Cell imbalances are a key challenge for lithium-ion (Li-ion) battery packs in electric vehicles (EVs) and other high-power applications, significantly reducing overall capacity, lifespan, and safety. This paper presents a novel single-inductor-based active cell balancing circuit and a specific switching algorithm to address this problem, redistributing charge from overcharged cells to undercharged ones. This methodology is designed to reduce balancing time and enhance system efficiency by directly targeting the most imbalanced cells. A detailed analysis of the circuit's design, operational principles, and control strategy is presented. The efficacy of the proposed technique is validated through simulation, demonstrating its ability to reduce a state-of-charge imbalance to less than 2% in 60 seconds under static conditions, maintaining cell balance effectively under static, charging, discharging, and dynamic conditions. This single-inductor topology offers a cost-effective and highly efficient solution, validated to improve the reliability, performance, and lifespan of Li-ion battery packs.

This article presents an innovative approach comprising coordinated subsystems for battery charging and water pumping within an agri-voltaic framework. The system is designed to improve efficiency and sustainability of agricultural operations by effectively utilizing solar photovoltaic (PV) energy. In this setup, PV array is integrated into agricultural land, enabling renewable energy generation without affecting crop production. Generated solar power is intelligently managed to serve two key functions: charging electric vehicle (EV) batteries and powering a water pump for irrigation. To ensure maximum energy extraction, an incremental-conductance-based maximum power point tracking (MPPT) algorithm is employed. A bidirectional dc-dc converter is used to regulate energy flow for EV battery charging. For water pumping subsystem, a synchronous reluctance motor drive (SyRMD) is controlled using a sensorless field-oriented control (FOC) strategy. For FOC of SyRMD, reference quadrature-axis currents are determined based on both the pump characteristics and available PV power. The rotor position required for sensorless control is estimated using a full-order sliding-mode observer integrated with a mixed second-third order generalized integrator-based phase-locked loop. Finally, performance of the proposed system is validated through experimental testing on a laboratory-scale prototype under varying solar irradiance conditions.

The growing adoption of non-conventional and distributed energy resources is driving a significant rise in demand for solar-based charging infrastructure. To address this need, this paper presents a multipurpose solar photovoltaic (SPV)-based, single-phase grid-connected electric vehicle charging station (SPV-GEVC). The proposed charger employs a soft-switching topology with a streamlined design to reduce component count. A novel switching technique is introduced to achieve zero-voltage and zero-current switching (ZVS/ZCS) during both turn-on and turn-off transitions without requiring additional hardware. The voltage source converter (VSC) not only regulates grid current but also provides harmonic suppression, reactive power compensation (RPC), and power factor correction (PFC) under varying operating conditions. To accurately extract the real power component of nonlinear load currents, a diffusion adaptive filtering algorithm with tanh farmwork (DAFATF)-based adaptive filter is applied, improving dynamic performance and minimizing mean square error under changing environmental conditions. Furthermore, adaptive power management ensures a reliable power supply to both EVs and emergency loads in grid-connected and stand-alone modes, while advanced grid synchronization techniques enhance stability during abnormal grid events.

This paper presents a generalized cascaded H-bridge (CHB) multilevel inverter topology powered by multiple independent photovoltaic (PV) sources, aimed at renewable energy integration and electric vehicle (EV) charging applications. The proposed configuration employs n H-bridge cells connected in series, where the number of output voltage levels is determined by the relation l = 2n + 1. This enables the generation of high-quality multilevel output waveforms with reduced total harmonic distortion (THD) and improved conversion efficiency compared to conventional two-level inverters. To ensure regulated power flow and seamless interfacing between distributed PV sources and EV battery systems, individual dual active bridge (DAB) converters are incorporated for each PV unit. These DAB stages provide galvanic isolation, bidirectional power transfer, and precise voltage matching, enabling efficient multiport energy management. The modular structure of the system allows scalability and independent control of each PV module, thereby enhancing flexibility, reliability, and adaptability to variable solar conditions. The effectiveness of the proposed approach is validated through MATLAB/Simulink simulations, where a seven-level inverter configuration is implemented and analyzed to confirm its suitability for efficient PV-based EV charging applications.

