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International Journal of Power Electronics and Drive Systems (IJPEDS)
Published by Institute of Advanced Engineering and Science
ISSN : -     EISSN : 20888694     DOI : -
Core Subject : Engineering,
Control & Systems Engineering Electrical & Electronics Engineering
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.
Arjuna Subject : -
Articles 2,781 Documents
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Adaptive forget-gated BiLSTM enhanced by DTW based feature selection in solar PV forecasting Are Sambasiva Rao; Kunada Dhana Sree Devi
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp2086-2100

Abstract

Growing exhaustion of fuel reserves and their harmful environmental impacts have driven the shift towards the maintenance of renewable energy sources like solar energy. PV systems, however, still face significant challenges while integrating renewable sources into existing utility systems. Particularly, environmental features like temperature, irradiance, and humidity are not perfectly well coordinated with the power output, are always asynchronous in nature, and affect the power prediction considerably. Close observation of energy datasets from many PV plants revealed many intrinsic environmental variables that are highly asynchronous. Many forecasting models learn redundant features which might seem useful, and thereby the test performance is overfitting. To address the nonlinear and asynchronous behavior of environmental variables, there is a serious requirement for intelligent feature selection algorithms guided by both correlation and temporal alignment metrics. This research proposes a novel adaptive dynamic time warping (A-DWT) feature selection with an adaptive forget gate (AFG-BiLSTM) to address the asynchronous issues. Experiments were conducted with varied environmental asynchronous features, and the results of the proposed model were compared with traditional BiLSTM and stacked BiLSTM models. In all the experiments, the proposed model showed decreased error by (94.9%) on Dataset-1 (0.069, 0.0035), by (94.6%) on Dataset-2 (0.078, 0.0042), and by (90.9%) on Dataset-3 (0.069, 0.0061) when compared to stacked BiLSTM. The MSE loss of the proposed method was observed to be between (0.3%) and (11%) on four datasets.
Evaluation of hybrid and standalone learning models for predicting lithium-ion battery capacity degradation Shobana Devendiren; A. Muthuraman; M. Vanitha; I. Arul Doss Adaikalam; R. Kalaivani; P. Kavitha
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp1581-1590

Abstract

The prediction of lithium-ion battery capacity degradation plays a vital role in ensuring safe and efficient operation in electric mobility and renewable energy applications. This paper evaluates standalone machine learning, deep learning, and hybrid models for battery capacity estimation. The evaluated ML models include random forest, gradient boosting, and extreme gradient boosting (XGBoost), while the DL model employs a multilayer perceptron. The hybrid framework combines DL based feature extraction with ensemble ML regression or classification. A real-world dataset comprising temperature, resistance, reactance, and battery type was preprocessed, scaled, and divided into training and testing subsets. Hyperparameter tuning, k-fold cross-validation, and uncertainty quantification were incorporated to improve reliability and reproducibility. Model performance was assessed using RMSE, MAE, and R² for regression and receiver operating characteristic–area under the curve (ROC-AUC) and F1-score for classification. ROC curves, calibration curves, metric-comparison charts, cycle-wise degradation plots, and residual analyses were used for evaluation. Results demonstrate that the hybrid model outperforms standalone approaches by reducing RMSE and improving calibration, reliability, uncertainty alignment, and interpretability. This also establishes its novelty over existing state of health (SOH) models and highlights future extensions involving LSTM-based temporal modeling and chemistry-adaptive transfer learning. Overall, hybrid modeling provides a promising solution for reliable predictive battery maintenance.
Energy optimization of an electric vehicle charging station using a hybrid STA-GWO MPPT strategy Samia Amrouni; Said Aissou; Rafik Medjoudj; Elyazid Amirouche; Nabil Benyahia; Abdelhakim Belkaid
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp2183-2196

