Search Results (400)

As the penetration of intermittent renewable energy sources continues to increase, ensuring reliable power system and frequency stability is of importance. Battery energy storage systems (BESSs) have emerged as an important solution to mitigate these challenges by providing essential grid support services. In this context, a state-of-charge (SOC)-frequency control strategy for grid-forming BESSs is proposed to enhance their role in stabilizing grid frequency and improving overall system performance. In the system, the DC-link capacitor is regulated to maintain the angular frequency through a matching control scheme, emulating the characteristics of the rotor dynamics of a synchronous generator (SG). Thereby, the active power control is implemented in the control of the DC/DC converter to further regulate the grid frequency. More specifically, the relationship between the active power and the frequency is established through the SOC of the battery. In addition, owing to the inevitable presence of differential operators in the control loop, a high-gain observer (HGO) is employed, and the corresponding parameter design of the proposed method is elaborated. The proposed strategy simultaneously achieves frequency regulation and implicit energy management by autonomously balancing power output with available battery capacity, demonstrating a novel dual benefit for sustainable grid operation. To verify the effectiveness of the proposed control strategy, a 0.5-Hz frequency change and a 10% power change are carried out through simulations and also on a hardware-in-the-loop (HIL) platform. Full article

This paper presents an advanced energy management system (EMS) for optimizing power distribution in a battery/ultracapacitor (UC) hybrid energy storage system (HESS) for electric vehicles (EVs). The proposed EMS accounts for all energy flow scenarios within a practical driving cycle. A regenerative braking control strategy is developed to maximize kinetic energy recovery using an induction motor, efficiently distributing the recovered energy between the UC and battery. Additionally, a power flow management approach is introduced for both motoring (discharge) and braking (charge) operations via bidirectional buck–boost DC-DC converters. In discharge mode, an optimal distribution factor is dynamically adjusted to balance power delivery between the battery and UC, maximizing efficiency. During charging, a DC link voltage control mechanism prioritizes UC charging over the battery, reducing stress and enhancing energy recovery efficiency. The proposed EMS is validated through simulations and experiments, demonstrating significant improvements in vehicle acceleration, energy efficiency, and battery lifespan. Full article

Reliable wave prediction plays a significant role in wave energy converter (WEC) research, but there are still prediction errors that would increase the uncertainty for the power grid and reduce the power quality. The efficiency and stability of the power take-off (PTO) system are also important research topics in WEC applications. In order to solve the above-mentioned problems, this paper presents a model predictive control (MPC) method composed of a prediction error compensation controller and a PTO optimization controller. This work aims to address the limitations of existing wave prediction methods and improve the efficiency and stability of hydraulic PTO systems in WECs. By controlling the charging and discharging of the accumulator, the power quality is enhanced by reducing grid frequency fluctuations and voltage flicker through prediction error compensation. In addition, an efficient and stable hydraulic PTO system can be obtained by keeping the operation pressure of the hydraulic motor at the optimal range. Thus, smoother power output minimizes grid-balancing penalties and storage wear, and stable hydraulic pressure extends PTO component lifespan. Finally, comparative numerical simulation studies are provided to show the efficacy of the proposed method. The results validate that the dual-controller MPC framework reduces power deviations by 74.3% and increases average power generation by 31% compared to the traditional method. Full article

Lightning strikes pose a significant risk during outdoor activities. The connection between conventionally used rescue blankets in alpine emergencies and the risk of lightning injury is unclear. This experimental study investigated whether rescue blankets made of aluminum-coated polyethylene terephthalate increase the likelihood of lightning injuries. High-voltage experiments of up to 2.5 MV were conducted in a controlled laboratory setting, exposing manikins to realistic lightning discharges. In a balanced test environment, two conventionally used brands were investigated. Upward leaders frequently formed on the edges along the fold lines of the foils and were significantly longer in crumpled rescue blankets (p = 0.004). When a lightning strike occurred, the thin metallic layer evaporated at the contact point without igniting the blanket or damaging the underlying plastic film. The blankets diverted surface currents and prevented current flow to the manikins, indicating potentially protective effects. The findings of this experimental study suggest that upward leaders rise from the edge areas of rescue blankets, although there is no increased risk for a direct strike. Rescue blankets may even provide partial protection against exposure to electrical charges. Full article

