Search Results (1,796)

Wireless power transfer (WPT) systems for electric vehicles (EVs) are increasingly being studied in the MHz range to increase power density and reduce the size of passive components. However, operation at higher frequencies significantly changes electromagnetic interference (EMI) behaavior. Fast switching in SiC- and GaN-based inverters, high-Q resonant operation, and frequency-dependent parasitic capacitances create conductive, capacitive, and magnetic interference mechanisms that are less significant in conventional kHz-range systems. Although many existing studies focus on power-transfer efficiency and converter optimization, EMI mechanisms in MHz-range EV WPT systems remain insufficiently systematized from a system-level electromagnetic perspective. This paper presents a state-of-the-art review of EMI generation mechanisms and system-level effects in high-frequency WPT systems for electric vehicles. The review considers the main interference sources and coupling paths, including switching-induced common-mode currents, resonant amplification of current and voltage stress, capacitive coupling between the coupler and nearby conductive structures, and magnetic-field redistribution caused by coil misalignment. Special attention is given to the transition from lumped-element assumptions to more distributed electromagnetic behavior at higher frequencies. The review also discusses the possible impact of these mechanisms on vehicle electronic subsystems and highlights the need for frequency-aware electromagnetic design, integrated modeling, and more rigorous EMC assessment for reliable MHz-range wireless EV charging systems. Full article

The increasing penetration of wind generation into autonomous and weakly coupled industrial microgrids requires control strategies that can maintain power-supply reliability under stochastic generation and sharply variable loads. This paper proposes an adaptive corridor-based supervisory control algorithm for a lithium-ion battery energy storage system (BESS) integrated with a wind-turbine generator. The novelty of the method is not the general use of battery storage for power smoothing but a control law that maintains the generator within a predefined active-power corridor while transferring fast and medium-duration imbalances to the battery under state-of-charge, power-limit, and response-delay constraints. Unlike PI-based smoothing, model predictive control, or fixed rule-based switching, the proposed approach uses corridor retention as the primary operating criterion and relies only on directly measurable variables. The model was implemented in MATLAB/Simulink for a 2 MW wind-turbine generator coupled with a 444 kWh/1776 kW lithium-ion battery energy storage system. Field-measurement-based simulation validation was performed in MATLAB/Simulink using industrial load data measured at an autonomous oilfield power plant; the validation scenarios included extracted step disturbances, a real multi-peak load profile, prolonged deficit operation, and a scaled configuration scenario. The algorithm compensated for 86.3–87.4% of short-term load peaks, reduced the standard deviation of generator power from 467 to 98 kW, and decreased recovery time from 6.8 to 1.6 s. Full article

This paper presents a comparative analysis of Flywheel Energy Storage Systems (FESS) employing Permanent Magnet Synchronous Machines (PMSMs) and AC Brushless DC (AC-BLDC) machines for fast and efficient frequency regulation. The study examines their electromechanical behavior during the key operational stages of charging, standby, and discharging, with a focus on mitigating inrush current and enhancing overall system efficiency. MATLAB/Simulink models were developed to evaluate machine dynamics, electromagnetic behavior, and harmonic distortion during their operation. The results show that electromagnetic effects, particularly inrush current, commutation harmonics, and inverter limitations, significantly influence torque smoothness, efficiency, and overall system performance. PMSMs demonstrate superior torque quality, lower Total Harmonic Distortion (THD), and more stable energy conversion under Field-oriented Control (FOC), making it well suited for high-performance FESS applications. In contrast, the AC-BLDC machine exhibits higher torque ripple and elevated THD due to six-step commutation but offers a simpler drive topology and cost advantages. The findings offer practical insights for selecting machines and controllers in high-speed FESS designs and emphasize the importance of mitigating transient electromagnetic effects to enhance efficiency and reliability in modern grid support applications. Improved modeling incorporating magnetic saturation, frequency-dependent iron losses, and inverter constraints is essential for accurate performance prediction. Future work includes Hardware-In-the-Loop (HIL), Power-HIL validation, and DlgSILENT PowerFactory co-simulation to confirm dynamic performance under grid-connected operation. Full article

