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31,456 Article Results

Lyapunov controller design for a fuel cell electric vehicle powered by a three-level floating capacitor boost converter

10.11591/ijpeds.v17.i3.pp1620-1629
Chaouqi Aouadi , Abdelmajid Abouloifa , Meriem Aourir , Ibtissam Lachkar
Electric vehicles (EVs) have undergone considerable progress facilitated by the adoption of contemporary technologies, such as innovative energy sources like the fuel cell (FC) in conjunction with novel power converter architectures. In this context, this work addresses the problem of controlling a three-level flying capacitor boost converter (FCBC) powered by a fuel cell. The converter is linked at its output to an impedance modelling the DC/AC converter and a brushless motor integrated into an FC-EV. Variations in load demand and power provided by the fuel cell are an issue to be considered, alongside voltage balancing, which involves maintaining balanced voltages between flying capacitors, as an imbalance can lead to uneven component voltage and reduced converter efficiency. To solve these issues, a nonlinear controller is developed making use of the Lyapunov approach which ensures the proper operation of the system by continuously adjusting control actions based on the system dynamics. Indeed, the regulator comprises three loops: one dedicated to balancing the voltage across the flying capacitor, and two nested loops; The inner loop controls the inductor current, while the outer loop regulates the output voltage.
Volume: 17
Issue: 3
Page: 1620-1629
Publish at: 2026-09-01

Adaptive predictive control for wind energy efficiency in real-time in smart grids

10.11591/ijpeds.v17.i3.pp2210-2223
Kadhim Hamzah Chalok , Karrar Hameed Kadhim
With the increasing global interest in renewable energy, advanced control strategies for wind energy conversion systems (WECSs) are critical for achieving not only maximum efficiency but also grid compatibility. This paper introduces a new adaptive, forecast-based predictive control algorithm to maximize the real-time energy efficiency of wind turbines integrated with smart grid architectures. The method couples a short-term wind-speed prediction module with a model-predictive control algorithm that uses the predictions to anticipate and adjust turbine operating conditions, such as pitch angle and rotor speed, to counteract the mechanical load from wind shear while maximizing power production. The analysis employs a dataset of 487 high-resolution cases of wind speed and wind turbine performance metrics to train and validate the adaptive model. The simulations were conducted in MATLAB/Simulink, using a model that captures the turbine’s nonlinear behavior and the electrical grid connection. The results indicate that the proposed controller increases the cycle-averaged power coefficient by approximately 8.2% and reduces tip-speed-ratio tracking error by roughly 70% relative to a conventional proportional-integral-derivative (PID) controller, while also reducing pitch and torque actuation, thereby lowering structural fatigue loads. This study is expected to provide a reliable solution to wind energy intermittency and facilitate the more stable and efficient integration of wind farms into modern smart grid systems.
Volume: 17
Issue: 3
Page: 2210-2223
Publish at: 2026-09-01

Optimization of EV battery charging for temperature control

10.11591/ijpeds.v17.i3.pp1601-1609
R. J. Vijaya Saraswathi , Krishnakumar Vengadakrishnan , Vasan Prabhu Veeramani , S. Kamalakkannan
Electric vehicles (EVs) offer a sustainable mode of transportation; however, excessive battery temperature rise during charging degrades performance, limits lifespan, and affects the safe operation of power converters and EV drive systems. To address this problem, a back propagation neural network (BPNN) based temperature prediction model is integrated with four nature-inspired optimization techniques (NIOTs): whale optimization algorithm (WOA), moth flame optimization (MFO), modified particle swarm optimization (MPSO), and grey wolf optimization (GWO), to optimize multi-stage charging current profiles. Among the evaluated methods, WOA achieves the best performance, reducing charging time by approximately 10% (11400 s to 10300 s) and average temperature rise by nearly 50% (3.9 °C to 1.9 °C) compared to the conventional constant current-constant voltage (CC-CV) strategy. The optimized charging currents directly support improved DC-DC converter operation and stable EV drive performance by limiting thermal stress and current transients. Overall, accurate thermal-aware charging enhances charging efficiency, ensures safe converter operation, and contributes to reliable and long-life EV battery and drive system performance.
Volume: 17
Issue: 3
Page: 1601-1609
Publish at: 2026-09-01

Analytical design, modelling and simulation of an LLC resonant converter for an electric vehicle auxiliary power module

