International Journal of Power Electronics and Drive Systems (IJPEDS)
International Journal of Power Electronics and Drive Systems (IJPEDS, ISSN: 2088-8694, a SCOPUS indexed Journal) is the official publication of the Institute of Advanced Engineering and Science (IAES). The scope of the journal includes all issues in the field of Power Electronics and drive systems. Included are techniques for advanced power semiconductor devices, control in power electronics, low and high power converters (inverters, converters, controlled and uncontrolled rectifiers), Control algorithms and techniques applied to power electronics, electromagnetic and thermal performance of electronic power converters and inverters, power quality and utility applications, renewable energy, electric machines, modelling, simulation, analysis, design and implementations of the application of power circuit components (power semiconductors, inductors, high frequency transformers, capacitors), EMI/EMC considerations, power devices and components, sensors, integration and packaging, induction motor drives, synchronous motor drives, permanent magnet motor drives, switched reluctance motor and synchronous reluctance motor drives, ASDs (adjustable speed drives), multi-phase machines and converters, applications in motor drives, electric vehicles, wind energy systems, solar, battery chargers, UPS and hybrid systems and other applications.
Articles
2,594 Documents
<![CDATA[Design and implementation of digital logic for brushless DC motor control in electric vehicles ]]>
<![CDATA[Brearley, Belwin J.]]>;
<![CDATA[Bose, K. Regin]]>;
<![CDATA[Kumar, K. Ganesh]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v16.i4.pp2143-2155
<![CDATA[In today's world, the rise in global warming is driving a shift towards electric mobility. The progress in battery technology and power electronic devices has facilitated the transition of vehicles from being powered by traditional internal combustion engines to electric motors. The types of motors utilized for propulsion include DC motors, three-phase induction motors, permanent magnet synchronous motors (PMSM), and brushless DC motors (BLDC). Among them, the BLDC motor, when paired with a suitable control algorithm, proves to be the most suitable option for electric vehicle applications. The existing control algorithms for BLD motors are quite complex. Therefore, this study presents the development of an innovative and simple digital control algorithm based on a combinational logic circuit to drive the BLDC motor under motoring and regenerative braking mode. The proposed control algorithm and its effectiveness are validated by simulating it using Xilinx & Proteus software and experimenting with the concept in hardware by utilizing a PIC microcontroller. The proposed control algorithm forms a cost-effective alternative for BLDC motor speed control.]]>
<![CDATA[Hybrid intelligent optimization algorithms-based power management for microgrid system ]]>
<![CDATA[Indumathi, R.]]>;
<![CDATA[Lakshmanan, S. A.]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v16.i4.pp2665-2676
<![CDATA[The integration of the photovoltaic (PV) and wind sources of power in microgrids is a beneficial method toward decentralized, efficient, and sustainable energy management. This research endeavors to develop and implement a novel hybrid control strategy that efficiently combines grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms for the optimization of renewable energy-based microgrids. The proposed method addresses three critical tasks under one integrated control mechanism: i) maximum power point tracking (MPPT) for PV and wind systems under fluctuating environmental conditions, ii) smart management of energy storage systems for batteries, and iii) adaptive load scheduling based on real-time availability of energy. By leveraging the complementary strengths of GWO and PSO, the system enjoys improved convergence rate, global search, and decision-making robustness. The hybrid controller is tested and validated through thorough testing in MATLAB/Simulink under dynamic simulation scenarios that mimic sudden weather and load variations. Comparative performance with conventional methods and benchmarking based on IEEE 516 standards demonstrates the improved reliability, responsiveness, and energy efficiency of the proposed system. This research contributes to the state-of-the-art of intelligent microgrid control through an integrated, bio-inspired solution toward resilient and optimized energy management.]]>
<![CDATA[Performance analysis of a cascaded dual full bridges 5, 7, and 9 levels inverter: experimental validation ]]>
<![CDATA[Saidani, Nabil]]>;
<![CDATA[Bachtiri, Rachid El]]>;
<![CDATA[FRI, Abdelaziz]]>;
<![CDATA[Hammoumi, Karima El]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v16.i4.pp2464-2475
<![CDATA[Cascaded full-bridge inverter is a suitable topology for grid-connected applications due to its ability to generate an output voltage waveform that closely resembles a sine wave, resulting in lower total harmonic distortion (THD) factors. This article proposes the use of the selective harmonic elimination (SHE) technique to produce a 5-level voltage using a symmetrical inverter and 7 and 9-level voltage using an asymmetrical inverter composed of only two full bridges loaded by an RL circuit of 51.4 Ω and 200 mH. The study primarily focuses on analysing the impact of the number of levels on the power quality of the inverter. This includes investigating the effects of the fundamental magnitude on the produced power, as well as measuring losses in the inverter, power factor, THD factor, and fundamental magnitude for each level configuration. The study demonstrates that asymmetrical MLIs lower THD (10.9% vs. 16.7%) and increasing voltage levels enhance waveform quality but slightly reduce the fundamental voltage magnitude, impacting AC power output. The simulation analysis has been conducted using the PSIM environment, and the results have been validated through experimental measurements.]]>