The baseline assessment analysis for total cost of ownership of the pilot demonstrations of the ESCALATE project was carried out for four different powertrain configurations, dealing with modular and scalable powertrains for various vehicle configurations in long-haul trucking. The baseline TCO methodology and results for battery electric trucks (BETs), fuel cell electric trucks (FCETs) and FC range-extending BETs are analysed based on the final designs of the demonstrator vehicles and their foreseen pilot use cases and operational scenarios. As real operation data is not yet available, the analysis relies on energy use and pilot mission analysis through simulation. Overall, the TCO analysis shows several key factors affecting the relative competitiveness of the different zero-emission powertrains and vehicles. Long-haul operations pose clear challenges to vehicle design and long-range vehicles on single charge or refill show increased curb weight, limiting allowable payload due to GVW limits. The best payload capacity is shown for opportunity charging BETs and FCETs. BETs are generally the closest competitor to conventional trucks, but a key factor is the relative energy price difference between diesel, electricity (private or public) and hydrogen. Energy sourcing will be an important factor for end users to enable competitive shift to zero-emission options. Access to cheap private electricity or local green hydrogen may facilitate a choice between the options. Full article

The electrification of urban freight is a central pathway for advancing China’s dual-carbon agenda, yet the spatial and temporal mismatch between charging supply and logistics demand remains a major bottleneck. Using Shanghai as a case study, this paper develops an integrated framework of hotspot identification, mechanism interpretation, and planning response for electric logistics vehicle (ELV) charging demand. Based on the operating records of more than 1200 pure electric logistics vehicles in Shanghai from 1 March to 30 November 2023, 85,367 valid charging events were extracted. ST-DBSCAN is used to detect charging demand hotspots, and a negative binomial model is employed to examine their determinants. The results show that charging demand is highly differentiated in space and time, following a pattern of daytime concentration in core logistics areas and nighttime dispersion toward peripheral parking and recharging spaces. Initial state of charge, daily mileage, logistics point of interest (POI) density, and road network density are all significantly associated with hotspot intensity, while the effects of time vary across daytime and nighttime charging contexts. The predominance of slow charging, together with a pronounced midday charging peak (12:00–17:00), points to a potential fast-charging pressure of fast-charging capacity in major logistics nodes. Based on these findings, the paper proposes targeted recommendations for hub-oriented fast-charging deployment, fleet–charging coordination, and data-driven governance. The study provides empirical evidence for improving the spatial planning and refined governance of urban freight energy infrastructure. Full article

The dual-active-bridge series resonant converter (DBSRC) is attractive for bidirectional DC conversion, but its output voltage may respond slowly and exhibit overshoot during start-up, load-step, and reference-step transients when conventional controllers are designed mainly from steady-state or small-signal models. This paper addresses the problem of improving the large-signal transient regulation of a DBSRC while avoiding undesired charging and discharging of the switching capacitor and output capacitor. A finite-state-machine-based state-trajectory control method is proposed. Thus, the converter consists of two full-bridge circuits, each with four switches. The proposed technique enhances the dynamic response of output voltage regulation. By examining the system dynamics in two state-plane domains, the switching behavior of the converter can be clearly characterized, enabling an accurate geometric representation of its operating mechanism. Consequently, a finite-state machine controller is designed based on state-trajectory planning. Switching conditions are utilized to achieve fast start-up and step-load transient responses. Finally, experiments are conducted to validate the effectiveness of the proposed control method. Full article

Real-time monitoring of lithium-ion capacitors (LICs) is crucial for ensuring reliability and predictive maintenance in dynamic applications such as electric transportation. However, traditional electrochemical impedance spectroscopy (EIS) techniques are complex and costly for onboard diagnostics due to their reliance on external excitation signals and dedicated hardware. Therefore, this paper presents an innovative framework for online state of health (SOH) estimation that bypasses these limitations by utilizing fast Fourier transform (FFT)-based passive impedance extraction directly from operational current and voltage signals. From experimental data, the equivalent circuit model (ECM) is developed, as well as its parameters, such as ohmic resistance, charge-transfer resistance, and Warburg diffusion. These parameters are identified through the extraction of impedance points in the low frequency region through FFT and the series resistance point using ohmic measurement, then performing a periodic curve fitting to these points. These curve fittings provide extracted ECM parameters. These parameters are used with a trained model to estimate the SOH of the monitored cell and are updated online. The proposed method was experimentally validated on five LIC cells aged under various C-rates (1C, 4C, 7C) and temperatures (35 °C, 40 °C, 50 °C), showing consistent impedance evolution with capacity fade. Validation of the utilized machine learning models, such as Polynomial Regression (PR), principal components analysis (PCA), and random forest (RF) regression, achieved SOH prediction errors as low as 2.23% compared to experimental results. The developed framework is particularly suitable for applications such as flash-charged electric buses but is broadly applicable across other energy storage systems as well. This advanced method enables real-time diagnostics without hardware modification, offering significant potential for integration into existing battery management systems (BMSs). Full article

