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[Development of a PEM fuel cell equivalent circuit model with PINN-based parameter identification ]]>
<![CDATA[Taleb, Ismail Ait]]>;
<![CDATA[Kourab, Zakaria]]>;
<![CDATA[Tayane, Souad]]>;
<![CDATA[Ennaji, Mohamed]]>;
<![CDATA[Gaber, Jaafar]]>
<![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.pp2804-2818
<![CDATA[This paper presents a novel equivalent electrical circuit model for proton exchange membrane fuel cells (PEMFCs) and introduces a physics-informed neural network (PINN) algorithm for parameter identification. The proposed model provides a more accurate representation of the fuel cell’s dynamic behavior while maintaining computational efficiency. Unlike conventional methods, the PINN framework integrates physical constraints with data-driven learning, ensuring physically consistent parameter estimation. To validate its effectiveness, the proposed model is compared with the widely used RC equivalent circuit and a generic PEMFC model. Experimental data from a 1.2 kW PEMFC test bench serve as a benchmark for evaluating the transient and steady-state performance of each modeling approach. Results demonstrate that the proposed circuit, combined with PINN-based identification, yields enhanced accuracy in predicting voltage response under various operating conditions. Additionally, the model exhibits improved adaptability to transient phenomena compared to conventional equivalent circuits. These findings highlight the potential of physics-informed machine learning for advancing fuel cell modeling and control strategies.]]>
<![CDATA[Optimization of solar panel orientation and tracking systems for standalone PV applications ]]>
<![CDATA[Prasetyo, Singgih Dwi]]>;
<![CDATA[Trisnoaji, Yuki]]>;
<![CDATA[Munir, Misbahul]]>;
<![CDATA[Permatasari, Meirna Puspita]]>;
<![CDATA[Mahadi, Abram Anggit]]>;
<![CDATA[Rizkita, Marsya Aulia]]>;
<![CDATA[Arifin, Zainal]]>
<![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.pp2721-2730
<![CDATA[The performance of photovoltaic (PV) systems is greatly influenced by the angle of arrival of sunlight and the geometric orientation of solar panels, especially in tropical regions with the potential for solar energy throughout the year. This study aims to evaluate the effect of tilt angle variation and tracking systems on energy output and performance indicators of standalone PV systems using PVsyst software. The simulation was conducted at the State University of Malang, Indonesia, by comparing four fixed-angle configurations (20°, 40°, 60°, and 80°) as well as a two-axis tracking system. The simulation results showed that the two-axis tracking system produced the highest normalized daily energy production of 6.8 kWh/kWp/day, with a performance ratio (PR) of 77.2% and a solar fraction (SF) of 97.1%, while a fixed configuration with an angle of 80° showed the lowest performance. These findings confirm the importance of selecting optimal panel orientation to maximize the efficiency of PV systems, as well as being the basis for the development of advanced research, such as field-based experiments, integration of adaptive MPPT algorithms, and economic feasibility studies in the application of PV systems in tropical and off-grid regions.]]>
<![CDATA[Numerical and experimental state of identification battery pack lithium-ion ]]>
<![CDATA[Anggraeni, Dewi]]>;
<![CDATA[Sudiarto, Budi]]>;
<![CDATA[Nasser, Eriko Nasemudin]]>;
<![CDATA[Hasbi, Wahyudi]]>;
<![CDATA[Natali, Yus]]>;
<![CDATA[Priambodo, Purnomo Sidi]]>
<![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.pp2623-2633
<![CDATA[Two key indicators of a battery management system (BMS) are the state of charge (SoC) and the state of health (SoH). Accurately estimating SoC is important to prevent potential issues. Additionally, space, computing time, and cost are important factors in hardware development. To address these considerations, the first-order extended Kalman filter (EKF) and adaptive extended Kalman filter (AEKF) models were selected due to their simpler data pre-processing and better accuracy. The study recommends using the first-order equivalent circuit model (ECM) method in conjunction with the EKF and AEKF algorithms due to their straightforward setup and efficient computational process. Analysis of the charge-discharge cycles shows that the AEKF method consistently outperformed the EKF method regarding SoC accuracy. Moreover, when given different initial SoC values, the AEKF method displayed superior SoC estimation accuracy compared to the EKF method. Moreover, while the accuracy of the EKF is diminished, the error value remains below 2.5% for up to 500 cycles. Additionally, the shorter computing time of the EKF method is a consideration for practical real-world implementation. Furthermore, experiments conducted over 500 cycles revealed that SoH estimation declined from 99.97% to 76.1947%, suggesting that the battery has reached the end of life (EOL) stage.]]>
<![CDATA[Lithium-ion battery charge-discharge cycle forecasting using LSTM neural networks ]]>