10.11591/ijpeds.v17.i3.pp1885-1901
Mohamad Affan Mohd Noh , Jacques Juicy Patureau Ravina , Sivakumar Sivanesan , Hafizul Azizi Ismail , Ali Akbar Firoozi
This paper presents an analytical design, modelling, and simulation of an LLC resonant converter for an electric vehicle auxiliary power module (APM). The work contributes a complete LLC‑focused workflow that starts from design specifications and proceeds through: i) Resonant‑tank selection using normalized gain–frequency maps to choose the inductance ratio Ln and a quality factor Qe with soft‑switching in mind; ii) Transformer sizing from core data and loss limits; and iii) Small‑signal‑based PI controller tuning those accounts for the plant’s inherent inversion. The workflow is implemented and validated in MATLAB Simulink and PLECS for input voltages of 235-265 V and load levels from 10% to 100%. The converter achieves a peak efficiency of 98.1% within an 80.6-128.3 kHz switching range, full‑load efficiency of 93.5%, and about ±1% output voltage regulation. The controller maintains stable output settling times of 0.42-0.44 ms during 100% to 80% load steps and the reverse transition. At the highest input voltage, the switching frequency drifts beyond the intended range, which indicates a clear target for controller refinement in future work. The results show that the proposed analytical workflow is practical for designing LLC converters that meet APM efficiency and regulation goals, with cross‑platform simulation providing consistent evidence of performance.
Volume: 17
Issue: 3
Page: 1885-1901
Publish at: 2026-09-01

Enhanced voltage control and load compensation for DFIG-based wind energy systems using a stator-side series converter

10.11591/ijpeds.v17.i3.pp2224-2246
Preethicaa Ramesh , Sowmmiya Uthayakumar , Tole Sutikno , Vigna Kumaran Ramachandaramurthy
The increasing deployment of doubly-fed induction generator (DFIG)-based wind energy systems (WESs) requires effective voltage regulation and power-quality enhancement under varying operating conditions. This paper proposes a coordinated voltage regulation and load-compensation technique using a rotor-side converter (RSCR) and a stator-side series converter (SSSCR). The RSCR provides two-directional slip-power control through cascaded voltage and current control loops, while the SSSCR provides series voltage injection for voltage regulation and load compensation. The proposed system is developed in MATLAB/Simulink and validated using an OPAL-RT OP4512 real-time platform under variable wind speed, unbalanced and non-linear loading, and voltage sag/swell conditions. Quantitative results show reductions in overall current total harmonic distortion (THD) of 25.34% and 36.78% under non-linear and unbalanced load conditions, respectively. The voltage regulation error is reduced from 9% to 3%, while the settling time decreases from 0.24 s to 0.12 s under non-linear loading. The proposed configuration also reduces the voltage overshoot from 2.61% to 0.02% during speed transition. These results demonstrate the effectiveness of the coordinated RSCR-SSSCR configuration for simultaneous voltage regulation and load compensation.
Volume: 17
Issue: 3
Page: 2224-2246
Publish at: 2026-09-01

To enhance efficiency in photovoltaic systems using optimization methods in battery management system

10.11591/ijape.v15.i3.pp985-994
Penumala Poornima , Kannan Boopathy
Battery management systems (BMS) have undergone significant advancements over the years, transitioning from basic control strategies to sophisticated optimization algorithms. Historically, established BMS approaches were limited in their ability to handle energy efficiently, often leading to suboptimal power utilization and higher total harmonic distortion (THD). Integration of in a BMS application in modern days. advanced algorithms, such as flying squirrel search optimization (FSSO), have been optimized using grey wolf optimizer (GWO), and artificial neural network (ANN) significantly enhanced the performance of the system. These algorithms are efficient in terms of optimizing the duty cycle, whereby there is even a healthy energy flow between renewable load requirements and power requirements. These algorithms are analyzed, and their effectiveness in comparing their effectiveness is different operational scenarios. GWO algorithm is indicated as having the best reduction of THD and consequently improving the quality of power in a minimized way reliability. The ANN shows great expertise in voltage. regulating, low level of THD. Meanwhile, the FSSO algorithm shows significant potential in improving energy efficiency and reducing operational costs, despite its higher THD levels in current phases. GWO has a minimal current THD of 4.65% in Phase 1, exceeding ANN and FSSO. ANN has a voltage THD of 10.61%. FSSO has greater current THD values over 15% and good voltage control, highlighting decisions.
Volume: 15
Issue: 3
Page: 985-994
Publish at: 2026-09-01

Techno-economic optimization of hybrid renewable energy systems for low-carbon EV charging station