<![CDATA[Supervised learning for fast inverse motor control mapping: a comparative study on SRM and BLDC motors ]]>
<![CDATA[Reddy, S. Sudheer Kumar]]>;
<![CDATA[Sekhar, J. N. Chandra]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v16.i4.pp2419-2428
<![CDATA[This paper investigates the application of machine learning (ML) models, specifically artificial neural networks (ANN) and XGBoost, for real-time motor control, focusing on switched reluctance motors (SRM) and brushless DC motors (BLDC). Traditional inverse dynamics mapping for motor control is compared with ML approaches to highlight advantages in speed, accuracy, and deployment efficiency. Datasets simulating the input-output behavior of both motor types are used to train and test the models. Key performance metrics such as mean squared error (MSE), R² score, training time, and latency are evaluated, with the goal of replacing traditional control methods in real-time applications. Results indicate that ML models outperform traditional methods in terms of prediction accuracy and deployment speed, suggesting a promising path toward more efficient and adaptive motor control systems. The novelty of this work lies in applying supervised learning directly for inverse motor control mapping, thereby eliminating the need for explicit analytical models and enabling a unified, data-driven benchmarking framework across SRM and BLDC.]]>
<![CDATA[Design of an EBNN-PID based adaptive charge controller for variable DC charging applications ]]>
<![CDATA[Rifadil, Mochammad Machmud]]>;
<![CDATA[Putra, Putu Agus Mahadi]]>;
<![CDATA[Muklis, Amalia]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v16.i4.pp2634-2644
<![CDATA[This paper presents an adaptive charging system for lithium-ion batteries using an Elman backpropagation neural network (EBNN) integrated with a PID controller and a ZETA converter. The system dynamically identifies the battery type and adjusts the charging voltage accordingly. The EBNN model was trained using 1441 samples of initial current and voltage data, achieving a mean squared error (MSE) of 7.64×10⁻¹⁴. A ZETA converter enables both step-up and step-down voltage regulation, while the PID controller ensures stability toward the predicted setpoint. Simulations in Simulink were conducted on four lithium-ion battery types with setpoints of 4.4 V, 8.8 V, 14.4 V, and 21.6 V. The results show that the PID-regulated output voltage closely matches the target with a maximum deviation of ±0.05V and an average voltage error of 0.1725%. The system achieves fast response times between 0.015 and 0.033 seconds. Extended testing through 24 randomized trials confirmed consistent identification and regulation across varying battery types. These findings validate the proposed EBNN-PID-based charging system as a highly accurate, flexible, and efficient solution for managing lithium-ion battery charging in real-time embedded applications.]]>
<![CDATA[Polyaniline as a conductive polymer and its role in improving the efficiency and conductivity of perovskite solar cells ]]>
<![CDATA[Nazerian, Vahdat]]>;
<![CDATA[Dizaj, Mehran Hosseinzadeh]]>;
<![CDATA[Sutikno, Tole]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v16.i4.pp2731-2743
<![CDATA[This article investigates the role of polyaniline as a conductive polymer in the active layer of perovskite solar cells. Samples were created by incorporating polyaniline into the transport layers to assess its impact on enhancing efficiency and conductivity. The application of this polymer across various layers of the cell structure led to improved stability and performance. Given its high doping capability, polyaniline was examined in detail, particularly focusing on two types of oxidation doping and its integration into the hole transport layer. Graphene oxide and reduced graphene oxide were chosen as comparative models, and their performance was evaluated against the standard polyaniline configuration. Laboratory results revealed that power conversion efficiency increased by 17.5% with graphene oxide and by 36.8% with reduced graphene oxide. Furthermore, short-circuit current density improved by 9.8% and 23.1%, respectively. These findings are consistent with existing studies in the field and support the validity of the approach.]]>
<![CDATA[Design and simulation of an electric vehicle charging system with battery arrangement and control parameters optimization ]]>
<![CDATA[Pasra, Nurmiati]]>;
<![CDATA[Samman, Faizal Arya]]>;
<![CDATA[Achmad, Andani]]>;
<![CDATA[Yusran, Yusran]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v16.i4.pp2521-2537
<![CDATA[The development of electric vehicle (EV) charging technology requires efficient, reliable, and economical systems to address users' concerns about battery drain. This study presents a simplification of EV charger design with an isolated model and optimal battery mode setting. The research method integrates step-up Y-Δ transformers, AC-DC converters, boost DC-DC converters, integral proportional control, and battery configurations. Series (S) - parallel (P) - series (S) battery arrangement pattern to maximize system performance. The test results using a 130 mF capacitor with the S40-P2-S6 and S80-P2-S3 array patterns produced an output voltage of 946 V, while the S100-P2-S3 array pattern achieved an output voltage of 1,182 V. The system is capable of fast charging with a time of 0.2 to 2 hours for a battery capacity of 30 to 100 kWh at a charging power of 50 to 150 kW with an efficiency of up to 97%. The combination of the use of an isolated model on the charger array and the EV battery setting pattern is proven to produce stable voltage values with minimal overshoot levels, thus addressing the complex charger design challenges and battery setting needs in the 800 to 1,100 V voltage range.]]>