Purpose: To develop an accelerator neutron source suitable for boron neutron capture therapy—a new promising method for treating malignant tumors—and to develop dosimetry tools and methods. Methods: Research into the transport and acceleration of a beam of charged particles, development and manufacture of an accelerator neutron source, study of the radiation generated, and development and implementation of dosimetry tools and methods. Results: A facility called VITA has been created, which includes a tandem electrostatic accelerator of an original design for producing a 2.3 MeV 10 mA proton beam, a lithium target for generating neutrons in the ⁷Li(p,n)⁷Be reaction, and a beam shaping assembly for forming a therapeutic neutron beam. The facility at the institute is used for scientific research, the facility in Xiamen (China) is used for clinical trials, and the facility in Moscow (Russia) will soon be used for clinical trials. Also, new tools and methods for measuring the boron dose, γ-ray dose, and sum of the fast neutron dose and the nitrogen dose have been proposed and implemented. The conducted studies demonstrated the high efficiency of the VITA^® facility, the first possibility of implementing prompt γ-ray spectroscopy for boron imaging, and the first possibility of implementing lithium neutron capture therapy, which has advantages over BNCT, and also presented the results of the development of new tools and methods for measuring the boron dose, γ-ray dose, and the sum of the fast neutron dose and the nitrogen dose. Conclusions: The authors strongly recommend using prompt γ-ray spectroscopy in treatment and developing lithium neutron capture therapy, including in combination with BNCT, and note the high efficiency, reliability and compactness of the VITA^® facility. Full article

Fast growth in electric vehicle (EV) charging stations is changing the way regional distribution networks are loaded. The difficulty is not only the size of the added demand, but also the fact that charging appears at different places, at different times, and under different voltage constraints. This paper considers the common planning situation in which station-level charging records are incomplete and only transformer-side aggregate measurements are available. A data-driven hosting capacity (HC) assessment method is developed for this setting. The method first constructs zone-specific daily load profiles and then separates EV charging components from mixed transformer curves through an improved ISODATA clustering method and an improved genetic algorithm (IGA). For planned electric vehicle charging stations (EVCSs) without historical measurements, Ordinary Kriging (OK) is used to infer charging profiles from nearby observed stations in the same functional zone. The calculated HC is then checked successively at the 10 kV, 35 kV, and 110 kV levels. When an upstream constraint is violated, an improved Entropy-weight TOPSIS (EW-TOPSIS) model reallocates the available capacity according to both network constraints and zone priority. The case study indicates that the method can identify upstream bottlenecks that are hidden in local assessments, preserve residential charging demand, and provide zone-specific guidance for EVCS expansion. Full article

The increasing global demand for high-performance, reliable, and sustainable energy storage systems has accelerated the development of supercapacitors as technologies capable of bridging the performance gap between conventional capacitors and batteries. Supercapacitors combine rapid charge–discharge capability, high power density, and exceptional cycle life through charge storage mechanisms based on ion adsorption and fast surface redox reactions at the electrode–electrolyte interface. This review examines the fundamental operating principles, charge storage mechanisms, electrode materials, mechanical and functional properties, fabrication methods, and engineering applications of modern supercapacitors. Carbon-based materials, metal oxides, conducting polymers, MXenes, sulfides, nitrides, borides, and emerging hybrid systems are critically compared in terms of capacitance, energy density, cycling stability, and mechanical robustness. Additionally, recent advances in scalable manufacturing approaches, including thin-film deposition and printing technologies, are discussed alongside key challenges such as limited energy density, interfacial instability, mechanical degradation, electrolyte compatibility, and large-scale processing. By consolidating recent developments across materials science, electrochemistry, and device engineering, this review provides insight into future directions for next-generation high-performance supercapacitor technologies. Full article