10.11591/ijpeds.v17.i3.pp2039-2046
Jeyagopi Raman , Sudesh Nair Baskara , Harpreet Kaur Channi
The rapid adoption of electric vehicles (EVs) requires charging infrastructure that is sustainable, power-electronically efficient, and grid-compatible. This study presents a techno-economic and environmental optimization of a hybrid renewable energy-based EV charging station near Guru Nanak Dev Engineering College (GNDEC), Ludhiana, India, as a sustainable alternative to conventional petrol stations. Unlike studies based on assumed EV load profiles, the proposed framework integrates real-world e-rickshaw utilization data, inverter efficiency, and sensitivity analysis within a HOMER Pro optimization framework. The system combines solar photovoltaic (PV), battery storage, and grid supply through a bidirectional inverter to manage dynamic charging demand. Local solar irradiance (~5.6 kWh/m²/day) and actual e-rickshaw usage data were used as simulation inputs. Configurations were evaluated using net present cost (NPC), levelized cost of energy (LCOE), inverter efficiency, and CO₂ emissions. The optimal PV-battery-grid configuration achieved an NPC of $28,500, LCOE of $0.11/kWh, inverter efficiency of 95%, and approximately 95% lower CO₂ emissions than grid-connected configuration, with a 6.5-year payback period. The findings highlight the importance of power-electronic efficiency and energy management for economically viable, low-carbon EV charging infrastructure, supporting SDGs 7 and 9.
Volume: 17
Issue: 3
Page: 2039-2046
Publish at: 2026-09-01

Analysis a mixtures of bentonite, palm kernel shell charcoal and magnesium sulfate (MgSO4) for reducing grounding resistance using rod-type electrodes at varying soil depths

10.11591/ijape.v15.i3.pp1386-1398
Ferry Rahmat Astianta Bukit , Naemah Mubarakah , Adrian Sinaga
A grounding system is an essential electrical safety mechanism designed to protect humans and equipment from disturbances such as lightning-induced surge currents or short circuits. It operates by channeling excess current into the ground through grounding electrodes, thereby reducing the risk of damage and hazards. High grounding resistance can compromise the dissipation of fault currents and overvoltages, leading to safety risks. This study focuses on reducing grounding resistance through chemical soil treatment using bentonite combined with a mixture of magnesium sulfate (MgSO₄) and palm shell charcoal. The optimal composition consists of 10% bentonite, 10% native soil, and 80% of the material mixture. Experiments were conducted at electrode depths of 30 cm, 60 cm, 90 cm, and 110 cm. Results showed that the initial resistance of 312.5 Ω at 30 cm depth decreased to 86.1 Ω, and at 110 cm depth, resistance decreased from 186.5 Ω to 53.0 Ω. The average reduction reached 77.4%, indicating that this material combination is highly effective in lowering grounding resistance and improving system performance.
Volume: 15
Issue: 3
Page: 1386-1398
Publish at: 2026-09-01

Design and analysis of low-k dielectric TSV liners for noise mitigation in high-frequency 3D ICs

10.11591/ijict.v15i3.pp1188-1196
Pathakunta Guru Prathap Reddy , Sravan Abhilash Kothapalli
Moore’s Law has driven the development of very large-scale integration (VLSI) technology, allowing continuous transistor scaling to increase speed, density, and performance. However, as two-dimensional (2D) integrated circuits (ICs) near their physical and performance boundaries, and 2.5D ICs still face interconnect delay and power issues, three-dimensional (3D) integration has become a practical solution. In 3D ICs, multiple active layers are vertically stacked and connected via through-silicon vias (TSVs), providing short, high-bandwidth interconnects between layers. Electrical TSVs are essential for signal transmission, but also cause noise coupling between adjacent TSVs, where an aggressive TSV can induce interference in a nearby TSV. This coupling can impair signal integrity, increasing delay and power consumption. To mitigate this, low-dielectric-constant (low-k) materials are used to reduce capacitive coupling. In this study, materials such as benzocyclobutene (BCB), Perylene-N, and Teflon AF 1600 are compared with conventional SiO₂. Generally, TSVs are two structures — single-liner and stacked-liner — which are analysed at 10 GHz and 1 THz frequencies. At 10 GHz, the single-liner structure incorporating SiO₂ exhibits a noise reduction of about 6.56 dB, whereas the stacked-liner configuration using Teflon AF 1600 provides a noticeably greater reduction of 8.40 dB. As the operating frequency increases to 1 THz, the advantage of the low-k dielectric becomes more evident, yielding 9.63 dB noise reduction for the single-liner and 12.04 dB for the stacked-liner structure. These results indicate that low-k materials effectively suppress capacitive coupling and mitigate high-frequency interference in 3D ICs. The stacked-liner design contributes additional isolation by creating a secondary dielectric barrier, which further minimizes electric field interaction between neighboring interconnects. Thus, the integration of low-k dielectrics with optimized liner architectures significantly enhances signal integrity and overall electromagnetic performance in advanced high-frequency 3D IC systems.
Volume: 15
Issue: 3
Page: 1188-1196
Publish at: 2026-09-01

