International Journal of Power Electronics and Drive Systems (IJPEDS)
The International Journal of Power Electronics and Drive Systems (IJPEDS), p-ISSN: 2088-8694, e-ISSN: 2722-256X, 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, electric drives, and energy 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; integrated and packaged; induction motor drives; synchronous motor drives; synchronous motor drives; permanent magnet motor drives; ASDs (adjustable speed drives); multi-phase machines and converters; applications in motor drives; electric vehicles; wind energy systems; solar; battery chargers; uninterruptible power supplies (UPS); and other applications. The IJPEDS uses a rolling submission process, allowing authors to submit at any time during the year without time restraints. This journal is a SCOPUS and ScimagoJR indexed journal, CiteScore: 3.2, SJR: 0.270, and SNIP: 0.639. POWER UP YOUR RESEARCH: Amplify Your Impact in Power Electronics and Drive Systems It conveys the journal's dedication to empowering researchers to enhance their research, increase their influence, and contribute to the advancement of power electronics and drive systems. IJPEDS Journal positions as a platform that supports and amplifies the impact of research in this field. Get to Know Us Better BECOMING AN AUTHOR SUBMISSION TEMPLATE SUBMISSION GUIDELINES IJPEDS POLICIES MEET OUR EDITOR CONTACT US
Institute of Advanced Engineering and Science
Design and implementation of digital logic for brushless DC motor control in electric vehicles
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.
Copyright (c) 2025 Belwin J. Brearley, K. Regin Bose, K. Ganesh Kumar http://creativecommons.org/licenses/by-sa/4.0
A comprehensive review of efficient wireless power transfer for electric vehicle charging: advancements, challenges, and future directions
Electric vehicles (EVs) have transformed the transportation sector, offering a sustainable alternative to fossil-fuel-powered vehicles. However, their widespread adoption faces challenges such as inadequate charging infrastructure, range anxiety, and concerns about user convenience. Wireless power transfer (WPT) technology provides an efficient, reliable, and user-friendly charging solution that eliminates physical connections, enabling both static and dynamic charging applications. This review explores key components of WPT systems, including wireless charging schemes, compensation circuits, coupling pad structures, and misalignment tolerance, emphasizing their impact on system efficiency and reliability. Findings highlight that WPT can enhance charging convenience, reduce dependence on large battery capacities, and support seamless EV integration into daily life. Additionally, WPT systems improve safety, lower maintenance needs, and create opportunities for autonomous charging. Key advancements in compensation topologies, coupling pad geometries, and misalignment-tolerant capabilities are discussed alongside their role in enhancing power transfer efficiency. By offering insights into the current state-of-the-art and future directions, this paper aims to support the development and deployment of WPT systems, contributing to the global transition toward sustainable transportation.
Copyright (c) 2025 Md. Ashraf Ali Khan, Kuber, Yusra Wahab, M. Saad Arif, Shahrin Md. Ayob, Norjulia Mohamad Nordin http://creativecommons.org/licenses/by-sa/4.0
Design and implementation of IoT-based soft starter for induction motor
The practical application of the induction motor is an essential part of electrical engineering. A direct connection of the motors to the mains voltage negatively affects both the motor itself and the mains system as a whole due to high starting current values, as a result, more accidents and shortening the drive system service life. This article discusses the development of designing and implementing of soft starter single-phase IM to reduce the inrush current using the firing angle reduction technique with remote monitoring and control using the ESP32 (node MCU) and Arduino Due microcontrollers. The integration of IoT-based tools software such as VS Code, enables the remote monitoring and control of motor features. Testing shows that the system effectively facilitates remote motor control, providing a flexible and accessible learning environment with minimum starting current, solving the inrush current problem facing IMs. The proposed soft starter gives three cases of firing angle reduction that show a percentage reduction in starting current for these cases (case I, case II, and case III) are 51%, 54% and 64%, respectively. Case III has a maximum starting current is 2.2 A compared to 6.2 A for direct connecting of IM to the power supply (DOL).
Copyright (c) 2025 Laith Najem Abood Khudhur, Amer Abdulmahdi Jabbar Chlaihawi http://creativecommons.org/licenses/by-sa/4.0
Fault diagnosis for inverter open circuit faults using DC-link signal and random forest-based technique
Three-phase voltage source inverters based on insulated-gate bipolar transistors (IGBTs) are widely used in various industrial applications. Faults in IGBTs significantly affect the performance of the inverter and entire system. Robust and accurate fault detection are the key requirements of fault diagnosis methods. This paper explores a method for diagnosing power switch open circuit faults of a voltage source inverter based on machine learning algorithms. The diagnosis is performed in two steps, firstly the fault is detected by applying the Random Forest classifier algorithm with the DC-link signal. Next, the fault switch location is performed by additionally using the inverter output AC current signals. The diagnostic results based on simulation data show that the fault can be detected with maximum accuracy. Meanwhile, the accuracy in locating the fault switch is also significantly improved with the additional use of current signals measured at the DC-link. Potential application of electromagnetic field signal is also highlighted for the practical implementation of fault diagnosis.
Copyright (c) 2025 Hoang-Giang Vu, Dang Toan Nguyen http://creativecommons.org/licenses/by-sa/4.0
Adaptive fuzzy logic controller based BLDC motor to improve the dynamic performance for electric tractor application
Permanent magnet brushless DC (PMBLDC) motors are widely used in a variety of industrial applications due to their high-power density and ease of regulation. The three-phase power semiconductors bridge is the standard way for controlling these motors. In order to initiate the inverter bridge and switch on the power devices, rotor position sensors must be provided with the correct commutation sequence. The power devices commutate progressively 60 degrees, depending on the location of the rotor. The right speed controllers are necessary for the motor to run as efficiently as possible. PI controllers are commonly employed with permanent magnet motors to achieve speed control in simple manner. Nevertheless, these controllers provide challenges in managing control complexity, including nonlinearity, parametric fluctuations, and load disturbances. PI controllers need accurate linear mathematical models. To overcome this, in this paper adaptive fuzzy logic controller (FLC) for controlling the speed of a BLDC motor is presented. When the motor drive system uses the adaptive FLC technology for speed control, it exhibits better dynamic behavior and is more resistant to changes in parameters and load disturbances. The main objectives of this work are to analyze and appraise the functioning of an electric tractor driven by a PMBLDC motor drive using adaptive FLC. The PMBLDC motor drive controllers are simulated using MATLAB/Simulink software.
Copyright (c) 2025 Ashwini Yenegur, Mungamuri Sasikala http://creativecommons.org/licenses/by-sa/4.0
Speed control of 3-phase induction motor with modified DTC using HTAF-ANN
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.
Copyright (c) 2025 Arpita Banik, Raja Gandhi, Chandan Kumar, Achyuta Nand Mishra, Rakesh Roy http://creativecommons.org/licenses/by-sa/4.0
Modelling and analysis of linear DC motor with constant thrust characteristics
This paper introduces a portable and user-friendly innovation in food processing by replacing traditional molding methods with a linear DC motor (LDM). Traditional methods, which involve manual pressing, are energy-intensive and time-consuming, reducing productivity. The proposed LDM offers a simple, cost-effective, and robust solution capable of producing constant thrust, unlike conventional LDMs that require complex and expensive control methods and are limited to short displacements. The research focuses on modelling and analyzing an LDM with constant thrust characteristics for food processing applications. The primary objective is to model the thrust using the permeance analysis method (PAM), ensuring constant thrust capability. Verification was conducted using the finite element method (FEM) and measurement results, showing a percentage difference of 1.7% and 6.5%, respectively, between PAM and the other methods. The study provides valuable guidance for designing LDMs with constant thrust capabilities, enhancing the efficiency and practicality of food processing devices.