This work integrates the Sixth-Order Gaussian Kernel Least Mean Algorithm (SOGKLMA) and optimized Fractional Order Proportional-Integral-Derivative (FOPID) controller to enhance the Electrical Power Quality (EPS). To address the EPQ issues, the first key objective is to extract the Fundamental Quantity (FQ) from the disturbed grid by employing (SOGKLMA). A SOGKLMA enhances dynamic performance through higher-order filtering that effectively adapts to excess errors by incorporating a Gaussian kernel function. This accurately estimates the fundamental component to maintain the load voltage at its predefined value, thereby estimating reference load voltage generation and enabling faster convergence under severe transients. Moreover, a Fractional Order Proportional Integral-Derivative (FOPID) controller is employed to maintain the DC-link voltage at 300V. The effectiveness of FOPID is validated by integrating the objective function, Integral Time Square Error (ITSE), with a comparative study. The Artificial Protozoa Optimizer (APO)-based FOPID confirms superior performance compared with the recently developed Beluga Whale Optimizer (BWO), yielding a lower ITSE of 0.001528 compared with 0.044899. The simulation responses of the proposed APO tuned FOPID ensure its supremacy by comparing with a recently developed approach called the Beluga Whale Optimizer (BWO) with an enhanced transient response, including lessening the maximum overshoot (Mp = 5.7%), undershoot (Us = 3.44%), settling time (Ts = 0.15s), and steady-state (ess = 0.678%). Finally, system robustness is examined through experimentation with a laboratory-scaled-down DVR prototype.

Triboelectric nanogenerators (TENGs) have emerged as an effective approach for harvesting low-frequency mechanical energy for self-powered wearable electronics and human-machine interface applications. Polydimethylsiloxane (PDMS), a widely used tribonegative elastomer, offers excellent flexibility and charge retention; however, its inherently low dielectric constant and limited charge transport capability restrict the attainable power density. In this work, a MAX-phase-enabled strategy is introduced by incorporating layered Ti3AlC2 as a functional filler within the PDMS matrix to overcome these intrinsic limitations and enable synergistic charge generation and transport. Unlike conventional carbon- or ceramic-based fillers, the MAX phase uniquely integrates metallic conductivity, ceramic-like mechanical robustness, and a two-dimensional layered structure with tunable surface chemistry. This hybrid architecture provides abundant interfacial polarization sites, efficient charge trapping centres, and accelerated carrier migration pathways, while retaining mechanical durability under repeated contact-separation cycles. Systematic compositional optimization reveals that a 10 wt% Ti3AlC2 loading maximizes the dielectric constant, electrical conductivity, and surface charge density without inducing percolation-driven performance degradation observed at higher filler concentrations. Kelvin probe force microscopy (KPFM) directly probes nanoscale charge dynamics, revealing faster charge redistribution and suppressed localized charge accumulation in the PDMS-MAX composite compared to pristine PDMS, thereby validating the proposed charge diffusion and migration mechanism. Consequently, the optimized TENG delivers an output voltage of 230 V, a short-circuit current of 28.5 & micro;A, and a peak power density of 20.5 W m-2 from a compact 20 mm & times; 20 mm device under an input force of 10 N at 2.5 Hz. Finite Element Analyses (FEA) further corroborate the enhanced electric field and surface charge density. The device exhibits excellent durability over 15 000 cycles, stable operation under varying humidity and aging conditions, and effective performance in capacitor charging, LED illumination, and real-time human motion sensing. This study establishes MAX-phase-filled elastomers as a robust and mechanistically distinct platform for high-performance triboelectric energy harvesting beyond conventional filler-based enhancements.

Increasing electrical energy demand driven by rapid population growth has placed significant pressure on conventional energy resources. Moreover, the depletion of fossil fuels and rising environmental pollution have accelerated the need for alternative, renewable energy sources such as solar, wind, biomass, and tidal energy. This paper presents a charger model that integrates these non-conventional energy sources with the electrical grid. A single-phase grid-interactive charger is developed with solar photovoltaic (SPV) for electric vehicle (EV) battery. In grid-connected charger with SPV array, conventional adaptive controllers often become ineffective under nonlinear load conditions and rapid fluctuations in solar irradiance, adversely affecting convergence speed and system stability. To address these challenges, a Sparse Andrew's Sine Norm Promoting Adaptive Algorithm (SASNPAA)-based control mechanism is proposed. The SASNPAA approach enhances disturbance rejection capability and ensures faster convergence, resulting in improved power quality and reliable operation of the PV-grid fed charger. The proposed control strategy estimates the weight of fundamental component of load current. This estimated weight is further integrated with the SPV contribution and DC-link voltage component for effective control of the voltage source converter (VSC). Additionally, an EV battery is incorporated to enable bidirectional power flow under dynamic conditions, including charging and discharging, along with varying load demands. The system is tested under varying solar insolation, grid disturbances, and load conditions to demonstrate its robustness. Simulated results are obtained using MATLAB/Simulink which confirm fast convergence and reduced mean square error (MSE). Experimental validation on a laboratory-scale setup further demonstrates the practical applicability of the proposed method.

