The sustainability of wireless sensor networks critically depends on intelligent and efficient charger deployment. This paper proposes a two-stage optimization framework that determine the total number of chargers needed to recharge and the optimal position of charger. In the first stage, a hybrid algorithm combining degree-based saturation and Grundy coloring efficiently determines the minimal number of chargers required. In the second stage, an Enhanced Aquila Optimization algorithm, inspired by eagle hunting behavior, identifies optimal charger locations under coverage and power constraints. Experimental results show that the enhancement method improves coverage by 6% compared to the standard algorithm. The Enhanced Aquila Optimization achieves 99% sensor coverage with faster convergence than Raindrop (82%), Grey Wolf (85%), Black Hole (89%), Dragonfly (89%), and standard Aquila Optimization (93%). Statistical analysis confirms the significant performance difference between the proposed and existing algorithms. Overall, the proposed approach provides a practical, scalable, and energy-efficient solution for sustainable wireless sensor network deployment.
This case report follows M.K., a 31-year-old man with spastic paraplegia who sustained a second-degree skin burn after his mobile phone charger cord was trapped beneath his back while hospitalized. This led to complications that necessitated three hospitalizations, multiple surgical interventions, and treatment for a hospital-acquired infection caused by multidrug-resistant bacteria. Nearly every patient brings a mobile phone (and, often, a charger) into the hospital, and the use of these and other electronic devices carries risks. Although numerous media and medical publications have reported serious and even fatal injuries caused by mobile phones and their chargers in domestic settings, reports of such incidents in hospitals remain scarce. This case study discusses the authors' experience in addressing various patient safety and risk management issues associated with patients' use of mobile phone chargers in a general hospital setting.
The shift towards electric vehicle (EV) adoption requires a solid and efficient charging infrastructure. A crucial part of these systems is the DC-DC converter, which connects the power grid or energy storage with the vehicle's battery pack. Conventional single-module DC-DC converters have inherent limitations in addressing the rising demands for higher power levels, enhanced efficiency, and greater operational reliability in current EV charging applications. The interconnection of several DC-DC converter modules is a promising method to overcome these obstacles, enabling increased power output and better fault tolerance. This research explores the performance of a dual-stage bidirectional EV charger featuring different interconnections. A comprehensive small signal averaged model of the DC stage of the dual-stage EV charger is presented for both the single converter and two converters connected in an interleaved configuration, including a stability analysis. Further, a relative assessment of the charger's performance with the various interconnections is performed, and the results are detailed in terms of various performance metrics, including efficiency, ripple percentage, and source power factor. The conclusions obtained from the comparative analysis are explicitly illustrated to facilitate the selection of the appropriate interconnection for a given application.
When it comes to developing a strategy for EV charging networks, improving the utilization and planning of charging stations becomes critical with the fast growing market for electric cars. Improvement of power quality has the largest effect on the performance and dependability of EVs essential for their large-scale implementation. Issues like low power quality and very high THD present in conventional systems are addressed in this research through a two-stage EV charger consisting of an active power factor correction (PFC) front-end and an LLC resonant DC-DC converter, achieving an input power factor of 0.98, THD of 5% and efficiency of 95%. Applying Support Vector Regression (SVR) as the type of a supervised machine learning algorithm, we employ a predictive maintenance model which processes real-time information and decreases downtimes by a third. Based on the identified shortcomings of ordinary diode bridge rectifiers, this approach extends these advantages and enhances the functionality of EV chargers dramatically.
We present a 6.78-MHz wireless, mode-convertible single-stage resonant charger (SSRC) that provides constant current/constant voltage charging in an extended coupling range for implantable biomedical devices. To extend the charging range in a wireless inductive link for reliable and seamless power transfer, it automatically switches between normal and resonant modes (NM and RM) by sensing current variation induced from the frequency splitting phenomenon, thereby achieving an extended charging distance up to 104.34%. In addition, the proposed charger limits the maximum current to avoid excessive charging, that potentially degrades the battery's health. The prototype SSRC has been fabricated using a 180 nm bipolar/CMOS/DMOS high voltage process with an active area of 0.575 mm2. The performance of the fabricated chip has been characterized on benchtop and ex vivo using a custom-designed 3-D printed fixture. The measurement results verified efficient power delivery to batteries while extending the charging distance from 23 mm to 47 mm in air and a 20-mm-thick pork slice without over-current issues. The measured peak power conversion efficiencies were 89.42 and 76.6% in the NM and RM, respectively.