<![CDATA[Srikantappa, Vimala Channapatana]]>;
<![CDATA[Devarakonda, Seshachalam]]>
<![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.pp2831-2840
<![CDATA[An important component for the dependable and safe utilization of lithium-ion batteries is the ability to accurately and efficiently predict their remaining useful life (RUL). In this research, a long short-term memory recurrent neural network (LSTM RNN) model is trained to learn from sequential data on discharge capacities across different cycles and voltages. The model is also designed to function as a cycle life predictor for battery cells that have been cycled under varying conditions. By leveraging experimental data from the NASA battery dataset, the model achieves a promising level of prediction accuracy on test sets consisting of approximately 200 samples.]]>
<![CDATA[Step size variability with high performance solar-wind grid integration using MPPT algorithm ]]>
<![CDATA[Dhandapani, Lakshmi]]>;
<![CDATA[Sreenivasan, Pushpa]]>;
<![CDATA[Batumalay, Malathy]]>
<![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.pp2655-2664
<![CDATA[This paper proposes a high-efficiency maximum power point tracking (MPPT) algorithm based on a variable step size control technique for standalone hybrid solar-wind energy systems. Unlike conventional approaches that utilize separate MPPT controllers for photovoltaic (PV) and wind systems, the proposed method integrates a single adaptive control strategy that simultaneously optimizes power extraction from both renewable sources. The algorithm dynamically adjusts the step size according to environmental variations, improving convergence speed and tracking accuracy. The system is modeled in MATLAB/Simulink, incorporating a 500 W solar PV system and a 560 W wind turbine, both interfaced through traditional boost converters. To validate the performance, simulations are conducted under varying solar irradiance levels (600 W/m², 800 W/m², and 1000 W/m²) and wind speeds (8 m/s, 10 m/s, and 12 m/s). Results indicate that the PV output power increases from 288.8 W to 513 W with rising irradiance, while the wind output improves from 301.4 W to 439.3 W with increasing wind speed. The combined hybrid system achieves total output powers of 557.35 W, 691.74 W, and 807.12 W across three operating intervals. These findings confirm that the proposed variable step size MPPT algorithm significantly enhances energy harvesting efficiency and system performance under dynamic environmental conditions.]]>
<![CDATA[Combination circuit of multilevel inverter, matrix converter, and H-bridge ]]>
<![CDATA[Al-Mahrouk, Akram Mohammed]]>;
<![CDATA[Mailah, Nashiren Farzilah]]>;
<![CDATA[Radzi, Mohd Amran Mohd]]>;
<![CDATA[Hassan, Mohd Khair]]>
<![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.pp2476-2490
<![CDATA[In this study, a new integrated circuit design called H-bridge multilevel inverter matrix converter (HMIMC), which combines a multilevel inverter (MI), a matrix converter (MC), and an H-bridge circuit, is developed. It aims to generate a high number of output voltage levels that reduce the component count (CC). The MI step is used to control the positive voltage source, where the output of MI is connected to the input of MC. The MC is used to share the positive input voltage due to output phases, depending on the requirements. Afterward, the H-bridge circuit is used in each phase to select the positive or negative output voltage. The main contribution of this design is that the MI does not need to be repeated thrice to produce a three-phase output voltage. A seven-level (7L) and thirteen-level (13L) of proposed circuit is presented, followed by a new algorithm operation that is used for suitable switching control. Afterward, MATLAB simulation is used to check the operation process, output signals of voltage and current, and total harmonic distortion (THD) results. Then hardware circuit of the proposed system is implemented to verify the design. Lastly, a brief comparison in terms of CC is conducted.]]>
<![CDATA[Interleaved buck converter using a floating dual series-capacitor topology ]]>
<![CDATA[Nguyen, Chan Viet]]>;
<![CDATA[Nguyen, Dang Tai]]>;
<![CDATA[Ho, Thanh Phuong]]>
<![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.pp2538-2548
<![CDATA[Interleaved buck converters (IBC) are widely utilized in step-down voltage applications due to their excellent performance and straightforward design. However, conventional IBCs require individual current sensors and feedback control circuits to maintain phase current balance, resulting in increased cost and design complexity. In this paper, a novel floating dual series capacitor (FDSC) converter based on an interleaved floating structure is proposed. The most distinctive aspect of this proposed converter is its ability to naturally balance the four inductor currents without the need for any current sensors or feedback control. Furthermore, the proposed converter also exhibits lower voltage stress on switching devices and inductors, contributing to improved efficiency and a reduction in overall magnetic volume. To validate the performance characteristics of the proposed converter, a 1.3 kW prototype of the FDSC topology was developed and tested to indicate the analytical results and demonstrate stable current balance even under different operating conditions. The experimental validation highlights the topology’s suitability for high step-down, compact, and efficient applications such as EV auxiliary power supply and voltage regulator modules.]]>