Effective thermal management is crucial for the performance, thermal safety, and lifespan of lithium-ion batteries in electric vehicles (EVs). Thermal management strategies are essential for preventing overheating, thermal imbalance, and the associated risk of thermal runaway. Nanofluids are emerging and attracting considerable attention as potential coolants for high-power energy storage and electronics systems. This review updates and summarizes the most recent advances in nanofluid-based cooling strategies for battery thermal management systems (BTMSs) over the past five years, emphasizing their implications for battery thermal safety. Three main nanofluid-based cooling strategies have been evaluated in depth, including nanofluid-based indirect liquid cooling, nanoparticle-enhanced PCM cooling, and nanofluid-based heat pipe cooling. Various nanofluid formulations, including mono, hybrid, and ternary nanofluids, have been considered and evaluated for their heat dissipation under high charge/discharge and abuse-relevant conditions. Thermal and hydraulic performance characteristics, including maximum temperature, maximum temperature difference, and pressure drop, have been comprehensively evaluated for different nanofluid-based cooling strategies. The findings demonstrated that nanofluids significantly improved heat transfer rates and enhanced temperature control efficiency. In particular, hybrid and ternary nanofluids exhibit superior thermal performance and effectively suppress the escalation of safety-critical temperatures. Beyond summarizing cooling performance, this review further discusses the role of nanofluid-based cooling strategies as functional thermal-control layers within intelligent BTMS architectures. Particular attention is given to their compatibility with sensing networks, BMS-/VCU-level supervisory control, predictive thermal models, actuator responsiveness, fault-warning algorithms, and long-term reliability under realistic driving and fast charging conditions. Therefore, this review provides architecture-oriented insights for developing safe, energy-efficient, and control-ready BTMSs for next-generation high-power and connected EVs. Full article

This paper presents a passive residue-coupled discrete-time delta–sigma ($\Delta \Sigma$) modulator for low-power narrowband sensing applications. Instead of adding a fourth active integrator, the proposed architecture keeps a third-order switched-capacitor main loop and reuses the intrinsic top-plate residue of an 8-bit asynchronous successive-approximation-register (SAR) quantizer. The retained capacitive digital-to-analog converter (CDAC) residue is passively reinjected through a charge-redistribution path, introducing an additional high-pass error-propagation factor in the effective noise transfer function (NTF). Under a bounded effective coupling coefficient, the proposed loop approaches fourth-order-like in-band noise suppression while retaining third-order active-loop complexity. Behavioral simulations show that the Enhanced mode improves the peak signal-to-noise-and-distortion ratio (SNDR) by 16.9 dB over the Baseline third-order mode at an oversampling ratio (OSR) of 128. Circuit-level corner verification of the standalone SAR confirms correct bit cycling and a settled residue-retention window under typical–typical (TT), slow–slow (SS), and fast–fast (FF) conditions: with the slowest conversion window of about 21.4 ns at the SS corner and a sampling period of 39.06 ns at $f_s=25.6$ MHz, roughly 17.66 ns of timing margin remains for residue holding, passive reinjection, and clock non-overlap. The proposed method provides an architecture-level route for improving in-band noise shaping without increasing the number of active integrator stages, and is particularly attractive for low-power, narrowband, and sensor-oriented analog-to-digital converter (ADC) applications. Full article

Metal–organic frameworks (MOFs) exhibit high specific surface area and porosity, which may facilitate electron transfer during electrochemical reactions. Therefore, it is clear that MOFs are promising materials for the development of electrochemical sensors. In particular, zinc (Zn) based MOFs offer several advantages such as high specific surface area, porosity, environmental friendliness and low cost. Thus, Zn-based MOF materials and their composites have been extensively utilized in the detection of various pollutants, biomolecules and food additives. The Zn-MOF-based materials have been extensively utilized in electrochemical and fluorescence sensing applications. Previously, various Zn-MOF-based sensing systems such as pristine Zn-MOF, carbon-supported Zn-MOF composites, MXene hybrids with Zn-MOF, and bimetallic/trimetallic Zn-based MOFs were explored to enhance sensing performance. Such materials exhibit remarkable analytical performance, such as a low limit of detection (LOD) (nM to pM range), wide linear response range (LR), fast response times, and high selectivity in the presence of interfering species. In electrochemical sensing, Zn-MOF-modified electrodes demonstrated improved charge-transfer kinetics and sensitivity, enabling accurate determination of the biomolecules, drugs and heavy metal ions in real samples. Similarly, Zn-MOF-based fluorescence sensors showed high luminescent properties and displayed sensitive detection of pollutants and biomolecules. Despite such promising sensing performances, some challenges, such as low stability, reproducibility and selectivity in real-time monitoring, etc., remain that need to be overcome. This review article summarizes the previously reported literature on the fabrication of Zn-MOFs, their composites and Zn-MOF-derived materials for the development of electrochemical and fluorescence sensors. We have also discussed the future directions for the rational design of the high-performance Zn-MOF-based sensing systems for environmental and biomedical applications. We believe that the present review article would be useful for the scientific community working on the fabrication of Zn-MOF-based sensors. Full article