Lead sulfide colloidal quantum dots (PbS QDs) have demonstrated great potential in short-wave infrared (SWIR) photodetectors due to their tunable bandgap, low cost, and broad spectral response. While significant progress has been made in surface ligand modification and defect state passivation, studies focusing on the interface between QDs and electrodes remain limited, which hinders further improvement in device performance. In this work, we propose an interface engineering strategy based on 3-aminopropyltriethoxysilane (APTES) to enhance the interfacial contact between PbS QD films and ITO interdigitated electrodes, thereby significantly boosting the overall performance of SWIR photodetectors. Experimental results demonstrate that the optimal 0.5 h APTES treatment duration significantly enhances responsivity by achieving balanced interface passivation and charge carrier transport. Moreover, The APTES-modified device exhibits a controllable dark current and faster photo-response under 1310 nm illumination. This interface engineering approach provides an effective pathway for the development of high-performance PbS QD-based SWIR photodetectors, with promising applications in infrared imaging, spectroscopy, and optical communication. Full article

Driven by the rapid advancement of Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) technologies, improving power quality and system stability during charging and discharging has become a research focus. To address this, this paper proposes a Model Predictive Control (MPC) strategy for Active Neutral-Point Voltage Balancing (ANPVB) in a single-phase three-level converter used in on-board V2G chargers. Traditional converters rely on passive balancing using redundant vectors, which cannot ensure neutral-point (NP) voltage stability under sudden load changes or frequent power fluctuations. To solve this issue, an auxiliary leg is introduced into the converter topology to actively regulate the NP voltage. The proposed method avoids complex algorithm design and weighting factor tuning, simplifying control implementation while improving voltage balancing and dynamic response. The results show that the proposed Model Predictive Current Control-based ANPVB (MPCC-ANPVB) and Model Predictive Direct Power Control-based ANPVB (MPDPC-ANPVB) strategies maintain the NP voltage within ±0.7 V, achieve accurate power tracking within 50 ms, and reduce the total harmonic distortion of current (THDi) to below 1.89%. The proposed strategies are tested in both V2G and G2V modes, confirming improved power quality, better voltage balance, and enhanced dynamic response. Full article

For the problem of consistency decline during the long-term use of battery packs for high-voltage and high-power energy storage systems, a dynamic timing adjustment balancing strategy is proposed based on the charge–discharge topology. Compared with the traditional balancing strategy, the dynamic timing adjustment balance strategy is more suitable for the transient high-frequency pulse and high-rate output of a high-power energy storage system. It gives full play to the pulse output adjustment function of the integrated charge–discharge topology. The advantages of this strategy include improving the balance between battery groups, the operating capacity of the system, and improving the continuous working ability of the system. Combined with the work condition of the high-power energy storage system, a balance control model is established, and a cycle charge–discharge test platform of battery packs is built. The effectiveness and advantages of the balance strategy of dynamic timing adjustment are verified by the experiment and simulations. The balancing time is less than 2 min, and the voltage difference is less than 6 mv. Full article

Uncoordinated electric vehicle (EV) charging can significantly complicate power system operations. In this paper, we develop a hybrid EV charging method that seamlessly integrates centralized EV charging and distributed control schemes to address EV energy demand challenges. The proposed method includes (1) a centralized day-ahead optimal scheduling mechanism and EV shifting process based on mixed-integer linear programming (MILP) and (2) a distributed control strategy based on a genetic algorithm (GA) that dynamically adjusts the charging rate in real-time grid scenarios. The MILP minimizes energy imbalance at overloaded slots by reallocating EVs based on supply–demand mismatch. By combining full and minimum charging strategies with MILP-based shifting, the method significantly reduces network stress due to EV charging. The centralized model schedules time slots using valley-filling and EV-specific constraints, and the local GA-based distributed control adjusts charging currents based on minimum energy, system availability, waiting time, and a priority index (PI). This PI enables user prioritization in both the EV shifting process and power allocation decisions. The method is validated using demand data on a radial feeder with residential and commercial load profiles. Simulation results demonstrate that the proposed hybrid EV charging framework significantly improves grid-level efficiency and user satisfaction. Compared to the baseline without EV integration, the average-to-peak demand ratio is improved from 61% to 74% at Station-A, from 64% to 80% at Station-B, and from 51% to 63% at Station-C, highlighting enhanced load balancing. The framework also ensures that all EVs receive energy above their minimum needs, achieving user satisfaction scores of 88.0% at Stations A and B and 81.6% at Station C. This study underscores the potential of hybrid charging schemes in optimizing energy utilization while maintaining system reliability and user convenience. Full article