Copyright (c) 2025 Raja Nor Firdaus Kashfi Raja Othman, Siti Zulaika Mat Isa, Nor Aishah Md. Zuki, Suhairi Rizuan Che Ahmad, Fairul Azhar Abdul Shukor, Md. Nazri Othman http://creativecommons.org/licenses/by-sa/4.0
Comparative analysis of various rotor types BLDC motor for residential elevator application
Brushless DC (BLDC) motors are widely used in applications where high efficiency is crucial. With advancements in permanent magnet technology, BLDC motors are increasingly suitable for high-torque applications such as residential elevators. Known for their high efficiency, low maintenance, and excellent controllability, BLDC motors are ideal candidates for this research. However, the challenge lies in identifying the most efficient rotor structure that can deliver the required torque for residential elevator applications while maintaining cost-effectiveness and compact design. This paper addresses this problem by simulating various rotor types of BLDC motors using the finite element method (FEM), Ansys Maxwell. four different rotor structures have been analyzed to evaluate their back electromotive force (EMF) and torque. The model generating the highest torque will be selected for manufacturing as a motor for residential elevators. Among the models studied, BLDC-ERA rotor structures produced the highest torque of 28 Nm, while BLDC-HR type generates the lowest torque. To ensure practicality and cost-effectiveness of installing elevators in double-story houses or smaller residences, the selected motor must be compact and affordable, enabling senior citizen to maintain their independence. This research not only aids other researchers in designing suitable motors for elevator applications but also contributes to societal well-being by promoting accessibility and independence for the elderly.
Copyright (c) 2025 Nor Aishah Md. Zuki, Raja Nor Firdaus Kashfi Raja Othman, Fairul Azhar Abdul Shukor, Kunihisa Tashiro http://creativecommons.org/licenses/by-sa/4.0
Effect of the angular offset of the stator windings on DSIM performance
The study outlined in this paper aims to analyze the effect of the different displacement angles between the two stator windings on the performance of a dual stator induction motor, which is a squirrel cage induction motor with two identical windings in its stator. The rated power of each winding is 1.1 kW and fed by an inverter operating with the pulse width modulation technique. The analytical model of the machine is used to analyze its characteristics to investigate the impact of the displacement angles between the two stator windings. A simulation program for the system has been developed using MATLAB/Simulink. Simulation results characterizing the comportment of this machine for different displacement angles of the stator windings show that the torque pulsations are noticeably lower at 30° shift than in the other two scenarios of 0° and 60°, the model exhibits noteworthy performances at this shift. The torque pulsations are noticeably lower at 30° shift than in the other two scenarios of 0° and 60°, and the model exhibits noteworthy performance at this shift. In this case, there are also reduced rotor current ripples, which decrease rotor heating. Despite this, the harmonics increased the peak stator phase currents for a 30° electrical offset.
Copyright (c) 2025 Fatma Lounnas, Salah Haddad http://creativecommons.org/licenses/by-sa/4.0
ANN based speed control of switched reluctance motor using MATLAB-interfaced DSP controller
The switched reluctance motor (SRM) is gaining significance as a competitive motor in industries due to its prominent features such as absence of rare-earth elements, strong fault tolerance, and competitive efficiency. This paper presents a comprehensive framework to a novel and simplified hardware implementation of SRM drive, accompanied by a stepwise procedure to develop the control process that includes system modelling with simulation analysis and experimental validation, useful for the novice researchers. A precise hardware control environment is introduced, by integrating MATLAB/Simulink platform with digital signal processor (DSP) microcontroller - TMS320F280049C, which minimizes the complexities of traditional controller coding. The paper provides an in-depth explanation of deployment of artificial neural network (ANN) speed control block, offering valuable insights into the practical aspects of ANN-based control in MATLAB. The paper also compares closed-loop speed control using proportional-integral (PI) and ANN control in SRM, and the results demonstrate accurate and adaptive performance of ANN control for variable speed- load conditions.
Copyright (c) 2025 Veena Wilson, Latha Padinjaredath Govindan http://creativecommons.org/licenses/by-sa/4.0
Processor-in-the-loop performance validation of a three-phase NPC three-level inverter using a novel sinusoidal PWM technique for scalar control of an induction motor
This paper presents the performance of a three-phase, three-level neutral point clamped inverter driving an induction motor for variable-speed applications, compared to a two-level inverter. The studied inverter operates using a novel sinusoidal pulse width modulation technique that improves the quality of voltage and current output signals while increasing efficiency. Motor speed control is achieved using the scalar control (V/Hz) method. Experimental validation of the simulation results is performed by executing the generated C code on the F28379D DSP LaunchPad within the MATLAB/Simulink and Code Composer Studio environment, applying the processor-in-the-loop (PIL) technique.
Copyright (c) 2025 Badr N’hili, Souhail Barakat, Abdelouahed Mesbahi, Mohamed Khafallah, Ayoub Nouaiti http://creativecommons.org/licenses/by-sa/4.0
Backstepping control in speed loop combined with load torque observer-ESO for IPMSM in electric vehicle
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.
Copyright (c) 2025 An Thi Hoai Thu Anh, Tran Hung Cuong, Nguyen Van Hoa http://creativecommons.org/licenses/by-sa/4.0
Enhanced speed regulation using separate P and I gain controllers in a fuzzy-PI framework
This paper explores an enhanced method for regulating the speed of brushless DC (BLDC) motors using field-oriented control. Conventionally, a proportional-integral (PI) controller is employed to adjust output speed and current FOC method. While the PI controller is effective in many scenarios, it exhibits limitations including poor performance when the speed reference changes rapidly. To address these limitations, a fuzzy-PI control scheme is proposed in this study with the aim of improving the speed control performance of BLDC motors, especially under rapidly changing speed reference. The proposed two separate fuzzy logic controllers adaptively adjust the proportional and integral gains so that it combines the robustness of fuzzy logic with the steady-state error of PI control. Simulation and experimental results demonstrate that the fuzzy-PI control significantly outperforms the conventional PI controller in terms of BLDC stability, response time, and accuracy. The proposed approach ensures more reliable and efficient speed regulation for BLDC motors, making it a reliable solution for applications where speed reference fluctuate frequently.
Copyright (c) 2025 Minh Duc Pham, Duong Nguyen Trong Qui, Truong Phuoc Hoa http://creativecommons.org/licenses/by-sa/4.0
Design and analysis of brushless permanent magnet motor for light electrically powered two-wheeler vehicle
This study provides a comprehensive process of designing an electric motor that will be used for a small two-wheeled electric vehicle. Due to high performance capability in term of power and torque, brushless permanent magnet topology is chosen so that a compromise between size constraint and performance can be met. For an accurate motor design sizing, the design process is initially carried out by determination of power rating that derived from vehicle dynamic calculation. Based on winding factor calculation, fractional-slot 12-slot/10-pole and 9-slot/10-pole motors equipped with non-overlapping winding are chosen and analyzed using finite element analysis (FEA) software. For an optimum electromagnetic performance, parametric optimization is included, mainly on the stator dimension. Despite the performance of both designs improved, only 9-slot motor results a convincing performance as the rated torque is 18% higher than the 12-slot design. For verification purpose, 1-D analytical solution is also included and compared with results deduced by the FEA. According to the analysis, the proposed motor designs are adequately reliable for a light electrically powered electric vehicle application.