<![CDATA[Comparative analysis of optimization techniques for optimal EV charging station placement ]]>
<![CDATA[Somasundaram, Deepa]]>;
<![CDATA[Prakash, G.]]>;
<![CDATA[Rajavinu, N.]]>;
<![CDATA[Lakshmi, D.]]>;
<![CDATA[Kavitha, P.]]>;
<![CDATA[Devaraj, V.]]>
<![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.pp2860-2867
<![CDATA[The optimal placement of electric vehicle (EV) charging stations plays a crucial role in improving accessibility, reducing travel distances, and minimizing infrastructure costs in smart urban planning. This study presents a comparative analysis of traditional optimization techniques-such as linear programming (LP), particle swarm optimization (PSO), k-means clustering, and greedy heuristic methods-alongside a machine learning-based approach using genetic algorithms (GA). A machine learning framework is implemented to simulate EV charging demand, optimize station deployment, and incorporate real-world constraints like cost, grid capacity, and user travel penalties. The results demonstrate that GA achieves superior performance in balancing cost-efficiency and user convenience, outperforming traditional techniques in solution quality under dynamic demand conditions. PSO and LP provide faster convergence but are less adaptive to changing parameters. The study highlights the potential of integrating machine learning into infrastructure planning and provides actionable insights for urban planners and policymakers in developing resilient and intelligent EV charging networks.]]>
<![CDATA[Backstepping control in speed loop combined with load torque observer-ESO for IPMSM in electric vehicle ]]>
<![CDATA[Anh, An Thi Hoai Thu]]>;
<![CDATA[Cuong, Tran Hung]]>;
<![CDATA[Hoa, Nguyen Van]]>
<![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.pp2271-2279
<![CDATA[Electric vehicles are gaining popularity due to their environmental friendliness and the need to conserve dwindling fossil fuel resources. In this field, interior permanent magnet (IPM) motors are considered the top choice for propulsion systems due to their high efficiency, high torque-to-current ratio, durability, and low noise. To optimize the speed control performance of IPM motors in the presence of disturbances, a nonlinear speed control algorithm for IPM systems using the backstepping method is developed in this paper. Additionally, a load torque observer using the extended state observer (ESO) method is implemented to enable the system to respond quickly and accurately to load changes while minimizing the effects of disturbances, thereby enhancing the operation and reliability of electric vehicles. The simulation results, conducted in MATLAB/Simulink, demonstrate that the combination of backstepping control and ESO offers good stability for the motor system, while mitigating the impact of disturbances and load variations. This is an important step in optimizing the control system of electric vehicles, contributing to the improvement of performance and reliability in electric vehicle applications.]]>
<![CDATA[Speed control of 3-phase induction motor with modified DTC using HTAF-ANN ]]>
<![CDATA[Banik, Arpita]]>;
<![CDATA[Gandhi, Raja]]>;
<![CDATA[Kumar, Chandan]]>;
<![CDATA[Mishra, Achyuta Nand]]>;
<![CDATA[Roy, Rakesh]]>
<![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.pp2197-2211
<![CDATA[In this research paper, an artificial neural network (ANN) algorithm is implemented with modifications to enhance the performance of a direct torque controlled (DTC) induction motor drive. Since the main challenge in the conventional DTC technique is to tune the PI controller appropriately therefore in this work, an ANN technique is incorporated in place of the conventional PI controller. Sudden changes in speed and loading in induction motor drives lead to sharp fluctuations and disturb the motor performance. In order to overcome these issues, a trained ANN controller is initially used here to enhance motor drive performance. Subsequently, the performance is further improved by modifying the activation function in the ANN controller. Here, motor parameters at rated and variable speed with various loading conditions have been analyzed and compared for the DTC with a conventional PI controller with ANN, and a proposed ANN controller. Simulation of the complete model with the conventional and proposed controllers is done using MATLAB/Simulink platform to observe the various speed responses for different conditions, and the experimental setup is used to demonstrate the effectiveness and performance of the proposed system.]]>
