in renewable energy applications such as solar photovoltaic systems and fuel cells. Traditional boost and impedance-source converters often suffer from high voltage stress, low efficiency at higher power levels, and complex multi-stage configurations. To overcome these challenges, the proposed topology integrates a hybrid structure comprising symmetrical inductors and capacitors, enabling high voltage gain at reduced duty cycles while minimizing component stress. The converter is analytically modelled and evaluated under continuous conduction mode, and its performance is verified through MATLAB/Simulink simulations and experimental validation using a hardware prototype. The results demonstrate that the proposed converter achieves a voltage gain of up to 10× with a duty cycle below 0.5, while maintaining efficiency above 95% and significantly reducing voltage stress across switching devices. Compared to existing high step-up converters, the proposed design offers improved efficiency, reduced component count, and enhanced reliability. These features make it a promising solution for efficient and sustainable energy conversion in modern renewable energy systems.
Volume: 15
Issue: 2
Page: 735-743
Publish at: 2026-06-01

Exploring the relationship of learning engagement, learning interaction, and learning outcomes in gamified massive open online courses

10.11591/ijece.v16i3.pp1329-1338
Azizul Mohd Yusoff , Sazilah Salam , Siti Nurul Mahfuzah Mohamad , Bambang Pudjoatmodjo
This study investigates the interplay between learning engagement, interaction, and outcomes within the context of gamified massive open online courses (G-MOOCs). By synthesizing literature on MOOCs, gamification, and user engagement, the research identifies significant correlations among these variables. Utilizing a structural equation model partial least squares (SEM-PLS) approach, the study analyzes data from a survey of Bachelor of Computer Science students at a technical and vocational education and training (TVET) public university. Results indicate that both learning engagement and interaction significantly influence learning outcomes, with optimal results achieved when both factors are high. These findings highlight the potential of gamification to enhance educational experiences and suggest directions for future research in gamified learning environments.
Volume: 16
Issue: 3
Page: 1329-1338
Publish at: 2026-06-01

A novel Lucas-based adaptive sampling optimization for enhancing network lifetime

10.11591/ijict.v15i2.pp607-615
Kanaka Raju Rajana , Shanmuk Srinivas Amiripalli
This paper introduced to enhance network lifetime using a novel Lucas based adaptive sampling methodology by sampling network condition to dynamically modifying sampling intervals using the Lucas sequence, this sequence not only used for sampling but also used to modify data collection, optimizing accuracy and energy efficiency. This technique aims to reduce superfluous data transmissions and conserve network resources by monitoring network utilization and adjusting sample with low medium and high rates. We enhance the network performance and longevity using Lucas based technique via simulation and demonstrating its potential. This may effectively approach novel address to challenges associated with constrained networks, particularly in the domain of IoT and wireless sensor networks (WSNs).
Volume: 15
Issue: 2
Page: 607-615
Publish at: 2026-06-01

Early prediction of myocardial infarction using proposed score tree algorithm

10.11591/ijict.v15i2.pp813-822
Nusrat Parveen , Utkarsha Pacharaney , Gayatri Hegde , Mohammad Rafique , Sana Firoj Nalband , Shamim Akhtar , Satish Devane
Early detection and diagnosis of a diseases will have a big impact on the medical field and help to prevent loss of life. This study begins by gathering information on myocardial infraction patients from hospitals and focuses on earlier diagnostics. In fact, the pre-processed, confirmed data from a qualified doctor is used for this research. Early prediction of myocardial infarction (MI) is proposed by many researchers. They have used Kaggle datasets that is not recent, and they work on post MI. We have proposed early myocardial infraction detection works on unsupervised datasets. To identify myocardial infraction, numerous machines learning supervised algorithms, including decision tree (DT), random forest (RF), are employed in the literature. In this study, we use the score tree algorithm (STA), which operates on an unsupervised dataset, to present a unique early MI prediction method.
Volume: 15
Issue: 2
Page: 813-822
Publish at: 2026-06-01

Enhanced transfer learning framework for brain tumor detection from MRI scans using attention-based feature fusion

10.11591/ijict.v15i2.pp497-507
Smita Bharne , Ekta Sarda , Shamal Salunkhe
Due to the complexity of the different tumor types in medical imaging detection of brain tumor is still as prominent challenge. This paper present the innovative technique enhanced transfer learning framework (ETLF) which integrating the advanced pre-processing with hybrid fine-tuned method for accurate brain tumor detection from magnetic resonance imaging (MRI) scans. The proposed model combine the strength of pre-trained convolutional neural networks (CNNs) such as EfficientNetB0 through domain specific transfer learning and attention based fine tuning. A novel feature fusion layer and adaptive learning rate scheduler are key indicators for model performance and prevent overfitting. The methodology is assessed on the benchmark dataset BraTS and Kaggle brain tumor datasets. The main contribution of work lies in development of domain- adaptive transfer learning with different datasets. The ETLF shows the high accuracy of 98.76% which able outperforms effectively in diagnosing tumor suitable of clinical purpose.
Volume: 15
Issue: 2
Page: 497-507
Publish at: 2026-06-01

Manufacturing mycelium moulds under controlled conditions using IoT

10.11591/ijict.v15i2.pp880-890
Subbulakshmi V. , Jeevaa Katiravan , Parvathy M. , Sridevi S.
In the process of making plastics, potentially dangerous substances like colourants or stabilisers are added. One example is phthalates, which are used to make PVC. The ecology is significantly impacted by the way plastic products are disposed of as well. The majority of plastics can take a long time to biodegrade lengthy time to break down if disposed of in a landfill. The issue of plastic trash is getting worse. Plastic is incredibly valuable due to its cheap availability and low cost of production; however, its recyclability has been oversold. Mycelium mould is a fantastic substitute for plastic. Mycelium is more efficient in terms of biodegradability and sustainability compared to plastic. The properties of Mycelium include heat insulation, fire resistance, water resistant, acoustic insulation, low weight, vegan meat, beauty products, and mainly bio-degradable. All these features make mycelium our only last chance to win the war against the plastic with greater potential than the other alternatives for plastics available in the market currently. Here, we have shown how mycelium can be grown in the most efficient way ever without any contamination and faster growth cycle. The primary goal is to lower the cost of mycelium mould, lessen mycelium spoiling, and accelerate its growth cycle by offering an ideal growing.
Volume: 15
Issue: 2
Page: 880-890
Publish at: 2026-06-01
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