Copyright (c) 2025 How Xuan Yu, Mohd Luqman Mohd Jamil, Nurul Ain Mohd Said http://creativecommons.org/licenses/by-sa/4.0
Comparison of the discounted costs of controlled asynchronous electric drives with matrix and with DC link frequency converters
A quality criterion based on discounted costs is proposed, which demonstrates a significant advantage of the variable frequency asynchronous motor drive with a matrix converter over the drive with a voltage source inverter, which contains a DC link. A MATLAB software simulation was conducted to ascertain the control characteristics. In light of the control range afforded by both drives, a criterion for discounted costs is proposed that is calculated as a mid-range within a specific rotational speed control range, or is determined based on a given tachogram. The aforementioned costs include the expense of the drive, the cost of losses, maintenance costs, amortization charges, and the cost of reactive power compensation due to phase shifts of the main harmonic current and voltage. In this study, we put forth a novel proposal for the incorporation of the cost of distortion power compensation resulting from the presence of harmonic components of the input current. The latter costs characterize the electromagnetic compatibility of the drive with the network. For the first time, a quality criterion for a regulated electric drive is proposed, which has a cost component that takes into account the electromagnetic compatibility of the drive with the network. A significant reduction in this component in a drive with a matrix converter compared to a drive with a DC link predetermines a reduction in discounted costs. For a given payback period and annual inflation rate, it was determined that the mid-range discounted costs were reduced by more than 11 times and the tachogram based discounted costs were reduced by more than 10 times for a drive with a matrix converter in comparison to a drive with a DC link.
Copyright (c) 2025 Viktor Petrushyn, Juriy Plotkin, Vasily Horoshko, Rostyslav Yenoktaiev, Andrii Yakimets http://creativecommons.org/licenses/by-sa/4.0
Improvement of DSIM control using fuzzy third-order sliding mode approach optimized by MOA
This study focuses on the contribution of a new hybrid controller based on the sliding mode technique associated with fuzzy logic and optimized by an innovative approach called the mayfly optimization algorithm (MOA) to improve the drive of the dual star induction motor (DSIM). The performance and robustness of this system are analyzed under different operating conditions with three proposed strategies and compared with each other under the MATLAB/Simulink environment. Through the simulation results obtained, we realize that the method that integrates the MOA with a hybrid controller associating the third order sliding mode with fuzzy logic (MOA-FTOSMC) makes a significant contribution to research work in this field and offers the best dynamic performance and adequately manages the uncertainty and variation of the system parameters under different operating regimes.
Copyright (c) 2025 Rahma Belkaid, Lamia Youb, Farid Naceri, Ghoulem Allah Boukhalfa http://creativecommons.org/licenses/by-sa/4.0
Nonlinear excitation control of multimachine systems via the invariant-set design
Power grids are inherently vulnerable to many uncertainties. All power networks are prone to instability because of the uncertainties inherent in the operation of power systems. Rotor-angle instability is a challenging issue, and if not properly managed, could give rise to cascading failures and even blackouts. This paper addresses the generator excitation system’s state feedback sliding mode control (SMC). The global system is divided into multiple subsystems to achieve decentralized control. A disturbance is defined as the influence of the system as a whole on a specific subsystem. The state-feedback controller is to be designed taking into account the disturbance attenuation level, ensuring the closed-loop system's asymptotic stability. The SMC designing algorithm is described; it is based on precisely determining the sliding surface utilizing the invariant-set (ellipsoid) technique. The control structure ensures that mismatched disturbances in power systems have little impact on the system trajectory in the sliding mode. Moreover, the proposed controllers are represented in this paper using linear matrix inequalities (LMIs) and the Lyapunov theory approach. Finally, a multi-machine model is implemented to demonstrate the success of the suggested approach, and a comparison between the proposed SMC and the conventional one demonstrates its superiority.
Copyright (c) 2025 Hisham M. Soliman, Ehab H. E. Bayoumi, Farag Ali El-Sheikhi, Fawzan Salem http://creativecommons.org/licenses/by-sa/4.0
Approach to self-synchronization of a group of static power converters
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.
Copyright (c) 2025 Victor Lavrinovsky, Nikita Dobroskok, Valery Bulychev, Ruslan Migranov, Yuriy Yu Perevalov, Anastasia Stotckaia http://creativecommons.org/licenses/by-sa/4.0
Fuzzy logic-based adaptive PLL switching strategy for voltage control in DVR assisted grid tied PV systems
This study aims to enhance power quality in grid-connected photovoltaic (PV) systems by introducing an intelligent fuzzy logic-based adaptive control strategy for dynamic PLL switching in a DVR-supported configuration. A 100-kW grid-tied PV system is modeled with a digital phase-locked loop (DPLL), a conventional synchronous reference frame PLL (CTPLL), and a dynamic voltage restorer (DVR). A Mamdani-type fuzzy inference system (FIS) performs real-time PLL selection based on phase-wise real-time fault monitoring. The system was tested under symmetrical and asymmetrical 20% sag and swell conditions, evaluating voltage stability at both PCC and load, total harmonic distortion (THD), recovery time, and synchronization accuracy. Results show that the proposed method reduces unnecessary DVR voltage injection from ~50 V to ~5-6 V under healthy conditions, maintains a near-unity power factor (< 0.95), and achieves up to 15% THD reduction in inverter current and PCC currents compared to DPLL-only operation. Recovery times improved by up to 25%, with stable synchronization maintained in all fault cases. The integration of adaptive PLL switching and targeted DVR activation offers a novel, hardware-efficient approach to harmonic suppression, voltage stabilization, and fault resilience in medium-scale PV systems.
Copyright (c) 2025 R. Srilakshmi, V. Chayapathy http://creativecommons.org/licenses/by-sa/4.0
Design of a static synchronous compensator for the north-south high-speed railway system
The modern high-speed rail system plays a crucial role in driving the nation’s economic development. The problem of voltage imbalance caused by intermittent load movements is a significant challenge for energy management and distribution. When electric trains are connected to the three-phase grid, power quality degradation occurs, resulting in distortion and imbalance of the three-phase grid current and voltage, which in turn increases operating costs. This paper has proposed a linear control method using a PI controller for a static synchronous compensator (STATCOM) to directly control the amount of reactive power loss for electric trains. This solution will also bring good and stable voltage quality to electric trains so that electric trains can operate for a long time. The STATCOM device in this paper is a three-phase voltage source converter with a simple structure and can be easily controlled. This is considered a simple and effective solution to balance voltage, improve power factor, and enhance harmonic quality for railway trains, thereby achieving an optimal operating solution. This discussion can be simulated using MATLAB/Simulink software to determine the operation and control steps for STATCOM, thereby improving the quality of the power system. The simulation results of current, voltage, and reactive power response are presented. The simulation results have demonstrated that the proposed algorithm successfully achieves the set goals of ensuring voltage stability and providing the necessary amount of reactive power for the train, thereby improving the quality of the power grid for the North-South high-speed train in Vietnam.
Copyright (c) 2025 An Thi Hoai Thu Anh, Tran Hung Cuong http://creativecommons.org/licenses/by-sa/4.0
Attenuated-chattering global second-order sliding mode load frequency controller for multi-region linked power systems
In this study, a new chattering-free global second-order sliding mode load frequency controller (CGSOSMLFC) is proposed for multi-region linked power systems (MRLPS). Key achievements of this paper include: i) a new CGSOSMLFC is investigated utilizing only output variables; ii) a global steadiness of the MRLPS is ensured by eliminating the hitting phase in traditional sliding mode control (TSMC), and the undesirable high-frequency vacillation marvel in the control signal is efficiently lessened by utilizing the second-order sliding mode control technique. Firstly, a novel estimator is constructed to conjecture the immeasurable state variables of the MRLPS. Then, an estimator-based CGSOSMLFC is synthesized to force the states of the controlled plant into the anticipated switching surface at an instance time and attenuate the chattering phenomenon in the control indication. Additionally, the total MRLPS’s stability analysis is executed by applying the Lyapunov function theory and linear matrix inequality (LMI), confirming the practicability and reliability of the method. Lastly, simulation outcomes on a three-zone linked power system are furnished to authenticate the usefulness and advantages of the proposed technique.