This paper proposes a multi-stage modernization strategy based on return on investment (ROI) analysis for integrating EV chargers, battery storage and renewable energy sources (RES) into DC traction grids. A power converter interface (PCI), which consists of multiple power converters and a Smart Grid-based integration concept is proposed for modifying the existing infrastructure. The approach combines technical and economic considerations to support decision-making under varying regulatory and operational conditions. The effectiveness of the approach is evaluated using representative cost models, showing that additional services enabled by the PCI, such as EV charging, power quality improvement and RES integration, can achieve ROI values in the range of 10-20%, depending on electricity tariffs, penalty coefficients and utilization levels. The results indicate that, under suitable conditions, staged modernization of traction substations can improve power quality, reduce overloads and increase infrastructure utilization.
This paper presents a Single-Inductor Bidirectional Converter (SIBC) for unified onboard Electric Vehicle (EV) charging, integrating grid, battery, and wireless ports within a single power stage. The topology enables native Grid-to-Vehicle (G2V), Wireless-to-Vehicle (W2V), and Vehicle-to-Wireless (V2W) operation without hardware reconfiguration, eliminating cascaded converter-inverter structures. Non-ideal steady-state and small-signal models are developed, revealing mode-dependent dynamics including a right-half-plane zero in V2W mode that constrains bandwidth. A two-loop Average Current-Mode Control (ACMC) is proposed to mitigate this limitation, achieving 40× bandwidth improvement over conventional voltage-mode control. Parametric sensitivity analysis of inductor equivalent series resistance establishes quantitative design boundaries for sustaining conversion efficiency above 90%. Scalability assessment to 3 kW operation demonstrates compatibility with silicon carbide devices and a bridgeless totem-pole PFC front-end achieving THD less than 4% and power factor higher than 0.99, satisfying IEC 61000-3-2 Class A with higher than 35 dB ripple rejection at the battery terminals. Experimental validation using a 136 W prototype achieves 93.4% transmitter and 95.07% receiver DC-DC efficiency, with an overall end-to-end efficiency of 75.03% including an 84.5% wireless link. A 1 kW interim hardware test confirms scalable operation, while simulation validates CC-CV battery charging compatibility. The results demonstrate that the SIBC architecture is not the dominant source of system losses and provides a compact, scalable foundation for advanced bidirectional EV charging systems.
This study presents the design and performance evaluation of a bidirectional electric vehicle charging system integrating solar photovoltaic energy with an Artificial Neural Network based control strategy. The proposed architecture employs a modified Single-Ended Primary Inductor Converter capable of supporting both Grid-to-Vehicle and Vehicle-to-Home operating modes while maintaining stable bidirectional power flow between the grid, photovoltaic source, and EV battery. The ANN controller dynamically regulates the duty cycle of the MOSFET switches using battery current feedback and reference current signals, enabling adaptive control under varying solar irradiance and grid conditions. Simulation results indicate that the proposed system achieves charging efficiencies above 90% while maintaining stable operation for both 72 V and 240 V EV battery configurations. Compared with conventional proportional–integral control approaches, the ANN controller demonstrates faster transient response and improved current regulation during dynamic operating conditions. The integration of solar photovoltaic energy further reduces reliance on grid power and enhances renewable energy utilization in EV charging infrastructure. These results indicate that the proposed ANN-controlled bidirectional charging system provides an efficient and flexible solution for renewable-integrated EV charging applications.
Environmentally induced decoherence leads to irreversible energy loss during the active charging and discharging processes of quantum batteries (QBs). To address this issue, we propose a charging protocol utilizing the nonlocal coupling properties of giant atoms (GAs). In this Letter, both the QB and its charger are implemented as superconducting GAs with multiple nonlocal coupling points to a shared microwave waveguide. By engineering these atoms in a braided configuration, where their coupling paths are spatially interleaved, we realize lossless energy transfer dynamics. This is achieved by exploiting destructive interference to suppress waveguide-mediated dissipation while simultaneously preserving coherent interactions between the charger and the QB. The charging properties of separated and nested coupled configurations are also investigated. The results show that these two configurations underperform the braided configuration. Additionally, we propose a long-range chiral charging protocol that facilitates unidirectional energy transfer between the charger and the battery, with the capability to reverse the flow direction by modulating the applied magnetic flux. Our results provide guidelines for implementing a decoherence-resistant charging protocol and remote chiral QBs in circuits with GA engineering.