Real-time energy management for large electric vehicle (EV) clusters requires both fast aggregate flexibility estimation and executable per-vehicle dispatch. Classical LP/MILP/MPC formulations provide strong feasibility and optimality guarantees when the model is fully specified, but their online solve time increases rapidly with cluster size; learning-based methods are fast but often rely on soft constraint penalties or external feasibility repair. We propose the Physics-Constrained Mamba-3 MIMO Aggregator (PC-M3), an amortized, constraint-aware sequence model that integrates a MIMO Mamba backbone, a history-dependent differentiable projection, a sparse routing layer, and an aggregation–disaggregation consistency loop, scaling AI-EMS from a single battery to ten-thousand-vehicle clusters in one forward pass. PC-M3 assigns every EV to one channel of a multi-input multi-output (MIMO) state-space recurrence and embeds the per-vehicle state-of-charge, power and energy constraints as a differentiable in-loop projection, jointly producing the cluster-level flexibility envelope and the per-vehicle charging trajectory. A sparse Routing-Mamba mixture-of-experts layer adaptively allocates capacity to behaviourally distinct sub-populations without supervised labels, and a consistency-trained aggregation–disaggregation loop binds the predicted envelope to the executed dispatch, forming a digital-twin-style predictive EMS pipeline that couples cluster dispatch with per-vehicle SoC evolution. On a single NVIDIA A100, PC-M3 sustains 0.34 s inference for 10,000 EVs over a 24-h horizon, about 18× faster than an Informer baseline and 2.4× faster than PowerMamba. Evaluated on the open ACN-Data and ElaadNL workplace and public charging corpora and on a 10,000-vehicle NREL dsgrid-TEMPO 2030 stress test, PC-M3 reduces the normalised envelope Hausdorff distance from 9.7% (PowerMamba) to 3.4%, cuts closed-loop cluster tracking RMSE from 1.45 MW (model predictive control) to 0.82 MW, and maintains zero observed feasibility violations with respect to the specified or imputed per-vehicle polytopes on every evaluated session. The framework provides a scalable, predictive, constraint-aware AI-EMS for V2G/G2V virtual-power-plant operation of large EV fleets. Full article

Solid-state batteries (SSBs) are emerging as a transformative alternative to conventional lithium-ion batteries (LIBs) for next-generation electric vehicles (EVs) by replacing flammable liquid electrolytes with solid-state materials. Compared with current LIB systems delivering approximately 160–300 Wh/kg at the pack level, SSBs are projected to achieve 400–800 Wh/kg, enabling improvements in driving range of nearly 50–100% while simultaneously reducing battery pack mass by 10–30%. These improvements directly enhance vehicle-level energy efficiency by lowering energy consumption from typical values of 150–180 Wh/km in present EVs to projected levels of 110–140 Wh/km in optimized SSB-based architectures. Furthermore, reduced internal resistance and improved electrochemical stability can increase round-trip efficiency from approximately 85–95% in conventional LIBs to values approaching 95–98% under optimized solid-state configurations. The enhanced thermal stability of solid electrolytes significantly reduces the need for active cooling systems, decreasing parasitic thermal-management energy consumption from 10–30% of total vehicle energy demand to below 5–15% in advanced SSB systems. Fast-charging capability is also substantially improved, with projected charging times decreasing from 20–40 min to approximately 10–15 min for 10–80% state-of-charge operation, while maintaining improved safety and reduced risk of thermal runaway. In addition, SSBs demonstrate projected cycle lifetimes exceeding 3000–5000 cycles, compared with 1000–2000 cycles for conventional LIBs, thereby lowering battery replacement frequency and lifecycle energy losses. This paper examines the electrochemical fundamentals, thermal behavior, charging/discharging efficiency, and vehicle-level implications of SSB technology for EV applications. Comparative analyses demonstrate that replacing LIBs with SSBs can increase EV driving range from approximately 400 km to 700–800+ km under equivalent battery mass conditions, while also improving coulombic efficiency beyond 99.5% and reducing self-discharge rates to below 1–2% per month. Current industrial case studies from Toyota, Factorial Energy, Mercedes-Benz, CATL, BYD, QuantumScape, and Samsung SDI further confirm accelerating commercialization pathways toward 2027–2030. Overall, the study demonstrates that SSBs are not merely incremental battery improvements but represent a system-level efficiency technology capable of simultaneously enhancing energy density, reducing thermal and electrical losses, extending vehicle range, accelerating charging, and improving long-term sustainability. Despite persistent challenges related to manufacturing scalability, interfacial resistance, and cost, SSBs are positioned to become a critical enabler of highly efficient, long-range, and safer electric mobility systems beyond 2030. Full article