Search Results (1,740)

Extreme fast charging (XFC) infrastructure is becoming increasingly necessary as the number of electric vehicles continues to grow. However, deploying such stations introduces several challenges related to power quality and compliance with regulatory standards. This work presents an alternative XFC station designed for charging multiple vehicles while ensuring low harmonic distortion in the grid currents, without the need for sinusoidal filters, by employing the Zero Harmonic Distortion (ZHD) converter. The proposed system offers galvanic isolation for each charging interface and supports additional functionalities, including the integration of Distributed Energy Resources (DERs) and the provision of ancillary services. These features are enabled through the combination of a bidirectional grid-connected active front-end operating at low switching frequency with high-frequency silicon carbide (SiC)-based dc/dc converters on the vehicle side. Hardware-in-the-loop (HIL) simulation results demonstrate a total demand distortion (TDD) of 1.12% for charging scenarios involving both 400 V and 800 V battery systems, remaining within the limits specified by IEEE 519-2022. Full article

Fast charging of electric vehicles can introduce phase-dependent harmonic distortion and voltage unbalance in low-voltage feeders, which may reduce admissible charging capacity even when voltage magnitudes remain within conventional limits. This paper proposes a phase-aware predictive scheduling framework for harmonic hosting management in feeders with a high penetration of electric vehicle charging. The proposed method formulates feeder operation as a predictive decision problem that jointly determines charging power levels, phase allocation, and the selective activation of multifunctional compensation resources under harmonic distortion, voltage unbalance, and neutral-current constraints. Unlike previous studies centered on harmonic characterization, static hosting assessment, or local converter-level mitigation, the proposed approach treats harmonic hosting as an active feeder-level network management problem. The framework is evaluated through time-series harmonic power-flow simulations using charger harmonic emission profiles and realistic feeder parameters. The numerical results indicate that coordinated phase-aware scheduling can increase admissible charging capacity, improve compliance margins for power-quality indices, and reduce mitigation efforts with respect to uncontrolled charging and non-coordinated compensation strategies. Overall, the results support the use of phase-aware scheduling as a feeder-level strategy to improve electric vehicle charging integration under harmonic and unbalanced constraints. Full article

This research presents a comprehensive framework for optimizing Electric Vehicle (EV) charging infrastructure along the Lake Michigan circuit (LMC) in Michigan to support ecotourism, considering both slow charging at destinations and fast charging along the corridor. The framework identifies the optimum location and number of Level 2 chargers and Direct Current Fast Chargers (DCFC), using heuristic algorithms. The study evaluates infrastructure planning based on four key objectives: (1) minimizing overall charging infrastructure costs, (2) reducing grid network upgrade costs, (3) providing an acceptable level of service to long-distance travelers using DCFCs by minimizing queuing delays and deviations from their intended routes, and (4) minimizing unserved charging demand at Level 2 chargers, which reduces redirection to DCFC and consequently mitigates battery degradation. The integration of Level 2 and DCFC networks facilitates strategic investment by effectively managing charging demand, allowing unserved Level 2 demand to be accommodated at DCFC stations while adhering to budgetary constraints. The results show that increasing the budget from $15 to $20 million reduces user inconvenience by 47%, while a further increase to $25 million yields an additional 18% reduction. Additionally, increasing users’ value of time from $13 to $36 per hour results in a 50% reduction in average queuing time. Full article

Accurate assessment of the State of Health (SOH) is critical for battery management systems in aviation. As a step towards this goal, this study presents a proof-of-concept for a novel SOH estimation method based on an Improved Particle Swarm Optimization-Extreme Learning Machine (IPSO-ELM) model, validated under controlled laboratory cycling conditions. Although traditional Extreme Learning Machines (ELM) are widely used due to their fast computation and good generalization, their random parameter initialization often leads to unstable convergence and limited accuracy. To address these limitations, this paper proposes a novel SOH estimation method based on an Improved Particle Swarm Optimization (IPSO) algorithm to optimize the key parameters of ELM. Three health indicators (HI)—constant-current charging time, equal-voltage-drop discharge time, and average discharge voltage—were extracted from charge–discharge curves as model inputs. The IPSO algorithm dynamically adjusts the inertia weight, introduces a constriction factor and a termination counter to enhance global search capability and avoid local optima. Experimental results on open-source datasets (B005, B007, B0018) and laboratory datasets (A001, A002) demonstrate that the proposed IPSO-ELM model achieves a Root-Mean-Square Error (RMSE) below 0.7% and a Mean Absolute Percentage Error (MAPE) below 0.5%. Compared with standard ELM and PSO-ELM models, it significantly outperforms them in accuracy (e.g., for B0018, RMSE is reduced to 0.21% and MAPE to 0.14%), convergence speed, and robustness, establishing a foundation for future development of aviation-ready SOH estimators. Full article