Copyright (c) 2025 Phan-Thanh Nguyen, Cong-Trang Nguyen http://creativecommons.org/licenses/by-sa/4.0
Implementation of adaptive PID control for maintaining temperature stability during steady-state conditions in stirred heating tank
Temperature stability is a crucial factor in industries such as chemicals, pharmaceuticals, and food processing, where fluctuations can damage product quality and increase energy consumption. This study aims to optimize heater power control using an adaptive proportional integral derivative (PID) control system to maintain temperature stability under steady-state conditions. The method involves applying adaptive PID control to a stirred heating tank using LabVIEW software with a national instruments controller module and a single-phase SCR to regulate heater power and adjust control parameters in real time. The results indicate that the system operates more effectively under stable conditions, with faster response times and a lower overshoot of less than 0.12%. However, under disturbed conditions, such as water drainage and replacement, the system requires more time to adjust the temperature and experiences increased energy consumption and heat loss. Despite this, the system still achieves an energy efficiency improvement, with efficiency values ranging from 77.66% to 80.03%. The implementation of adaptive PID control demonstrates significant potential in enhancing system accuracy and response to temperature changes, contributing to the development of more efficient industrial control technologies.
Copyright (c) 2025 Pricylia Valentina, Hendro Tjahjono, Agus Sunjarianto Pamitran, Iwan Roswandi, Putut Hery Setiawan, Arif Adtyas Budiman, Dedy Haryanto, Sanda, Kukuh Prayogo, Mulya Juarsa http://creativecommons.org/licenses/by-sa/4.0
Machine learning-based energy management system for electric vehicles with BLDC motor integration
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.
Copyright (c) 2025 K. S. R. Vara Prasad, V. Usha Reddy http://creativecommons.org/licenses/by-sa/4.0
Adaptive control strategies for enhancing the performance and stability of renewable energy systems
The comparison done in this paper uses two control strategies for a solar-fed three-phase inverter, one with standard sinusoidal pulse width modulation (SPWM) and the other with unified space vector PWM (USPWM), equipped with adaptive voltage control (AVC) and a load current observer (LCO). A photovoltaic (PV) source feeds into a controlled DC link, which in turn supplies a DC voltage to the inverter powering an RL load. From the simulation, it is clear that although SPWM meets the output standards, it has increased total harmonic distortion (THD) and slower transient response. Alternatively, using USPWM control decreased THD to 3.01% and made the system respond in 11 ms, compared to 22 ms before. The new method provides better efficiency and better power quality when used with dynamic loads.
Copyright (c) 2025 Sreedevi Kunumalla, Durgam Rajababu, A. V. V. Sudhakar http://creativecommons.org/licenses/by-sa/4.0
Supervised learning for fast inverse motor control mapping: a comparative study on SRM and BLDC motors
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.
Copyright (c) 2025 S. Sudheer Kumar Reddy, J. N. Chandra Sekhar http://creativecommons.org/licenses/by-sa/4.0
Design and development of AC motor speed controlling system using touch screen with over heat protection
Design and implementation of an AC motor speed control and monitoring system based on a touch screen interface with built-in overheat protection, utilizing Arduino, meets the increasing demand for efficient, user-friendly motor control in many industrial applications. This system offers an easy-to-use interface to manage the speed of an AC motor, with real-time feedback and adjustments through a touch screen display. The system employs an Arduino microcontroller, which accepts inputs from the touch screen and processes these to regulate the motor's speed through a pulse width modulation (PWM) method. The system also has an overheat protection system, which it is able to monitor the temperature of the motor via a temperature sensor. When the motor reaches a predetermined temperature, the system automatically shuts off power to avoid damage. The intuitive touch screen facilitates convenient monitoring of motor parameters like temperature, giving a smooth experience to operators. The modular design of the system provides scalability across applications, ranging from household appliances to large industrial systems, with reliability, energy efficiency, and safety in motor-driven processes.
Copyright (c) 2025 Prathipati Ratna Sudha Rani, Gouthami Eragamreddy, Syed Inthiyaz, Sivangi Ravikanth, Mohammad Najumunnisa, Bodapati Venkata Rajanna, Cheeli Ashok Kumar, Shaik Hasane Ahammad http://creativecommons.org/licenses/by-sa/4.0
Dynamic modelling and small-signal analysis of an efficient bridge-type multi-input DC converter for hybrid low-power systems
This paper presents the dynamic modelling and small-signal analysis of a bridge-type multi-input DC converter designed for hybrid low-power systems. The converter architecture supports the integration of multiple energy sources such as fuel cells and photovoltaic (PV) arrays, enabling enhanced flexibility and reliability. A CUK-based configuration is employed to achieve continuous input current and reduced voltage stress across the switches. The dynamic behavior of the converter is analyzed through average large-signal and small-signal state-space modelling. Stability is assessed using the Routh-Hurwitz criterion, and steady-state analysis is carried out to support performance evaluation. Experimental results obtained from a 250 W prototype confirm the validity of the developed models and demonstrate the efficiency and suitability of the proposed converter for hybrid renewable energy systems.
Copyright (c) 2025 Baya Reddy Lomada, Vangala Naga Bhaskar Reddy http://creativecommons.org/licenses/by-sa/4.0
Design and simulation of inductive power transfer system using hybrid compensation topologies
This research addresses the principle of wireless power transfer (WPT). The system is primarily based on inductive power transfer (IPT). IPT is a recent technology that enables electrical power to be transferred between two coils via a magnetic field without the need for physical conductors. This method is particularly useful in applications where conventional wires cannot be used, such as biomedical implants, electric vehicles, and consumer electronics. Existing advances in system design, magnetic materials, and compensation topologies have significantly improved system performance and expanded their application range. Main challenges in IPT systems include improving efficiency and transmission distance. Hybrid compensation techniques in IPT systems have emerged as a promising solution to enhance system stability and power transfer efficiency under varying load conditions. IPT systems ensure highly efficient battery transfer and charging. This paper presents the design and simulation of a 3.7 kW IPT system employing hybrid compensation topologies specifically inductor–capacitor–capacitor/series (LCC/S) and LCC/LCC configurations to enhance power transfer efficiency and maintain zero phase angle (ZPA) operation. The proposed system is simulated using ANSYS Maxwell and MATLAB/Simulink to evaluate voltage gain, resonant behavior, and power output under varying load conditions. The LCC/LCC topology demonstrates superior load-independent ZPA characteristics and improved receiver-side voltage stability. Simulation results confirm that both configurations achieve high efficiency and robust power transfer over an air gap of 100 mm, with the LCC/LCC system showing better tolerance to misalignment. These findings suggest that hybrid compensation topologies are viable candidates for medium-power wireless charging systems in electric vehicles and industrial automation.
Copyright (c) 2025 Noor Sami, Harith Al-Badrani http://creativecommons.org/licenses/by-sa/4.0
Performance analysis of a cascaded dual full bridges 5, 7, and 9 levels inverter: experimental validation
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.
Copyright (c) 2025 Nabil Saidani, Rachid El Bachtiri, Abdelaziz FRI, Karima El Hammoumi http://creativecommons.org/licenses/by-sa/4.0
Combination circuit of multilevel inverter, matrix converter, and H-bridge
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.