<![CDATA[Dehydration of Moringa leaves using microcontroller and IoT controlled electrical dryer ]]>
<![CDATA[Jalil, Saifuddin Muhammad]]>;
<![CDATA[Dabet, Abubakar]]>;
<![CDATA[Akmal, Syarifah]]>;
<![CDATA[Meliala, Selamat]]>;
<![CDATA[Muhammad, Muhammad]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v16.i4.pp2688-2698
<![CDATA[The dehydration of Moringa Oleifera leaves is crucial to preserving their high nutritional value and extending shelf life for use in food and pharmaceutical applications. Traditional drying methods often result in nutrient degradation and lack precise environmental control. This study presents the design and implementation of an internet of things (IoT)- enabled electrical dryer system controlled by a microcontroller for the efficient dehydration of Moringa leaves. The system integrates temperature and humidity sensors, an Arduino Mega microcontroller, and a web-based interface for real-time monitoring and control. The electrical dryer maintains optimal drying conditions, significantly reducing moisture content while preserving essential nutrients. Data is logged and visualized through IoT connectivity, allowing for remote access and performance analysis. The dehydration of Moringa leaves requires approximately one kg of electricity for batteries in dual-energy dryers, which are based on microcontrollers and the IoT. The results demonstrate that the proposed system offers a reliable, energy-efficient, and scalable solution for the controlled dehydration of Moringa leaves, with potential applications in smart agriculture and postharvest processing. The excellent drying time is achieved in a greenhouse dryer, which maintains a temperature of 45 °C within the drying chamber, resulting in a median drying time of 6 hours. The standard moisture percentage of clean and dry Moringa leaves is measured at 18.5% (wb) and 8% (wb), respectively.]]>
<![CDATA[Approach to self-synchronization of a group of static power converters ]]>
<![CDATA[Lavrinovsky, Victor]]>;
<![CDATA[Dobroskok, Nikita]]>;
<![CDATA[Bulychev, Valery]]>;
<![CDATA[Migranov, Ruslan]]>;
<![CDATA[Perevalov, Yuriy Yu]]>;
<![CDATA[Stotckaia, Anastasia]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v16.i4.pp2342-2352
<![CDATA[This study examines the control and synchronization of an orderly connected network of three-phase bidirectional power converters, serving as the grid interface for an energy storage system. The primary objective is to ensure stable operation under single-phase and non-symmetrical three-phase grid conditions. The control employs independent phase voltage regulation for compatibility. To achieve seamless coordination of an unlimited group of converters, the paper proposes a synchronization method based on a modified Kuramoto model. This method is designed to be compatible with independent phase control during asymmetric grid states. The proposed approach utilizes a structured connection graph, defined by phase shift magnitude, to synchronize the converter group. A brief overview of the tools for synchronizing oscillator groups is provided. A computer model was developed to study the operating modes of this converter class under both symmetrical and asymmetrical loads. Simulation studies confirmed the viability of the synchronization method. Furthermore, the research results were successfully applied in the design and implementation of a physical 10 kW grid - connected uninterruptible power supply prototype, demonstrating practical feasibility.]]>
<![CDATA[Machine learning-based energy management system for electric vehicles with BLDC motor integration ]]>
<![CDATA[Prasad, K. S. R. Vara]]>;
<![CDATA[Reddy, V. Usha]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v16.i4.pp2400-2410
<![CDATA[This paper proposes a machine learning-based energy management system for electric vehicles with BLDC motor integration. Efficient energy management is essential for improving the performance, range, and reliability of electric vehicles (EVs), particularly those powered by brushless DC (BLDC) motors. Traditional energy management systems (EMS), such as rule-based and fuzzy logic controllers, often lack the adaptability required for dynamic driving conditions and optimal energy distribution. This paper presents a machine learning (ML)-based EMS framework tailored for EVs equipped with BLDC motors, aiming to enhance system responsiveness and energy efficiency. ML algorithms, including decision trees, random forests, support vector machines (SVMs), and XGBoost, are trained on diverse datasets that reflect varying load demands, driving cycles, and battery state-of-charge (SOC) levels. The proposed EMS is modeled and validated in Python programming to simulate realistic EV operating scenarios. Simulation results indicate that the ML-based EMS outperforms conventional methods by achieving up to 15% energy savings, reducing battery stress, and maintaining smoother SOC transitions. These findings highlight the potential of ML-driven strategies for creating adaptive, intelligent EMS solutions in next-generation BLDC motor-based EVs.]]>