Abstract

This paper presents a hybrid electric vehicle charging station powered by both a PV source and the utility grid, incorporating an energy management strategy that prioritizes the utilization of solar energy while exporting surplus power to the grid during periods of low charging demand. To enhance the performance of maximum power point tracking, a hybrid control strategy integrating the grey wolf optimizer (GWO) and the super-twisting algorithm (STA) is proposed. The GWO performs rapid global exploration to accurately identify the maximum power point, whereas the STA ensures precise, robust, and chattering-free tracking under steady-state operating conditions. The proposed system was modeled in MATLAB/Simulink and validated under a dynamic irradiance profile characterized by both abrupt and gradual variations. Simulation results demonstrate a convergence time of 2-3 ms, residual power oscillations below 0.1%, and an average tracking efficiency of 99.38%. Compared with conventional MPPT techniques, the proposed STA-GWO approach significantly suppresses steady-state oscillations, accelerates convergence, and prevents MPP tracking failure under rapid irradiance fluctuations through the global optimization capability of GWO. These findings highlight the effectiveness of the proposed hybrid MPPT strategy in improving the robustness, energy conversion efficiency, and grid integration capability of PV-powered EV charging stations, making it a promising solution for next-generation sustainable charging infrastructure.
Comparative simulation of fractional-order PD sliding mode and fuzzy logic controllers for a second-order discrete-time nonlinear system Ahmed Bennaoui; Salah Benzian; Hamza Sulimani; Aissa Ameur
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp1822-1830

Abstract

Tight output regulation in power converters and electric drive systems requires a control strategy that simultaneously minimizes tracking error and maintains smooth actuation-two objectives that are intrinsically in tension for nonlinear, parameter-varying plants. Despite the widespread deployment of fractional-order PD sliding mode control (FOPD-SMC) and Mamdani fuzzy logic control (FLC) in this domain, no prior study has placed them in a direct, metric-identical comparison on a common plant. The present work closes this gap by implementing both controllers on the same second-order discrete-time nonlinear plant-representative of DC-DC converter output dynamics and motor-drive input-output behavior and evaluating them under a composite reference that combines sinusoidally-modulated ramps with step transitions, scored by the integral of squared error (ISE) and integral of absolute error (IAE). FOPD-SMC achieves ISE= 1.639 × 10-2 and IAE= 3.345 × 10-2, outperforming FLC by 87.6% and 50.4%, respectively; the advantage originates from the non-integer memory embedded in the sliding surface via the Gr¨unwald-Letnikov operator and from the explicit decomposition of the control law into nominal-tracking and robustness components. FLC, conversely, produces a chattering-free, continuously varying control signal a structural consequence of smooth Gaussian membership functions and linguistic rule aggregation, at the cost of a mean absolute tracking error twice that of FOPD-SMC. These findings establish a quantitative selection criterion: FOPD-SMC is recommended when tight voltage or current regulation is the primary objective, while FLC is preferred where smooth torque delivery and reduced actuator stress outweigh marginal gains in tracking accuracy.
Adaptive electromagnetic interference mitigation for wide-bandgap power converters: a review Md Shishir Rahman; Siti Mahfuza Saimon; Shahrin Md Ayob; Muhammad Yusof Mohd Noor
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp1941-1949

Abstract

Wide-bandgap (WBG) semiconductor devices such as silicon carbide (SiC) and gallium nitride (GaN) enable higher switching frequencies, greater efficiency, and increased power density, but their fast-switching transients and steep dv/dt and di/dt characteristics generate substantially elevated electromagnetic interference (EMI). This review synthesizes peer-reviewed studies to quantitatively compare EMI mitigation outcomes across source-side modulation, gate-driving, filtering, packaging, and AI-based techniques for WBG converters. Packaging-integrated common-mode screens cut CM current by up to 26 dB while raising partial-discharge inception voltage by 53%. Chaotic PWM with passive filtering reaches up to 50 dB attenuation with a 74% reduction in inductor volume. AI-based closed-loop adaptation attains up to 19.2 dB average attenuation with 98.5% CISPR 25 compliance, while RL-based filters reach 25-30 dB across wide frequency ranges. Active gate-driving reduces peak EMI by 19-39 dB. No single technique simultaneously delivers the highest suppression, efficiency, and lowest cost. Hybrid Si/WBG design currently offers the most balanced trade-off. These outcomes are consolidated into a taxonomy of propagation mechanisms, mitigation techniques, application-specific strategies across five domains, and open gaps in standardized testing and validation. These findings provide a practical, quantitative reference for engineers designing next-generation, EMC-compliant WBG power electronic systems.
Lyapunov controller design for a fuel cell electric vehicle powered by a three-level floating capacitor boost converter Chaouqi Aouadi; Abdelmajid Abouloifa; Meriem Aourir; Ibtissam Lachkar
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp1620-1629