Copyright (c) 2025 Akram Mohammed Al-Mahrouk, Nashiren Farzilah Mailah, Mohd Amran Mohd Radzi, Mohd Khair Hassan http://creativecommons.org/licenses/by-sa/4.0
Time-domain performance of QBC with self-lift circuit
This study examines the performance of a high-gain quadratic boost converter (QBC) coupled with a self-lift circuit under two control methodologies: sliding mode control (SMC) and fractional-order proportional integral derivative (FOPID) control. The QBC topology is used because it can boost voltage significantly, which is especially useful for renewable energy applications. Simulation studies show that both controllers can control the output voltage of the converter, but the FOPID controller works better in dynamic situations. In particular, it makes settling happen faster, cuts down on overshoot, and lowers steady-state error compared to the SMC method. The overall results show that the FOPID controller is a good choice for improving stability and transient response. This makes it a good choice for advanced high-performance power electronic systems.
Copyright (c) 2025 Subbulakshmy Ramamurthi, Palani Velmurugan, Shobana Devendiren, Soundarapandiyan Manivannan http://creativecommons.org/licenses/by-sa/4.0
Small signal modeling of restructured boost converter in continuous conduction mode
This paper introduces small signal modeling of the restructured boost converter (RBC) in continuous conduction mode (CCM) by using the circuit averaging technique. The averaging technique produces linear transfer functions of the converter. The transfer functions relating the duty cycle to output voltage, duty cycle to inductor current, input voltage to output voltage, and input voltage to inductor current are obtained. To validate the converter model, power simulation (PSIM) simulations are developed, and experiments are conducted. The function of RBC is similar to a conventional boost converter, i.e., to level up the input voltage. A comparative analysis between the RBC and conventional boost converter is performed. The results highlight the advantages of RBC over a conventional boost converter.
Copyright (c) 2025 Anwar Muqorobin, Sulistyo Wijanarko, Muhammad Kasim, Pudji Irasari, Ketut Wirtayasa, Puji Widiyanto http://creativecommons.org/licenses/by-sa/4.0
Experimental validation of virtual flux concept in direct power control with dynamic performance
The virtual-flux direct power control (VFDPC) technique is a sensorless control approach aimed at improving the performance of grid-connected power converters. The approach involves simulating the grid voltage and AC-side inductors similar to an AC motor drive system, a principle deriving from direct torque control (DTC). The basic idea of VFDPC is to indirectly estimate the voltage at the converter's input through the concept of virtual flux, enabling the real-time calculation of instantaneous active and reactive power without necessitating direct voltage measurements. An essential element of the VFDPC approach is the implementation of a lookup table, used as a decision-making tool that identifies the most suitable voltage vector (a particular output state of the converter) in accordance with real-time power conditions. This provides instantaneous and smooth control of power flow, leading to enhanced operational stability. This approach allows for continual optimization of the converter's output, enabling VFDPC to significantly decrease total harmonic distortion (THD) while preserving reliable steady-state and dynamic performance. Experimental validation demonstrates that incorporating real-time feedback into virtual flux estimates improves the precision of voltage prediction and the responsiveness of the power control system. Consequently, VFDPC exhibits enhanced adaptability for various grid and load situations, presenting an appropriate choice for current power systems that demand efficient, reliable, and sensorless operation.
Copyright (c) 2025 Muhammad Hafeez Mohamed Hariri, Nor Azizah Mohd Yusoff, Muhammad Zaid Aihsan, Tole Sutikno http://creativecommons.org/licenses/by-sa/4.0
Design and simulation of an electric vehicle charging system with battery arrangement and control parameters optimization
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.
Copyright (c) 2025 Nurmiati Pasra, Faizal Arya Samman, Andani Achmad, Yusran http://creativecommons.org/licenses/by-sa/4.0
Interleaved buck converter using a floating dual series-capacitor topology
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.
Copyright (c) 2025 Chan Viet Nguyen, Dang Tai Nguyen, Thanh Phuong Ho http://creativecommons.org/licenses/by-sa/4.0
Bidirectional AC/DC converter connecting AC and DC microgrids for smart grids
This paper proposes a converter connecting two independent AC and DC microgrids in a flexible microgrid and smart grid system. With this converter, basic DC/DC converter types such as Flyback are used to develop the power circuit and controller for the converter that is capable of integrating the operating functions for the operation between microgrids. The converter uses bidirectional switching locking technology to simplify the control algorithm. The energy is converted in two directions, AC/DC and DC/AC, with different working principles of increasing and decreasing voltage according to the standards of the distribution grid and DC microgrid. The TDH value is significantly limited when using the recovery circuit solution. The converter is designed, simulated based on OrCAD software, and tested with a capacity in the range of 2-10 kW. The DC microgrid output voltage is 400 VDC, voltage is 220 VAC.
Copyright (c) 2025 Nguyen Van Dung, Nguyen The Vinh http://creativecommons.org/licenses/by-sa/4.0
Effect of gas flow rate on ionizing power characteristics of penning type ion source
An experimental observation on the effect of hydrogen gas flow rate value on ionization power characteristics of penning type ion source has been conducted. The experiments were conducted in the range of gas flow rate values between 3 and 8 sccm, which is a range of discharge that is generally used in cyclotron operations. The characteristic of ionization power is the change in power which is determined from the cathode voltage and cathode current that occurs when the gas flow rate is varied. The fixed operating parameter is the magnetic field at a value of 1.29 T. The characteristic data is presented in graphs and analyzed theoretically. The experiment was conducted at the DECY-13 cyclotron. The results of the analysis show that the effect of increasing the gas flow rate does not significantly affect the characteristics of ionization power. However, further analysis shows that the increase in gas flow rate will have a significant effect on the increase in ion formation rate in the ionization chamber due to a significant increase in the increase in gas pressure in the chamber. The benefit of the results of this study is as an initial capital to increase ion productivity from ion sources.
Copyright (c) 2025 Silakhuddin, Bambang Murdaka Eka Jati, Dwi Satya Palupi, Taufik, Idrus Abdul Kudus, Fajar Sidik Permana, Suharni http://creativecommons.org/licenses/by-sa/4.0
Study of asymmetrical-multi level inverter using two switching angle techniques
An inverter is a device that transforms DC power into AC power. Inverters can be categorized into single-level inverters and multilevel inverters. This paper discusses two controlled strategies-equal step angle and sinusoidal switching angle-for a multilevel inverter, highlighting their effectiveness in harmonic mitigation as the number of voltage levels increases. The simulation software used to generate 3-15 level voltage outputs is PSIM, which allows for the adjustment of switching angles based on both equal step and sinusoidal switching values. Various types of DC sources are connected to H-bridge units, with MOSFET driving signals applied via gating blocks. The study demonstrates a notable reduction in total harmonic distortion (THD) when the switching angles are altered in equal and sinusoidal steps. Initially, the output signal generates a square wave without a filter. However, after implementing an LC filter, the output voltage signal more closely resembles an AC signal, and THD values are further reduced. Additionally, the output voltage signal's fast Fourier transform (FFT) is presented.
Copyright (c) 2025 Dewan Ashikur Rahaman, Tapan Kumar Chakraborty http://creativecommons.org/licenses/by-sa/4.0
Enhanced voltage stability in power distribution networks through optimal reconfiguration using hybrid metaheuristic algorithms
An optimal network reconfiguration (ONR) is used in distribution power systems to improve voltage decreases within the permitted period and minimize real power losses. Consequently, attaining optimal reconfiguration in distribution systems is regarded as the primary objective of numerous researchers. Conventional heuristic techniques such as genetic algorithms (GA), ant colony optimization (ACO), and particle swarm optimization (PSO) can reduce active power losses and enhance network stability. These algorithms indicate a greater number of difficulties, including inadequate convergence characteristics, a reduction in power loss, and an increase in bus voltage. This research proposes effective optimization strategies utilizing the salp swarm algorithm (SSA) and whale optimization algorithm (WOA) to augment bus voltage, reduce distribution losses, and improve network dependability. The proposed algorithms are executed and evaluated on the IEEE 33-bus and 69-bus networks to determine the ideal network architecture. The efficacy of the examined methodologies is illustrated through MATLAB under steady-state conditions, showcasing benefits in the reduction of active power loss relative to current algorithms. The comparison indicates that the SSA algorithm exhibits superior performance in terms of power losses and bus voltage enhancement relative to the WOA method. due to its enhanced exploration and exploitation capabilities, which help avoid local optima and ensure a more effective search for optimal solutions. SSA's adaptive mechanism and cooperative behavior improve convergence speed and solution accuracy, making it more efficient for optimization in network reconfiguration.