Grid-integrated renewable energy systems that com bine wind generation with auxiliary sources often experience power-quality degradation due to wind-speed variations, non linear loads, and interactions among multiple energy units. To address this, a grid-side converter (GSC) based novel control strategy is proposed which uses Laplace Function–Generalized Soft Root Sign (LF-GSRS) adaptive filter providing effective harmonic mitigation, fast dynamic response and effective power coordination between energy sources. The GSC integrates a doubly fed induction generator (DFIG)-based wind energy conversion system (WECS) with aproton exchange membrane (PEM) fuel cell stack and battery energy storage to enhance grid resilience and power quality. The steady-state accuracy and convergence speed are improved by the LF-GSRS algorithm using sparsity conscious weight tuning and non-linear error transformation. The presented controller achieves a total harmonic distortion (THD) of 1.18% simulated in MATLAB/Simulink and real-time validation based on OPAL-RT. Comparative analysis demonstrates that LF-GSRS based GSC control outperforms conventional GSRS, Logistic Distance Metric Adaptive Filter (LDMAF), Logarithmic Hyperbolic Cosine Adaptive Filter (LHCAF), Least Mean Mixed Norm (LMMN), conventional GSRS, and d–q-based GSC control methods.

This paper presents a teaching-learning-based optimization (TLBO) approach to minimize conduction losses in switches and the transformer windings of a triple active bridge (TAB) DC-DC converter. In a typical TAB system, the grid, photovoltaic (PV), and battery (or load) sides operate with fixed DC link voltages. This constant voltage condition creates a large voltage difference across the switches and passive components under certain conditions of battery voltage which increases the transformer current stress during operation. As a result, the converter suffers from higher power losses, lowering its overall efficiency. To address this issue, the proposed method applies TLBO to adjust the grid-side DC link voltage dynamically according to the battery voltage, while keeping the PV DC link voltage and battery current fixed. The optimisation is carried out for different battery voltage levels that vary with state of charge ( SoC) from 0% to 100%. TLBO algorithm is used to determine the optimal grid side DC voltage for each SoC condition of the battery. This selection aims to minimise the total losses of the TAB converter while maintaining stable operation. The proposed strategy demonstrates how intelligent voltage adjustment can significantly reduce conduction and leakage losses, thereby improving the efficiency of TAB converters. This approach is especially relevant for renewable energy systems and hybrid storage applications, where balancing efficiency and reliability is critical.

In this paper a new adaptive filter EFOLMS introduced in a grid connected hybrid wind energy conversion system which is used for extraction of the fundamental component of nonlinear load current. Proposed topology operates on common DC bus that integrates a solar PV array, PMSG wind turbine wind energy conversion system and battery energy storage system (BESS). Power exchange between battery and DC link is regulated by a bidirectional DC-DC converter helps overall system stability and reliability. This EFOLMS filter provides precise harmonic mitigation and power quality improvement. Performance of EFOLMS filter were checked & studied on different dynamic condition as varying wind speed, solar irradiation and load unbalance. The outcomes of this extensive simulation studies confirms the extraction accuracy and response were superior under dynamic condition compared to conventional techniques which makes EFOLMS filter well suited for modern hybrid renewable energy system.

This research proposes an echo-state network (ESN)-driven strategy to improve power quality (PQ) in utility grids through dynamic voltage restorer (DVR) implementation. With the growing integration of nonlinear or critical loads, PQ degradation has become a pressing concern. The ESN-based algorithm efficiently estimates fundamental voltage quantities from a disturbed grid and supports PQ improvement. The proposed approach integrates an ESN for real-time estimation of fundamental voltage components and an adaptive interval type-2 fuzzy logic (IT2 FLC) controller for DC-link voltage stabilization. This dynamic controller mitigates voltage stress and maintains a robust DC voltage level, crucial for generating the reference load voltage. The controller gains are optimized using the bioinspired gorilla troops algorithm (GTA), which enables a low computational burden and enhanced adaptability to grid disturbances. Additionally, it is noteworthy that the proposed technique reduces the values of settle and rise time (0.15 s, 0.19 s), overshoot and undershoot (3.28%, 3.56%), and recovery time to 0.421 s. These statistics indicate that it outperforms the traditional synchronous reference frame-proportional integral (SRF-PI) and LMS-IT1 FLC strategies. The hybrid ESN and GTA-IT2 FLC framework allows online gain tuning, ensuring sinusoidal load voltage and a significant reduction in total harmonic distortion from 12.19% to 2.74%. The proposed method delivers superior performance even in disturbed grid conditions, offering fast rise time, minimal overshoot, and less steady-state errors compared to conventional SRF-PI and LMS-fuzzy logic controller. The findings obtained from a scaled-down prototype of DVR highlight the approach's effectiveness.