Insight invest: sentiment-aware stock prediction using LSTM and conversational interface

10.11591/ijict.v15i3.pp1115-1122
Ankit Pande , Aakhyan Jeyush , Abhishek K. Lakhote , Saket A. Rathi , Manoj B. Chandak
The volatile nature of financial markets requires sophisticated tools that integrate advanced analytics with accessible interfaces to facilitate informed investment decisions. This research introduces Insight Invest, an intelligent investment assistant that combines sentiment analysis with time-series forecasting to deliver comprehensive stock market insights. The platform introduces the emotional quotient (EQ), a novel metric derived from the sentiment analysis of financial news, to quantify market sentiment and align it with historical stock price data. Leveraging long short-term memory (LSTM) models, the system provides precise predictions of future stock trends. Automated data collection and processing are achieved through a Flask-based backend, while an OpenAI-powered chatbot delivers intuitive interpretations of predictions and EQ values. The user-centric design, implemented using Next.js, ensures a seamless and responsive experience. By integrating state-of-the-art machine learning techniques with intuitive interfaces, Insight Invest bridges the gap between complex predictive analytics and practical usability, offering a robust framework for informed investment strategies.
Volume: 15
Issue: 3
Page: 1115-1122
Publish at: 2026-09-01

Metaverse based immersive learning prototype for satellite communication using silvercoms model

10.11591/ijict.v15i3.pp1408-1418
Komputerio Akbar , Meyliana Meyliana , Harco Leslie Hendric Spits Warnars , Ilvico Sonata
Advancements in immersive technologies and the metaverse are transforming engineering education. Satellite communication, as a complex and abstract domain, requires innovative approaches to enhance conceptual understanding and learner engagement beyond traditional methods. This study proposes the Silvercoms model, an immersive learning framework tailored for satellite communication systems education, integrating metaverse technology with pedagogical design. The model combines 3D interactive simulations, collaborative virtual environments, and systems-level content delivery, structured using the 6E instructional model and supported by the motivated strategies for learning questionnaire (MSLQ) to address both cognitive and motivational aspects of learning. The system is developed using a systems engineering approach and implemented with Unity3D in a virtual reality (VR) based metaverse environment. The prototype includes modules such as satellite orbit simulation, satellite history, and interactive satellite systems, enabling experiential and concept-driven learning. This study contributes by integrating immersive technology with structured pedagogical frameworks, offering a novel approach to improving learning effectiveness in satellite communication systems education.
Volume: 15
Issue: 3
Page: 1408-1418
Publish at: 2026-09-01

Advanced encryption standard with asymmetric key exchange for text encryption

10.11591/ijict.v15i3.pp1263-1271
Ravindra K Reddy , Vijayalakshmi P
When creating communication systems, securing data is crucial and improved randomization in creating secret keys contributes to more secure systems. Unfortunately, the symmetric ciphers used for data encryption, such as the advanced encryption standard (AES), may be subject to attacks that exploit timing measurements to deduce the secret key used for encryption, resulting in a significant lack of research on the security of hybrid implementations (usually defined as incorporating AES and asymmetric ciphers) for AES encryption. We introduce a new hybrid encryption method that combines the AES encryption standard with elliptic curve cryptography (ECC). In this hybrid form of encryption, ECC is utilized to facilitate secure encryption and transmission of the AES key and cycle through 16 rounds of AES to encrypt the majority of the data. We provide a performance comparison between the new algorithm and the AES-128 encryption standard. Our findings indicate that the new hybrid encryption method produced an average encryption time of 0.0002 seconds for 10MB files, significantly faster than the AES-128 encryption standard, which takes an average of 0.0016 seconds. The new hybrid method exhibits significant resistance to cryptanalytical attempts, experiencing an average avalanche effect of 49.84%, while maintaining the AES nonlinearity value at 112. As such, we conclusively state that the new hybrid encryption method utilizing ECC and AES will provide effective security for user data against timing side-channel attacks while remaining an efficient method for performing encryption.
Volume: 15
Issue: 3
Page: 1263-1271
Publish at: 2026-09-01