Tribo-electrostatic separation (TES) of proteo-lignocellulosic biomass residues such as oilseed meals holds promise for sustainable classification of their ingredients with retained functionalities. However, there is a significant gap in our understanding of how the tribo-charging behavior of oilseed meal constituent particles (i.e., protein, cellulose, lignin, and hemicellulose) correlate with their enrichment during TES. This research utilized an online tribo-charge measurement system to investigate the charging behavior of soybean protein isolate, microcrystalline cellulose (MCC), alkaline lignin, de-alkaline lignin, and xylan using four different tribo-charger materials of polytetrafluoroethylene (PTFE), polyvinyl chloride (PVC), nylon, and copper alloy C12200 at different lengths under laminar airflow, considering different particle dosing rates (g/h). Accordingly, all protein and fiber powders acquired positive charge densities (nC/m2) with PTFE tubes and negative charges with nylon tubes, irrespective of length or particle dosing rates. In contrast, all powders in contact with PVC and copper alloy tribo-chargers exhibited distinct charging behaviors, where soybean protein had a high positive charge, MCC was negatively charged, and lignin particles remained almost neutral. The charge-to-mass ratios (nC/g) of protein and dietary fiber particles were highest at lower particle dosing rates with long tribo-charger tubes, indicating that particle-wall collisions contribute more to charge acquisition in diluted flows, while particle-particle collisions are more significant in concentrated flows. The deduced triboelectric series was: (+) Nylon> Soybean protein> Xylan> Alkaline lignin> De-alkaline lignin> Copper alloy> PVC > MCC > PTFE (-). These findings will be utilized to assess oilseed meal TES, providing insights into the key factors that correlate the particles' tribo-charging behavior with their separation behavior.
The widespread use of electric vehicles (EVs) strongly depends on the construction of EV charging stations (EVCSs) that should fulfill charging requests while considering various economic issues. This paper investigates the optimal sizing of an EVCS integrated with renewable energy generators, an energy storage system (ESS), and multiple types of chargers. The optimal sizing problem is formulated as a mixed-integer linear program that takes seasonal variations of renewable energy generation into account, while aiming to minimize the equivalent annual total cost of the EVCS. Then, a novel charging scheduling strategy for EVs is developed by considering charger utilization efficiency, thereby reducing unnecessary expenses. Finally, different case studies are analyzed to verify the effectiveness and economic performance of the proposed strategy. The results indicate that incorporating EV charging scheduling into the optimal sizing problem can improve the economic performance of the EVCS.
Going beyond traditional chemical batteries, we investigate a quantum battery system under both external driving and dissipation. The system consists of a coupled two-level charger and battery immersed in non-equilibrium fermionic reservoirs. By considering the changes in the energy spectrum induced by external driving and charger-battery coupling in a non-perturbative manner, we go beyond the secular approximation to derive the Redfield master equation. In the non-equilibrium scenario, both charging efficiency and power of the quantum battery can be optimized through a compensation mechanism. When the charger and battery are off-resonance, a significant chemical potential difference between the reservoirs, which characterizes the degree of non-equilibrium, plays a crucial role. Specifically, the charger's frequency should be higher (lower) than that of the battery when the average chemical potential is negative (positive) to achieve enhanced charging efficiency and power under strong non-equilibrium conditions. Remarkably, the efficiency in the non-equilibrium case can surpass that in the equilibrium setup. Moreover, we find no positive correlation between entanglement and efficiency; therefore, entanglement is not necessary to enhance the performance of quantum devices. Our results provide insights into the design and optimization of quantum batteries in non-equilibrium open systems.
Wide-bandgap mixed-halide perovskite photovoltaic modules show strong potential for portable chargers, building-integrated photovoltaics, agrivoltaics, and tandem systems, but large-area processing exacerbates crystallization heterogeneity, surface defects, and halide phase segregation. Conventional spin-coating passivation fails to deliver uniform interfacial control at scale. Here, an industrially inspired solution-soaking quenching technique is introduced, in which hot blade-coated wide-bandgap perovskite films ( ~ 30 cm2) are immersed in cold SrI2/isopropanol. It enables rapid surface reconstruction and uniform surface passivation, enhances photoluminescence uniformity, improves crystallinity, reduces roughness, and stabilizes halides via gradient Sr2+ incorporation. These effects mitigate tensile stress, optimize energy-level alignment, and suppress light-induced phase separation. Methylammonium-free wide-bandgap small-area (0.04 cm2) devices achieve efficiencies up to 22.03%, while a 10.13 cm2 module delivers 20.32% efficiency with excellent operational stability. The method is versatile across wide-bandgap perovskite compositions and enables practical applications including portable chargers, semitransparent modules (18.41% bifacial equivalent efficiency), and >27% efficient all-perovskite tandem windows.