This paper presents a high-efficiency photovoltaic (PV)-driven water pumping system that combines a sensorless maximum power point tracking (MPPT) algorithm with a three-level T-type inverter. The proposed MPPT eliminates the need for current sensors by employing a Luenberger observer for current estimation, thereby reducing hardware cost and improving system reliability. The T-type inverter enhances power quality and overall system efficiency by lowering switching losses and total harmonic distortion (THD). The complete PV-boost-inverter-motor configuration is modeled and simulated in MATLAB/Simulink. Simulation results demonstrate stable operation, effective DC-link voltage regulation, and improved output waveform quality under variable solar irradiance. The proposed system offers a simple, cost-effective, and reliable solution for sustainable solar water pumping applications in rural and off-grid areas.

This paper presents a high speed a unified affine-projection-like adaptive algorithm (UAPLA) based adaptive algorithm controller for 3-phase grid connected solar photovoltaic (3-P-GC-SPV) system. System performance in the dynamic and steady state condition with the proposed UAPLA is much better than synchronous reference frame (SRF) theory, least mean square (LMS) based controllers. UAPLA can achieve lower mean square error (MSE) under impulsive noise. A gradient descent method is used for updating the active load component. The system performance is tested at various test points (TP). After satisfactory results at various test point the entire system is modeled and run in the MATLAB-SIMULINK software. The obtained results are compare with results of standard least mean square (LMS) and synchronous reference frame-phase locked loop (SFR-PLL) to demonstrate the superiority of UAPLA based adaptive algorithm controller.

In this paper, bidirectional charging system using a Dual Active Bridge (DAB) converter are enhanced by employing a Modified Notch Filter Second Order Generalized Integrator Phase-Locked Loop (MNFSOGIPLL) technique. MNFSOGI-PLL improves various critical aspects, including power quality, suppression of various harmonics, and elimination of DC offset otherwise these factors contribute to synchronization errors, control instability, and transformer core saturation. Such issues typically arise during grid disturbances, irregular power generation, and quality of power. The system configuration includes a connected nonlinear charging load at the point of common coupling (PCC), with a Front-End Converter (FEC) and delivering a regulated power to the DAB converter. A battery is interfaced with the DAB to facilitate bidirectional energy flow, enabling both Grid-to-Vehicle (G2V) charging and Vehicle-to-Grid (V2G) power transmission. The MNFSOGI-PLL effectively mitigates DC offset and harmonics, ensuring accurate synchronization and stable operation. Simulation results confirm the improved performance of the proposed topology in terms of power quality, synchronization accuracy, and efficient bidirectional power transfer.

An EV charging system combined with renewable energy improves electricity reliability, promotes renewable sources, and reduces environmental pollution. To balance and control power flows among renewable energy sources, EVs, and the grid, an isolated multi-port converter is required to charge EVs. In this proposed system, an AFEC is used on the grid side to keep a steady DC link voltage using PI and PR controllers. Without using a dedicated boost converter, the solar PV operates at MPPT to continuously generate maximum power, and the EV's batteries are charged in both CC and CV modes according to the EV battery SoC. The grid-side H-bridge functions as the reference bridge, providing the reference angle for synchronization. The PV side H-bridge delay depends on the MPPT reference voltage and the associated PI control action, while the EV battery side H-bridge delay depends on its operation mode (CC or CV) and the related PI controller. These coordinated delay angles generate the appropriate switching pulses for each bridge, ensuring efficient bidirectional power transfer and stable DC-link operation. Proposed multi-port TAB converter with multidirectional power flow control is modelled in MATLAB/Simulink and validated through real-time and hardware prototype results. A detailed discussion of modelling, implementation, and results is provided.