Copyright (c) 2025 Mohammed Zuhair Azeez, Abbas Swayeh Atiyah, Yaqdhan Mahmood Hussein, Hatem Oday Hanoosh http://creativecommons.org/licenses/by-sa/4.0
Bidirectional power converter for electrical vehicle with battery charging and smart battery management system
In electric vehicles (EVs), efficient energy management is critical for reliable power transfer between the battery and motor. This paper presents the design and implementation of a bidirectional DC-DC converter equipped with a smart battery management system (BMS). The system supports bidirectional power flow, operating in boost mode during acceleration and buck mode during regenerative braking, thereby enhancing overall energy efficiency and vehicle performance. A PIC microcontroller governs the system, performing real-time monitoring of key battery parameters such as state of charge (SOC), state of health (SOH), voltage, and temperature. Safety features include automatic cooling fan activation when the temperature exceeds 45 °C and generator startup when battery voltage falls below 23 V. Real-time data is displayed via an LCD interface to improve user interaction and system transparency. The proposed system achieved a conversion efficiency of 90-93% during experimental testing, with stable switching, reliable automation, and effective thermal protection. The embedded energy management system optimizes charging and discharging cycles while preventing overcharging, deep discharge, and thermal stress. This intelligent, automated power converter enhances battery life, improves EV reliability, and contributes to sustainable transportation by enabling features like vehicle-to-grid (V2G) energy transfer. The proposed architecture is well-suited for integration into modern EV infrastructure. Although the system architecture supports future V2G integration, V2G functionality was not implemented or tested in the present experimental setup.
Copyright (c) 2025 Bodapati Venkata Rajanna, Kondragunta Rama Krishnaiah, Ganta Raghotham Reddy, Shaik Hasane Ahammad, Mohammad Najumunnisa, Syed Inthiyaz, Gouthami Eragamreddy, Ambarapu Sudhakar, Nitalaksheswara Rao Kolukula http://creativecommons.org/licenses/by-sa/4.0
Design and DSP-based validation of a cascaded DSOGI-PLL for mitigating grid disturbances
Ensuring a smooth power injection into an electric grid in the presence of imperfections, such as phase disturbances, voltage imbalance, frequency variations, harmonics, and DC offsets, requires fast and robust phase-locked loop (PLL) techniques. Among these, the double second-order generalized integrator (DSOGI)-based PLL is widely used due to its strong performance in challenging grid conditions. However, conventional DSOGI-PLL has limitations in handling DC offsets and harmonic disturbances. To address these challenges, this paper introduces the design of a cascaded DSOGI-PLL that enhances attenuation of DC components and low-order harmonics while maintaining computational simplicity for DSP-based implementation. Experimental validation on a TMS320F28379D DSP platform demonstrates that the proposed scheme achieves synchronization settling within 48 ms even under severely polluted grid conditions, while reducing output unit-vector THD to 0.5% when the input voltage contains 22% THD. These results confirm the cascaded DSOGI-PLL as a significant improvement over conventional PLLs.
Copyright (c) 2025 Ilias En-Naoui, Abdelhadi Radouane, Azeddine Mouhsen, Hamid Yantour http://creativecommons.org/licenses/by-sa/4.0
Advanced control architectures for enhanced simulation and operational analysis of solar PV-driven vehicle systems
Interplanetary interest in solar PV systems in automobiles has grown as renewable energy, especially in transportation subsystems, is used more widely. Emphasizing innovative control strategies to increase power conversion efficiency, reliability, and flexibility, this paper identifies and assesses solar photovoltaic integrated vehicle drive systems. In Simulink, several researchers replicate power systems, solar PV systems, vehicle propulsion systems, and power conversion technologies. To imitate real-world settings, researchers evaluate the efficiency of the device at many solar light and load values. High-level control techniques suitable in such unpredictable conditions are MPPT and dynamic load control. These controls are definitely required to ensure the correct functioning of the plant system, independent of natural variables, like irradiation and temperature. After that, the performance of the suggested control strategies is investigated under the main success criteria: energy analysis, system efficiency, and operational stability. This implies that solar PV integrated systems for automobiles could gain from ideal performance and durability, hence improving the off-grid operation of cars. These findings offered latent promise for use in the developing transportation sector and advancement of solar PV technology.
Copyright (c) 2025 Mani Raghupathi, Dhanraj Susitra, Nagarajan Karthikeyan http://creativecommons.org/licenses/by-sa/4.0
Numerical and experimental state of identification battery pack lithium-ion
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.
Copyright (c) 2025 Dewi Anggraeni, Budi Sudiarto, Eriko Nasemudin Nasser, Wahyudi Hasbi, Yus Natali, Purnomo Sidi Priambodo http://creativecommons.org/licenses/by-sa/4.0
Design of an EBNN-PID based adaptive charge controller for variable DC charging applications
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.
Copyright (c) 2025 Mochammad Machmud Rifadil, Putu Agus Mahadi Putra, Amalia Muklis http://creativecommons.org/licenses/by-sa/4.0
Enhanced integration of renewable energy and smart grid efficiency with data-driven solar forecasting employing PCA and machine learning
A significant obstacle to preserving grid stability and incorporating renewable energy into smart grids is variations in solar irradiation. To improve solar power management's dependability, this research proposes a short-term solar forecasting framework powered by AI. Multiple machine learning models, such as long short-term memory (LSTM), random forest (RF), gradient boosting (GB), AdaBoost, neural networks (NN), K-Nearest neighbor (KNN), and linear regression (LR), are integrated into the suggested system, which also uses principal component analysis (PCA) for dimensionality reduction. The Abiod Sid Cheikh station in Algeria (2019-2021) provided real-world data for the model's validation. With a two-hour-ahead RMSE of 0.557 kW/m², AdaBoost had the most accuracy, whereas LR had the lowest, at 0.510 kW/m². In addition to increasing computing efficiency, PCA preserved 99.3% of the data volatility. In addition to increasing computing efficiency, PCA preserved 99.3% of the data volatility. These findings highlight the efficiency of hybrid AI models based on PCA for accurate forecasting, which is crucial for smart grid stability.
Copyright (c) 2025 Jayashree Kathirvel, Pushpa Sreenivasan, M. Vanitha, Soni Mohammed, T. Sathish Kumar, I. Arul Doss Adaikalam http://creativecommons.org/licenses/by-sa/4.0
Step size variability with high performance solar-wind grid integration using MPPT algorithm
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.
Copyright (c) 2025 Lakshmi Dhandapani, Pushpa Sreenivasan, Malathy Batumalay http://creativecommons.org/licenses/by-sa/4.0
Hybrid intelligent optimization algorithms-based power management for microgrid system
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.