India's transition to electric mobility demands charging infrastructure that is cost-efficient, grid-compatible, and capable of integrating solar generation. Existing studies typically examine demand forecasting, PV utilisation, charging-topology behaviour, and economic viability in isolation, limiting their relevance for large-scale deployment. This work proposes a unified co-design framework that jointly optimises charging-station siting, charger sizing, PV allocation, and operational economics under India's tariff structure. Hourly EV demand is predicted using a hybrid forecasting model that combines Temporal Fusion Transformers with Graph Neural Networks to capture spatial and temporal variations. Solar-generation modelling, topology-based charger efficiencies, and distribution-grid constraints are incorporated into a techno-economic formulation. A multi-objective optimisation approach (NSGA-II) identifies configurations that minimise cost, reduce peak grid loading, and maximise solar utilisation. The framework is demonstrated using a representative mixed urban-highway region. Results show a 28-35% reduction in peak grid load, a 40-70% improvement in utilisation, and a 12-18% decrease in the levelised cost of charging compared with non-optimised deployments. The findings highlight the importance of integrated planning that aligns solar availability, demand behaviour, and tariff incentives. The proposed methodology offers a scalable decision-support tool for policymakers, utilities, and private developers planning future EV charging networks in India.
Unmanned Aerial Vehicles (UAVs) are emerging as a fundamental part of Flying Ad Hoc Networks (FANETs). However, owing to the limited energy capacity of UAV batteries, wireless power transfer (WPT) technologies have recently gained interest from researchers, offering recharging possibilities for FANETs. Based on this background, this study highlights the need for wireless charging to enhance the operational endurance of FANETs in Internet-of-Things (IoT) environments. This review investigates WPT power replenishment to explore the dynamic usage of UAVs in two ways. The former is for using a UAV as a mobile charger to recharge the ground nodes, whereas the latter is for WPT applications in in-flight (UAV-to-UAV) charging. For the two research domains, we describe the different methods of WPT and its latest advancements through the academic and industrial research literature. We categorized the results based on the power transfer range, efficiency, wireless charger topology (ground or in-flight), coordination among multiple UAVs, and trajectory optimization formulation. A crucial finding is that in-flight UAV charging can extend the endurance by three times compared to using standalone batteries. Furthermore, the integration of IoT for the deployment of a clan of UAVs as a FANET is rigorously emphasized. Our data findings also indicate the present and future forecasting graphs of UAVs and IoT-integrating UAVs in the global market. Existing systems have scalability issues beyond 20 UAVs; therefore, future research requires edge computing for WPT scheduling and blockchains for energy trading.
Reliable electric vehicle (EV) charging depends on both sufficient infrastructure and stable power quality. In real-world distribution networks, single power quality (PQ) disturbances, such as frequency deviation, harmonics, temporary undervoltage/overvoltage, transient events, voltage deviation, interruptions, sags, and swells can significantly influence charging efficiency, equipment safety, and battery longevity. However, existing public resources rarely provide standardized, high-resolution datasets linking specific PQ disturbances to EV charging performance under controlled and replicable conditions. We present a dataset that systematically evaluates the impact of ten representative single PQ disturbances on EV charging. Test cases were designed following IEEE standards, and experiments were conducted on a proprietary full-vehicle charging test platform to capture authentic charging responses. The dataset includes grid-side voltage and current waveforms, charger telemetry, and battery charging profiles at high temporal resolution, covering the most representative AC charging scenarios. Technical validation demonstrates the reliability of data collection, consistency across repeated tests, and alignment with PQ definitions. The dataset provides foundation for: (i) benchmarking diagnostic and classification algorithms for PQ events, (ii) quantifying the impact of specific disturbances on charging current and efficiency, and (iii) supporting the design of robust EV chargers and grid-integration strategies. While the present release focuses on single disturbances, it establishes a reference framework for future studies on more complex or composite PQ scenarios.
Wireless sensor networks play a vital role in a wide range of modern applications, from environmental monitoring to industrial automation. A key challenge in maintaining the long-term functionality of these networks lies in effective energy management, where recharging sensors is often more practical and economical than frequent battery replacement. One critical aspect of this process is the optimal placement of chargers to ensure maximum sensor coverage while minimizing deployment costs. This paper presents a hybrid optimization framework that combines graph-theoretical concepts-specifically the Degree of Saturation approach-with the Enhanced Grey Wolf Optimization algorithm to solve the charger placement problem in WSNs. The Degree of Saturation method identifies independent groups of sensors to reduce the number of chargers required, while Enhanced Grey Wolf Algorithm determines their optimal spatial positions to ensure efficient energy replenishment. Extensive simulations demonstrate the superiority of the proposed method over conventional techniques. Compared to wavelet-based approaches such as Haar (83%), Daubechies 2 (85%), Biorthogonal (86%), and Symlets 8 (85%), as well as evolutionary algorithms like Raindrop (87%) and Blackhole (91%), the proposed Enhanced Grey Wolf Optimization-based method achieves a significantly higher efficiency of 97%. These results highlight the robustness and effectiveness of the proposed approach for real-world Wireless sensor networks deployment.