This paper presents a hybrid fast charging strategy for static wireless power transfer (WPT) systems that synergistically combines pulsed current and multi-stage current (MCM) modes to enable rapid yet battery-health-conscious electric vehicle (EV) charging, thereby promoting sustainable transportation. The proposed approach employs a memetic algorithm (MA) to dynamically optimize the charging parameters, achieving an optimal balance between speed and battery longevity while maintaining 90.78% system efficiency at the SAE J2954-standard 85 kHz operating frequency. A neural-network-based state of charge (SOC) estimator provides accurate real-time monitoring, complemented by MA-tuned PI control for enhanced resonance stability and adaptive pulsed current–MCM profiles for the optimal energy transfer. Simulations and experimental validation demonstrate faster charging compared to that using the conventional constant current–constant voltage (CC-CV) methods while effectively preserving the battery’s state of health (SOH)—a critical advantage that reduces the environmental impact of frequent battery replacements and minimizes the carbon footprint associated with raw material extraction and battery manufacturing. By addressing both the technical challenges of high-power WPT systems and the ecological imperative of battery preservation, this research bridges the gap between fast charging requirements and sustainable EV adoption, offering a practical solution that aligns with global decarbonization goals through optimized resource utilization and an extended battery service life. Full article

In islanded microgrids with high-proportion renewable energy, the disconnection from the main grid leads to the characteristics of low inertia, weak damping, and high impedance ratio, which exacerbate the safety risks of frequency and voltage. To balance the requirements of system operation economy and frequency–voltage safety, a coordinated optimization scheduling method for frequency and voltage in islanded microgrids considering the active support of battery energy storage (BES) is proposed. First, to prevent the state of charge (SOC) of BES from exceeding the frequency regulation range due to rapid frequency adjustment, a BES frequency regulation strategy with an adaptive virtual droop control coefficient is adopted. The frequency regulation capability of BES is evaluated based on the capacity constraints of grid-connected converters, and a joint frequency and voltage regulation strategy for BES is proposed. Second, an average system frequency model and an alternating current power flow model for islanded microgrids are established. The influence of steady-state voltage fluctuations on active power frequency regulation is analyzed, and dynamic frequency safety constraints and node voltage safety constraints are constructed and incorporated into the optimization scheduling model. An optimization scheduling method for islanded microgrids that balances system operation costs and frequency–voltage safety is proposed. Finally, the IEEE 33-node system in islanded mode is used as a simulation case. Through comparative analysis of different optimization strategies, the effectiveness of the proposed method is verified. Full article

Effective energy management in microgrids is essential for integrating renewable energy sources and maintaining operational stability. Machine learning (ML) techniques offer significant potential for optimizing microgrid performance. This study provides a comprehensive comparative performance evaluation of four ML-based control strategies: deep Q-networks (DQNs), proximal policy optimization (PPO), Q-learning, and advantage actor–critic (A2C). These strategies were rigorously tested using simulation data from a representative islanded microgrid model, with metrics evaluated across diverse seasonal conditions (autumn, spring, summer, winter). Key performance indicators included overall episodic reward, unmet load, excess generation, energy storage system (ESS) state-of-charge (SoC) imbalance, ESS utilization, and computational runtime. Results from the simulation indicate that the DQN-based agent consistently achieved superior performance across all evaluated seasons, effectively balancing economic rewards, reliability, and battery health while maintaining competitive computational runtimes. Specifically, DQN delivered near-optimal rewards by significantly reducing unmet load, minimizing excess renewable energy curtailment, and virtually eliminating ESS SoC imbalance, thereby prolonging battery life. Although the tabular Q-learning method showed the lowest computational latency, it was constrained by limited adaptability in more complex scenarios. PPO and A2C, while offering robust performance, incurred higher computational costs without additional performance advantages over DQN. This evaluation clearly demonstrates the capability and adaptability of the DQN approach for intelligent and autonomous microgrid management, providing valuable insights into the relative advantages and limitations of various ML strategies in complex energy management scenarios. Full article