Copyright (c) 2025 R. Indumathi, S. A. Lakshmanan http://creativecommons.org/licenses/by-sa/4.0
The role of thermal insulation layers and the integration of solar energy in temporary heating systems
This paper examines thermal insulation strategies for building walls and the integration of solar heating systems to improve the performance of temporary heating systems in residential buildings in Kosovo. A two-story house was used as the case study, simulating four different scenarios of thermal insulation layer placement in the walls with different capacities of the heating system. The proposed thermal balance method of the building takes into account the arrangement of thermal insulation layers and their impact on the building’s energy savings. The results indicate that external insulation offers the best balance between heat retention and energy efficiency, while internal insulation enables faster heating and a shorter time to reach the desired temperature. Under low-temperature conditions, solar energy was analyzed and integrated as an additional source to enhance the heating system capacity and reduce electricity consumption. Simulation results demonstrate further improvement in system performance, enabling optimized operating schedules and a significant reduction in energy consumption.
Copyright (c) 2025 Rexhep Selimaj, Sabrije Osmanaj http://creativecommons.org/licenses/by-sa/4.0
Dehydration of Moringa leaves using microcontroller and IoT controlled electrical dryer
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.
Copyright (c) 2025 Saifuddin Muhammad Jalil, Abubakar Dabet, Syarifah Akmal, Selamat Meliala, Muhammad http://creativecommons.org/licenses/by-sa/4.0
Advanced thermal modeling of lithium-ion batteries: foundations for advanced capacity prediction
Thermal modeling of lithium-ion batteries is crucial for optimizing their performance and reliability in applications such as electric vehicles and energy storage systems. This study introduces a novel thermal modeling framework to predict internal battery temperature as a function of current and ambient temperature. Three advanced methodologies, NN-LM, NN-BR, and GPM, were evaluated using drive cycle data across temperatures that vary from -20 °C to 25 °C. Among these, Gaussian process modeling (GPM) demonstrated the highest accuracy with an RMSE of 0.034%, while NN LM achieved an RMSE of 0.083%, offering a computationally efficient alternative suitable for real-time applications. The developed thermal model establishes a foundation for future research aimed at predicting battery capacity by incorporating the effects of internal temperature. Furthermore, accurate monitoring of internal temperature is critical for preventing thermal runaway by enabling early detection of unsafe thermal conditions. This work establishes a robust foundation for future research, aiming to develop real-time capacity prediction models, ultimately enhancing battery management systems under diverse operating conditions.
Copyright (c) 2025 Abdelhadi Elkake, El Mehdi Laadissi, Meriem Mossaddek, Tabine Abdelhakim, Abdelowahed Hajajji http://creativecommons.org/licenses/by-sa/4.0
Robust sliding mode control of a DFIG based on the SVM strategy
This paper presents a direct power control (DPC) method for a doubly-fed induction generator (DFIG) used in variable-speed wind power systems, combining sliding mode control (SMC) with space vector modulation (SVM). The proposed SMC-based DPC with SVM (SMC-DPC_SVM) achieves decoupled power control through flux orientation, enhancing performance through the robustness of SMC and the precision of SVM. Simulation results demonstrate the effectiveness of this control strategy. The conventional direct power control (C-DPC) approach delivers fast and robust power response, and a comparative analysis between C-DPC and the proposed SMC-DPC_SVM strategy highlights the advantages of the latter. Robustness was evaluated under varying machine parameters, confirming system stability. The proposed control method was implemented and validated using MATLAB/Simulink, achieving a total harmonic distortion (THD) of less than 5%, indicating high-quality power delivery to the electrical grid.
Copyright (c) 2025 Ibrahim Yaichi, Kouddad Elhachemi, Aoumri Mohammed http://creativecommons.org/licenses/by-sa/4.0
Optimization of solar panel orientation and tracking systems for standalone PV applications
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.
Copyright (c) 2025 Singgih Dwi Prasetyo, Yuki Trisnoaji, Misbahul Munir, Meirna Puspita Permatasari, Abram Anggit Mahadi, Marsya Aulia Rizkita, Zainal Arifin http://creativecommons.org/licenses/by-sa/4.0
Polyaniline as a conductive polymer and its role in improving the efficiency and conductivity of perovskite solar cells
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.
Copyright (c) 2025 Vahdat Nazerian, Mehran Hosseinzadeh Dizaj, Tole Sutikno http://creativecommons.org/licenses/by-sa/4.0
Assessment of the efficiency and performance of different PV system configurations under various fault conditions
Partial shadowing, bypass-diode issues, photovoltaic (PV) module deterioration, and wiring issues are examples of PV failures that have a substantial effect on power production and cause distinct peaks in a PV system's P-V curves. Various PV fault types have been used in the solar cell system in this work. Four types were used: open circuit, line to ground, cross-line to line, and intra-line to line. The impact of various PV system failure types on the system's performance was emphasized in this study. MATLAB is used to display the simulation results for the four approaches (series parallel (SP), total cross tied (TCT), honeycomb (HC), and bridge link (BL)) under various fault scenarios. The current-voltage (I-V) and power-voltage (P-V) curves are used to compare the results for each fault scenario. The open circuit fault between PV (7.8) in the first string and PV (18.19) in the fourth string resulted in a 40% decrease in the short-circuit current of the photovoltaic system compared to its normal value in the SP topology, while in the HC and BL topologies, the current value exceeded the allowable limit. This, in turn, had an impact on the (I-V) characteristics of this topology. The fault's impact was minimal and within the typical bounds of its (I-V) characteristics in the TCT topology.
Copyright (c) 2025 Raghad Adeeb Othman, Omar Sharaf Al-Deen Al-Yozbaky http://creativecommons.org/licenses/by-sa/4.0
Enhanced incremental conductance MPPT method for maximizing photovoltaic power generation
This research proposes an enhanced maximum power point tracking (MPPT) algorithm that integrates the variable step size (VSS) method to significantly improve power extraction from photovoltaic (PV) systems. The primary objective is to optimize performance under dynamic environmental conditions. Through comprehensive experimental studies, the proposed algorithm’s performance was evaluated and directly compared against conventional incremental conductance (INC) and perturb and observe (P&O) algorithms. The results demonstrate a substantial increase in power generation, with the proposed algorithm delivering 18.79% more power compared to INC and 39.67% more power than P&O. These findings underscore the efficacy of the developed algorithm at improving the efficiency and robustness of PV power generation, particularly in variable operating environments.
Copyright (c) 2025 Asnil, Refdinal Nazir, Krismadinata, Muhammad Nasir http://creativecommons.org/licenses/by-sa/4.0
Adaptive ANFIS-based MPPT for PV-powered green ships with high gain SEPIC converter
To align with global climate goals, the International Maritime Organization (IMO) has enforced strict measures to reduce greenhouse gas emissions from the shipping industry by promoting energy efficiency and cleaner propulsion methods. Ship engines remain major contributors to environmental pollution due to their dependence on fossil fuels and inefficient propulsion systems, highlighting the need for clean and sustainable alternatives. This study aims to design a renewable energy-based marine power system that effectively stores and utilizes solar energy, improving overall efficiency and reducing emissions for process innovation. A hybrid setup was developed using photovoltaic (PV) panels, batteries, and a bidirectional DC-DC converter to enable flexible power flow during both charging and discharging cycles. An adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) algorithm was employed alongside a SEPIC converter to enhance energy extraction from the PV system under dynamic conditions. The integrated system achieved a power extraction efficiency of 97.12%, confirming the effectiveness of the ANFIS-based MPPT strategy and showcasing the viability of intelligent renewable energy solutions in maritime applications.