This paper presents a three-phase grid-integrated photovoltaic (PV), battery, and electrolyser system. This paper employs a robust, widely-linear quaternion multiband structured subband adaptive filter (RWL-QMSAF) algorithm for voltage source converter (VSC) control. The RWL-QMSAF algorithm mitigates harmonics, maintains a unity power factor, provides reactive power compensation, and addresses load unbalancing. Stochastic Frank-Wolfe for Constrained Bilevel Optimization (SFWCBO) is employed for bidirectional DC-DC converter control. The bilevel strategy balances system-level objectives, such as minimizing power losses and maintaining battery current tracking, to achieve efficient energy management. The system aims to enhance renewable energy utilization, enable efficient hydrogen production, and ensure reliable power quality under dynamic grid conditions. Simulation results validate the approach, demonstrating good dynamic response in load dynamics, PV insolation change, battery, and electrolyser dynamic conditions. The system achieves improved stability with reduced total harmonic distortion (THD) and enhanced hydrogen production efficiency. The THD of the grid current is less than 5 % which satisfies the IEEE519-2022 standards. The proposed system provides a scalable and intelligent solution for the next generation of renewable-integrated hybrid energy systems.

In this paper an innovative control strategy for a sustainable Light Electric Vehicle (LEV) drive mechanism that utilizes both solar power from PV array and a rechargeable battery for green energy operation. The vehicle is propelled by a PM-Assisted Synchronous Reluctance Motor (PMa-SynRM), selected due to its superior efficiency and sturdy torque characteristics. During accelerating and decelerating modes, a Bi-Directional DC-DC converter (BDDC) is helps to control the power flow and while maintaining the DC-link voltage unchanged, offering smooth energy transfer across the SPV array, storage device (battery) and motor. MTPA approach is developed to improve the current utilization and enhance the torque production. Furthermore, the design removes the periodic torque ripples that arise from the inverter harmonics and magnetic saliency by employing a Repetitive Current Control (RCC) technique. RCC improves current and torque waveforms quality and ride smoothness by observing, learning and removing repetitive troubles by delayed error feedback. For precise voltage synthesis, the inverter section employs Sinusoidal Pulse Width Modulation (SPWM). The MATLAB/Simulink platform is employed to represent and simulate the entire system. The model incorporates the simulation outcomes, the proposed sustainable LEV drive system greatly improves current control precision, torque ripple reduction and overall energy efficiency.

As the integration of heterogeneous renewable energy sources with grid poses significant challenges due to resource intermittency, nonlinear loads, and stringent power quality requirements. This paper presents a hybrid system comprising a doubly-fed induction generator (DFIG) based wind energy conversion system, a solar photovoltaic (SPV) array, and a battery energy storage system (BES) coordinated through a novel Maximum Versoria Criterion–based Adaptive Filter (MVCAF). Control framework enables harmonics mitigation, seamless power sharing, and reliable grid current regulation under dynamic operating conditions. Unlike conventional adaptive approaches such as least mean square (LMS), normalised LMS (NLMS), logarithmic hyperbolic cosine adaptive filter (LHCAF), and logistic distance metric adaptive filter (LDMAF), proposed MVCAF demonstrates faster convergence, lower steady-state error, and enhanced immunity to impulsive disturbances. To evaluate real-world performance, 24-hour averaged wind and solar data from Saurashtra region of India, obtained from national institute of wind energy (NIWE), national solar Radiation database (NSRDB), and national aeronautics and space administration (NASA) power repositories, are employed. Simulation studies confirm that MVCAF maintains total harmonic distortion (THD) of grid current at 1.2%, within IEEE-519 standards, even under unbalanced nonlinear loads. Experimental validation using a laboratory prototype further corroborates effectiveness of proposed scheme in achieving stable DC-link voltage, reliable BES operation through SoC-governed bi-directional control, and high-quality current injection. Results establish MVCAF as a scalable and practical solution for hybrid renewable integration in modern power system with high renewable penetration.

Series-connected Lithium-ion battery packs are the standard for high-voltage electric vehicle (EV), yet they face an unavoidable physical limitation: manufacturing variations in capacity and impedance inevitably cause cell imbalances. While traditional balancing relies on terminal voltage to detect these disparities, this method becomes unreliable during the flat-voltage plateau regions characteristic of Li-ion discharge curves. We present a robust active balancing architecture that sidesteps this limitation by utilizing State-of-Charge (SoC) rather than voltage as the control metric. The system employs a distributed bidirectional Buck-Boost converter topology configured for a pack-to-Cell energy transfer. Rather than dissipating excess energy as heat, the controller actively routes power from the main DC bus directly to the weakest cells. To ensure control precision, we integrated a Feedforward Neural Network (FNN) estimator that predicts SoC with a Root Mean Square Error (RMSE) below 0.5%. We validated this framework on a 12-cell series string model in MATLAB/Simulink. modes: static (resting), dynamic charging, and discharging. This topology offers a cost-effective and highly efficient solution, validated to improve the reliability, performance, and lifespan of Li-ion battery packs.