Wavelet-based spectrum sensing with improved thresholding for enhanced detection in cognitive radio networks

10.11591/ijict.v15i3.pp1123-1134
Nur Hanis Abdul Rani , Mas Haslinda Mohamad , Nurusolihah Zamri , Nor Khairiah Ibrahim
Cognitive radio (CR) technology is an adaptive, intelligent radio and network technology that can automatically detect available channels in a wireless spectrum. Spectrum sensing is the most important component in CR due to its ability to sense and recognize parameters related to the radio channel characteristics. However, there are some spectrums that are not used known as spectrum holes. It is challenging to accurately identify these spectrum holes, especially when employing traditional energy detection techniques, which suffer from incorrect threshold selection at low signal-to noise ratio (SNR) levels. This work suggests a wavelet-based spectrum sensing technique in conjunction with an enhanced thresholding method to improve detection accuracy and decrease noise to overcome this constraint. MATLAB simulations are used for evaluating three threshold functions: hard, soft, and improved. The results indicate that the improved threshold achieves superior denoising performance and a higher detection probability compared to the traditional energy detection method. In this study, the energy detection technique was also implemented for comparison with the wavelet-based approach. The findings reveal that wavelet-based sensing consistently provides a higher detection probability (𝑃𝐷𝐸𝑇), demonstrating its effectiveness and reliability for cognitive radio (CR) application.
Volume: 15
Issue: 3
Page: 1123-1134
Publish at: 2026-09-01

A recent hybrid of IoT with adaptive extended Kalman filter fuzzy logic for children’s health dietary

10.11591/ijict.v15i3.pp1217-1225
Noorrezam Yusop , Massila Kamalrudin , Mohd Nazrien Zaraini , Siti Fairuz Nurr Sardikan
The increasing prevalence of childhood obesity highlights the critical need for intelligent dietary monitoring systems that are tailored to individual nutritional requirements. This work describes the creation and testing of an internet of things (IoT) hybrid using an adaptive extended Kalman filter and fuzzy logic (AEKFFL-IoT) model aimed at providing personalised food calorie prediction for children. The system uses IoT devices to collect real-time sensor data, such as height, weight, and BMI, and then employs extended Kalman filter (EKF) algorithms to denoise signals and anticipate trends. Fuzzy logic inference is then utilised to adaptively calculate caloric requirements based on biometric data. Experimental results reveal that the proposed AEKFFL model has a training root mean square error (RMSE) of 21.43 kcal and a testing RMSE of 22.35 kcal, exceeding existing rule-based, wearable, and ANN-driven models in terms of accuracy and generalisation. Furthermore, the system achieves high classification accuracy (94.5%) for BMI categorisation and fuzzy rule application. Comparative examination confirms the model’s adaptability, real-time integration, and mobile deployment capability. This study presents a scalable and intelligent approach for child-centered dietary monitoring, paving the path for personalised digital health interventions.
Volume: 15
Issue: 3
Page: 1217-1225
Publish at: 2026-09-01

Enhanced anomaly detection in IoT networks via feature fusion and learning-based echo state networks

10.11591/ijict.v15i3.pp1154-1166
P. Palpandi , B. Sakthivel , M. Ponnrajakumari , M. Indirani , S. Govindaraju , S. Deivasigamani
The fast development of internet of things (IoT) networks has led to an increased probability of cyberattacks. Intrusion detection systems (IDS) are needed for identifying unauthorised access and malicious activities in such dynamic environments. However, existing machine learning (ML) models failed to handle the complexity and variability of modern cyber threats. In this work, a hybrid deep learning (DL)-based anomaly detection model is presented for IoT cybersecurity. The model combines three types of features: (i) supervised feature extraction using linear discriminant analysis (LDA) to extract the most discriminative features, (ii) unsupervised feature learning through autoencoders to capture latent representations of the input data, and (iii) statistical features such as mean, variance, skewness, and kurtosis to learn input characteristics. The fused feature matrix is fed into a learning based echo state network (LBESN) for final detection. The parameters of the LBESN model are tuned using black eagle optimizer (BEO). Experimental results on standard intrusion detection datasets such as UNSW-NB15, KDD99, and InSDN show that the proposed model achieves superior performance in terms of accuracy, precision, recall, and F1-score compared to conventional DL techniques.
Volume: 15
Issue: 3
Page: 1154-1166
Publish at: 2026-09-01
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