Copyright (c) 2025 G. Jegadeeswari, Rohini Govindaraju, D. Balakumar, D. Lakshmi, S. Marisargunam, M. Batumalay, B. Kirubadurai http://creativecommons.org/licenses/by-sa/4.0
ANN-based MPPT for photovoltaic systems: performance analysis and comparison with nonlinear and classical control techniques
In photovoltaic energy systems, maximum power point tracking (MPPT) techniques are essential for optimizing power output under changing climatic conditions. Several techniques have been proposed in the literature, including classical techniques such as perturb and observe (P&O) and incremental conductance (INC), nonlinear controllers such as backstepping, and artificial intelligence-based techniques like fuzzy logic. This study compares the performance of an artificial neural network (ANN)-based MPPT approach with these nonlinear and classical MPPT techniques. It analyses the advantages and limitations of the various techniques to evaluate their performance in terms of efficiency, accuracy, and output power stability under changing climatic conditions. The study aims to help researchers select the most effective technique to improve the efficiency of photovoltaic systems. The simulation was carried out using MATLAB/Simulink. The simulation results indicated that the artificial neural network achieved better performance than the other techniques in terms of tracking speed, with an efficiency of up to 99.94%, while maintaining stable output power under changing climatic conditions. The backstepping controller also showed stable output power compared to traditional techniques. Fuzzy logic had a lower efficiency than both the artificial neural network and backstepping. Perturbation and observe and incremental conductance are easy to implement, but they showed oscillations around the maximum power point, which reduces the overall efficiency of the system.
Copyright (c) 2025 Abdouni Khadija, Benboukous Mostafa, Drighil Asmaa, Hicham Bahri, Mohamed Bour http://creativecommons.org/licenses/by-sa/4.0
Support-centric PSO-based fuzzy MPPT tuning for photovoltaic systems under uniform conditions
Several conventional maximum power point tracking (MPPT) algorithms have been applied to harvest the optimal power of a photovoltaic (PV) system. However, the main drawbacks of these algorithms are their fluctuations around the maximum power point (MPP) and their dependence on climatic conditions variation. To overcome these issues, a fuzzy logic controller (FLC) is proposed, where the system performance depends strongly on the choice of membership functions (MFs). They are typically selected by trial and error, which may not always yield the best results. This paper seeks to enhance the efficiency of the traditional FLC method by using the particle swarm optimization (PSO) algorithm for optimizing the supports of the triangular MFs. The simulation was performed using MATLAB-Simulink environment using the "1Soltech 1STH-215-P" PV module and a single-ended primary-inductor converter (SEPIC) converter, under ideal environmental conditions of 25 °C and 1000 W/m². A comparison is established between PSO-optimized FLC and the standard FLC-based MPPT method, as well as with several other state-of-the-art approaches reported in related research. The simulation data present that the PSO-optimized FLC approach outperforms other algorithms.
Copyright (c) 2025 Amel Smaili, El-Ghalia Boudissa, M’hamed Bounekhla http://creativecommons.org/licenses/by-sa/4.0
Development of a PEM fuel cell equivalent circuit model with PINN-based parameter identification
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.
Copyright (c) 2025 Ismail Ait Taleb, Zakaria Kourab, Souad Tayane, Mohamed Ennaji, Jaafar Gaber http://creativecommons.org/licenses/by-sa/4.0
Performance placement of BESS in the Sulawesi-Southern interconnected power system
Frequency regulation and active power loss management are crucial aspects of power system operations. Battery energy storage systems (BESS) have emerged as an innovative solution to enhance grid performance, especially in addressing frequency fluctuations and reducing power losses. This study explores the role of BESS in optimizing frequency regulation and managing active power losses in the power system through several BESS integration scenarios. In this study, a BESS with a capacity of 8.437 MW was used and analyzed using symmetric steady-state simulations in DigSILENT PowerFactory software. The simulations aim to test the effectiveness of BESS in frequency regulation and minimizing active power losses in the Sulbagsel system. The analysis results show that implementing BESS can respond effectively to both over-frequency and under-frequency conditions in the Sulbagsel system. In the discharge scenario, BESS can reduce the system's average frequency by 0.02 Hz and decrease active power losses by up to 1.09 MW. Conversely, in the charge scenario, active power losses increase by 1.22 MW when the BESS is installed on Bus Tonasa. This study provides valuable insights for developing BESS-based frequency regulation strategies that contribute to the stability and efficiency of the power system.
Copyright (c) 2025 Zaenab Muslimin, Indar Chaerah Gunadin, Fitriyanti Mayasari, Muhira Dzar Faraby, Asma Amaliah, Isminarti http://creativecommons.org/licenses/by-sa/4.0
Lithium-ion battery charge-discharge cycle forecasting using LSTM neural networks
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.
Copyright (c) 2025 Vimala Channapatana Srikantappa, Seshachalam Devarakonda http://creativecommons.org/licenses/by-sa/4.0
Adaptive intelligent PSO-Based MPPT technique for PV systems under dynamic irradiance and partial shading conditions
This research introduces an adaptive improved particle swarm optimization (AIPSO) approach for maximum power point tracking (MPPT) approach designed to enhance energy harvesting from photovoltaic (PV) systems under dynamic irradiance conditions. The proposed AIPSO algorithm addresses the challenges associated with traditional MPPT methods, particularly in scenarios characterized by fluctuating solar irradiance, such as step changes and partial shading. By incorporating a robust reinitialization strategy along with updated velocity and position equations, the algorithm demonstrates superior performance in terms of convergence accuracy, tracking speed, and tracking efficiency. This modification enables the algorithm to effectively escape local maxima and explore a wider search space, leading to improved convergence and optimal power point tracking. Furthermore, the adaptive nature of the PSO enhances the algorithm’s ability to respond to real-time changes in environmental conditions, making it particularly suitable for large- scale PV systems subjected to varying atmospheric factors. Here, “adaptive” denotes coefficient scheduling (C3) and a re-initialization trigger that responds to irradiance regime changes; “intelligent” denotes robust regime shift detection and safe duty ratio clamping. Across uniform, step change, and partial shading conditions, the proposed AIPSO achieves fast reconvergence and high tracking efficiency with negligible steady state oscillations, as summarized in the results. Building on this contribution, future research will focus on evaluating its scalability across different PV architectures and large-scale grid integration with real hardware setup.
Copyright (c) 2025 Muhammad Gul E. Islam, Mohammad Faridun Naim Tajuddin, Azralmukmin Azmi, Rini Nur Hasanah, Shahrin Md. Ayob, Tole Sutikno http://creativecommons.org/licenses/by-sa/4.0
Comparative analysis of optimization techniques for optimal EV charging station placement
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.
Copyright (c) 2025 Deepa Somasundaram, G. Prakash, N. Rajavinu, D. Lakshmi, P. Kavitha, V. Devaraj http://creativecommons.org/licenses/by-sa/4.0
Predictions of solar power using ensemble machine learning techniques
Predicting solar power production accurately is becoming more and more crucial for efficient power management and the grid's integration of renewable energy sources. Using data from an Australian photovoltaic (PV) power station, this study employs a variety of machine learning (ML) ensemble techniques, such as gradient boosting (GB), random forest (RF), and extreme gradient boosting (XGBoost), to forecast solar power production. ML models are developed utilizing pertinent information from electricity and meteorological data in order to forecast solar power. The predictive performance of trained ML models is verified in terms of metrics like mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R2). With higher R2 values and lower error results (MAE and RMSE), XGBoost performs better than GB and RF. Optimizing the hyperparameters of the XGBoost model significantly improves its performance. The tweaked XGBoost model shows a significant improvement in R2 (more than 5% to 10%) and error results (reduced MAE and RMSE by 0.01 to 0.06), when compared to other ensemble approaches. Compared to other ensemble approaches, the tuned XGBoost methodology is more robust and generates more accurate forecasts in solar power.
Copyright (c) 2025 Arangarajan Vinayagam, R. Mohandas, R. Jeyabharath, B. S. Mohan, Srinivasan Lakshmanan, C. Bharatiraja http://creativecommons.org/licenses/by-sa/4.0