The rapid adoption of electric vehicles (EVs) presents significant challenges regarding the stability of the power grid, involving increasing the peak demand as well as voltage deviation, power quality (PQ) degradation, harmonic distortion, and reactive power mismatch. This paper proposes an integrated artificial intelligence solution to optimize EV charging, utilizing predictive forecasting and adaptive control, to ensure compliance with the power quality standards of grid power. In the artificial intelligence solution, we employ a Temporal Fusion Transformer (TFT) to forecast multi-horizon charging demand, with a Proximal Policy Optimization (PPO)-based deep reinforcement learning agent to coordinate smart charging. The proposed artificial intelligence solution comprises a multi-objective power quality optimizer with Distribution Static Compensator (D-STATCOM) capabilities, allowing for real-time harmonic filtering and reactive power compensation. The proposed artificial intelligence solution was validated using the MATLAB platform, utilizing simulations comprising a 10 MVA distribution feeder with 20 EV chargers, with power ratings ranging from 7 to 150 kW. The simulation results confirmed substantial improvements in the grid’s performance, including a reduction in energy losses by 59.7%, a decrease in instances of load-shedding by 75.4%, and an improved power factor from 0.910 to 0.969. The performance measures used to indicate power quality observed substantial improvements, with the Total Harmonic Distortion (THD) reduced from 6.8% to 4.6%, limited to a maximum of 3.03% (as prescribed by IEEE-519), and an observed 7.2% to 4.1% decrease in voltage deviation. The proposed AI-based strategy optimally balances charging efficiencies, cost minimization, and power quality improvements. The proposed AI-driven approach successfully balances charging efficiency, cost optimization, and power quality enhancement, providing a scalable solution for large-scale EV integration in smart grid environments.
We survey a representative sample of ∼1000 rural Michigan residents to understand their constraints and attitudes around electric vehicle (EV) adoption and find that only 5% of respondents would choose a battery EV (BEV) as their next vehicle, a smaller number than is found in national surveys. Rural residents face real constraints to adopting EVs. However, their attitudes also correlate with their interest in EVs and they can be misinformed: 42% of respondents think they cannot meet their driving needs by a level 2 home charger while we estimate 75% of those respondents could; 30% of respondents think there is no public charger near their home while we estimate there is a public fast charger within 5 miles for 49% of these respondents; and 35% prefer an internal combustion engine vehicle because of cost, while we estimate a suitable BEV exists within their reported budget for 65% of these respondents. In general, there is room for information interventions that reduce inconsistent perceptions around BEVs. We find that respondents may be falsely pessimistic about BEV feasibility (56% of all respondents). A smaller percentage of respondents are also falsely optimistic (15% of all respondents).
High-frequency non-intrusive load monitoring provides detailed harmonic information for appliances' power disaggregation, and machine-learning approaches have demonstrated good performance in this task. However, these methods provide little transparency regarding the information structure of the aggregate signal. To address this, this paper models NILM as a coding-decoding process and applies information-theoretic measures to quantify uncertainty, recoverability, temporal contribution, and inter-appliance masking effects in aggregate signals. In the analyzed dataset, transfer entropy suggests negligible temporal gains, which is consistent with the observed effectiveness of pointwise models such as Random Forest. Moreover, conditional mutual information emphasizes the asymmetric masking relationships between appliances, with the laptop charger acting as a dominant interferer in the considered measurements. These findings are validated through a Random Forest regression model with minimum Redundancy Maximum Relevance feature selection. The results show that the mutual information between an appliance and the aggregate is a good predictor of disaggregation performance in the examined data, as appliances with high mutual information, such as hair dryer and electric water heater, achieve lower estimation errors, while others, such as iron, are difficult to recover despite stable distributions. This relationship is statistically supported by a strong negative monotonic correlation between normalized mutual information and the disaggregation error (Spearman rs=-0.81, p=0.015). Hence, this work demonstrates how information-theoretic analysis can help characterize disaggregation difficulty prior to model training and assess the observability of appliances in high-frequency NILM.