The decarbonization of residential cooling systems requires innovative solutions to overcome the mismatch between the renewable energy availability and demand. Integrating latent thermal energy storage (LTES) with heat pump/air conditioning (HP/AC) units can help balance energy use and enhance efficiency. However, the dynamic behavior of such integrated systems, particularly under low-load conditions, remains underexplored. This study investigates a 5 kW HP/AC unit coupled with an 18 kWh LTES system using a bio-based Phase Change Material (PCM) with a melting temperature of 9 °C. Two configurations were tested: charging the LTES using either a thermostatic bath or the HP/AC unit. Key parameters such as the stored energy, temperature distribution, and cooling capacity were analyzed. The results show that, under identical conditions (2 °C inlet temperature, 16 L/min flow rate), the energy stored using the HP/AC unit was only 6.3% lower than with the thermostatic bath. Nevertheless, significant cooling capacity fluctuations occurred with the HP/AC unit due to compressor modulation and anti-frost cycles. The compressor frequency varied from 75 Hz to 25 Hz, and inefficient on-off cycling appeared in the final phase, when the power demand dropped below 1 kW. These findings highlight the importance of integrated system design and control strategies. A co-optimized HP/AC–LTES setup is essential to avoid performance degradation and to fully exploit the benefits of thermal storage in residential cooling. Full article

This paper proposes a dual-mode CMOS analog front-end (AFE) circuit for short-wave infrared (SWIR) image sensors, which integrates a hybrid readout circuit (ROIC) and a 12-bit two-step single-slope analog-to-digital converter (TS-SS ADC). The ROIC dynamically switches between buffered-direct-injection (BDI) and direct-injection (DI) modes, thus balancing injection efficiency against power consumption. While the DI structure offers simplicity and low power, it suffers from unstable biasing and reduced injection efficiency under high background currents. Conversely, the BDI structure enhances injection efficiency and bias stability via an input buffer but incurs higher power consumption. To address this trade-off, a dual-mode injection architecture with mode-switching transistors is implemented. Mode selection is executed in-pixel via a low-leakage transmission gate and coordinated by the column timing controller, enabling low-current pixels to operate in low-noise BDI mode, whereas high-current pixels revert to the low-power DI mode. The TS-SS ADC employs a four-terminal comparator and dynamic reference voltage compensation to mitigate charge leakage and offset, which improves signal-to-noise ratio (SNR) and linearity. The prototype occupies 2.1 mm × 2.88 mm in a 0.18 µm CMOS process and serves a 64 × 64 array. The AFE achieves a dynamic range of 75.58 dB, noise of 249.42 μV, and 81.04 mW power consumption. Full article

Inconsistencies in lithium-ion battery packs pose significant challenges for both electric vehicles and energy storage systems, causing diminished energy utilization and accelerated battery aging. This study investigates the characteristics and aging processes of 32 batteries, creating simulation models for cells and packs based on experimental data. Through a controlled single-variable approach, the decoupled analysis of multi-parameter inconsistencies is carried out. Simulation results demonstrate that parallel-connected packs can maintain charge consistency without the need for external balancing systems, thanks to their self-balancing mechanisms. On the other hand, series-connected packs experience accelerated capacity degradation primarily due to charge inconsistencies linked to differences in Coulombic efficiency (CE) and the initial state of charge (SOC). For packs with minor capacity variations and temperature inconsistencies, a passive balancing current of 0.001 C can effectively eliminate up to 3.8% of capacity loss caused by charge inconsistencies within 15 cycles. Active balancing systems outperform passive ones primarily when there is significant capacity inconsistency. However, for packs that have undergone capacity screening before assembly, both active and passive balancing systems prove to be equally effective. Additionally, inconsistencies in internal resistance have a minimal impact on overall pack capacity but limit the power of both series-connected and parallel-connected packs. These findings offer essential insights for the development of balancing systems within battery management systems. Full article

Electrospinning has evolved into a vital nanofiber production technique with broad applications across biomedical, environmental, and industrial sectors. Alternating current (AC) and pulsed voltage (PV) electrospinning offer transformative alternatives by utilizing time-varying electric fields to overcome the drawbacks of DC electrospinning by employing an oscillating electric field that facilitates balanced charge dynamics, improved jet stability, and collectorless operation, leading to enhanced fiber alignment and significantly higher production rates, with reports exceeding 20 g/h. Conversely, PV electrospinning applies intermittent high-voltage pulses, offering precise control over jet initiation and termination. This method enables the fabrication of ultrafine, bead-free, and structurally uniform fibers, making it particularly suitable for biomedical applications such as controlled drug delivery and tissue scaffolds. Both techniques support tunable fiber morphology, reduced diameter variability, and improved structural uniformity, contributing to the advancement of high-performance nanofiber materials. This review examines the underlying electrohydrodynamic mechanisms, charge transport behavior, equipment configurations, and performance metrics associated with AC and PV electrospinning. It further highlights key innovations, current limitations in scalability and standardization, and prospective research directions. Full article