Abstract

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.
Hybrid active filter control for harmonic reduction in quadratic boost converter-integrated grid systems S. Venkata Ramana Rao; Mahiban Lindsay
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp1564-1580

Abstract

This article examines the proportional-integral (PI) and fractional-order proportional-integral-derivative (FOPID) controllers for a quasi-buck converter-hybrid active filter (QBC-HAF) to improve the harmonic suppression and power quality of a hybrid energy storage system (HESS) fed to the grid. This system uses a high-gain QBC, which is connected to a VSI to control the DC-link voltage and to allow controlled energy transfer from photovoltaic and battery sources. A MATLAB/Simulink test bed and hardware prototype were developed to validate performance. The results demonstrate the enhanced stability and quality of the output waveforms for both controllers, with the FOPID providing better harmonic suppression, reducing output current total harmonic distortion (THD) from 4.34% to 4.14% and output voltage THD from 4.75% to 4.42%. The simulation results were validated using experimental tests, showing that the FOPID-controlled QBC-HAF system is suitable for power quality improvement, voltage stabilization, and dynamic performance in renewable integrated grids.
Optimization of EV battery charging for temperature control R. J. Vijaya Saraswathi; Krishnakumar Vengadakrishnan; Vasan Prabhu Veeramani; S. Kamalakkannan
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp1601-1609

Abstract

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.
Accurate RUL prediction of EV batteries using random forest and ensemble learning frameworks Ponkumar Ganesapandiyan; P. Hemachandu; N. Rajavinu; M. Bhoopathi; P. Kavitha; R. Kalaivani
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp2291-2300

Abstract

Precise remaining useful life (RUL) estimation for lithium-ion batteries is essential for improving the safety, reliability, and maintenance of electric vehicles (EVs). This study proposes a random forest (RF)-based ensemble learning framework using the publicly available Hawaii Natural Energy Institute (HNEI) dataset containing 15,064 charge-discharge cycles. Seven degradation-related features, including cycle index, discharge time, voltage decrement, maximum discharge voltage, minimum charging voltage, time at 4.15 V, and constant-current charging duration, are extracted to characterize battery aging. The proposed RF model is compared with linear regression (LR), long short-term memory (LSTM), and attention-LSTM models using MAE, root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²). RF demonstrates superior prediction performance, achieving MAE of 5.20, RMSE of 6.83, MAPE of 1.33%, and R² of 0.997. Parity and residual analyses further confirm its strong predictive consistency. The proposed approach provides an accurate, computationally efficient, and interpretable solution for BMS applications, enabling effective battery health monitoring, predictive maintenance, charging optimization, and timely replacement.
Design and analysis of modified single switch AC-DC bridgeless Cuk converter Abdullah Sahib Abdulsada; Hassan Wahhab Salih; Nasir Hussein Selman
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp1902-1913

Abstract

The Cuk converter is widely used in power electronics due to its ability to provide both step-up and step-down voltage conversion with low ripple characteristics. However, conventional designs suffer from increased component count, higher losses, and significant voltage stress on switching devices. In this paper, an improved single-switch bridgeless AC-DC Ćuk converter is proposed for power factor correction (PFC) and loss reduction. The proposed topology employs a two-stage integrated structure with two low-value coupling capacitors (1 µF each). These capacitors effectively reduce voltage stress across the main switch and enhance switching performance. The operating principles of the proposed converter are analyzed under different switching conditions, highlighting its improved dynamic behavior and reduced component stress. Simulations using MATLAB/Simulink show that the proposed design achieves high efficiency, very low output voltage ripple (less than 1%), and a near-unity power factor of 0.9988. This performance, particularly the fast dynamic response, was achieved using a specially designed proportional-integral (PI) controller for output voltage regulation. The converter demonstrates fast dynamic response with a settling time of less than 0.23 s and a maximum overshoot of 7.5% under sudden load and input variations. Compared to conventional two-switch topologies, the proposed design significantly reduces switch voltage stress while maintaining a simpler structure, leading to lower cost and compact size. These results confirm that the proposed converter is a promising solution for high power quality applications.

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