Amid the global decarbonization of urban transportation, the large-scale deployment of electric buses faces major challenges, including concentrated charging demand, increased peak electricity demand, and inefficient energy utilization at transit depots. Existing studies usually optimize depot energy system configuration and bus scheduling separately, which often leads to biased system-level decisions. To address this limitation, this study proposes a collaborative optimization framework that integrates cross-line scheduling with the configuration of photovoltaic–storage–charging systems at depots to improve overall resource utilization. Specifically, this study formulates a mixed-integer linear programming (MILP) model to minimize the total daily system cost. The proposed model comprehensively captures multiple factors, including the costs of bus investment, charging infrastructure, photovoltaic deployment, energy storage deployment, and carbon emissions. In this study, Benders decomposition is used as a solution framework to handle the coupling structure of the model. Case studies show that, compared with conventional operation modes, the combination of cross-line scheduling and fast charging technology produces a significant synergistic effect. This combination reduces the required fleet size from 17 to 14 buses and substantially lowers investment in depot infrastructure, thereby minimizing the total system cost. Sensitivity analysis further shows that the deployment scale of photovoltaic systems has a clear threshold effect on electricity costs, whereas the core economic value of energy storage systems depends on peak shaving and arbitrage under time-of-use electricity pricing. Overall, this study demonstrates the critical role of integrated planning in improving the economic efficiency and operational feasibility of electric bus systems. It provides important theoretical support and practical guidance for depot design and resource scheduling in low-carbon public transportation networks. Full article

An important challenge for materials researchers in the modern era is the fabrication of high-performance electrodes with novel designs and structures to enhance electrochemical sensing and energy storage performance. Recently, perovskite-structured bimetallic hydroxide materials, owing to their high conductivity, decent surface area, abundant redox activity, and good stability, have emerged as promising candidates for bifunctional electrochemical applications. In this study, we designed perovskite-type CuSn(OH)₆ microspheres via a facile coprecipitation method for nifedipine (NFD) sensing and supercapacitors (SCs). Various characterization techniques were employed to confirm the successful synthesis of CuSn(OH)₆. The uniform formation and distribution of CuSn(OH)₆ within the sphere structure provide rich reactive sites and enhance structural stability, thereby improving electrochemical activity. This architecture also induces a synergistic effect between Cu and Sn, which increases conductivity and accelerates redox kinetics. Consequently, the electrode modified with CuSn(OH)₆/GCE exhibited a wide linear concentration range of 0.4–303.3 µM and a low detection limit of 0.44 µM for NFD detection. This sensor further demonstrated superior analytical reliability, with selectivity of <5%, cycling stability of 84.79%, reproducibility of 3.3%, and recovery rates of 99.2–99.8% in the serum sample. Concurrently, the CuSn(OH)₆/NF showcased a high specific capacitance of 514 F g⁻¹ at 1 A g⁻¹, good longevity of 83.05% retention after 5000 cycles, and low charge transfer resistance of 6.56 Ω and solution resistance of 1.04 Ω, validating fast ion–electron transport. These results underscore that perovskite-based CuSn(OH)₆ is an efficient dual-functional electrocatalyst for sensitive electrochemical detection and high-performance SCs. Full article

A novel modular multilevel converter (MMC)-based spark-gap trigger generator for high-voltage pulsed-power applications has been developed and presented in this work. It fully exploits the inherent modularity of MMC topology to generate high-voltage trigger pulses in a flexible and scalable manner. A prototype based on insulated gate bipolar transistors (IGBTs) was constructed to effectively trigger the breakdown of the spark gaps of a Marx Bank consisting of four capacitors charged to 50 kV. It is characterized by a fast rise time and produces pulses of 15 kV with a duration of ~200 ns. Using semiconductors and foil capacitors, the new trigger generator successfully replaces the thyratron-based generator. Full article

The development of lithium-ion batteries (LIBs) capable of extreme fast charging (XFC) while preserving safety, durability, and practical energy density remains a central challenge for next-generation electric transportation and grid-scale storage. Conventional graphite anodes are fundamentally limited at high current densities by sluggish intercalation kinetics, which cause lithium plating, motivating the exploration of alternative insertion materials. This review provides a comprehensive and internally consistent assessment of titanium-based oxide anodes, encompassing TiO₂ polymorphs, lithium titanate (Li₄Ti₅O₁₂), and Wadsley–Roth titanium niobium oxides, through the combined lenses of crystal topology, diffusion pathways, redox chemistry, interfacial behavior, and resource scalability. By systematically comparing structural frameworks and electrochemical mechanisms across these material classes, we demonstrate that fast-charging performance is governed not by nano-structuring alone, but by the intrinsic coupling between operating potential, framework rigidity, and multi-electron redox activity. While Li₄Ti₅O₁₂ establishes the benchmark for safety and cyclability, and TiO₂ polymorphs provide structural versatility, titanium niobium oxides uniquely reconcile high theoretical capacity with minimal lithiation strain and open diffusion channels, positioning them as highly promising candidates for sub-10 min charging without catastrophic degradation. This review highlights the persistent obstacles these materials suffer, such as limited round-trip energy efficiency (RTE), interfacial gas evolution, poor dopant stability, and unsustainable extraction, while simultaneously exploring targeted design strategies to overcome them. Finally, this review provides a materials design and comparison framework for the development of safe, high-power, and commercially viable ultrafast-charging LIBs. Full article

The demand for high-energy-density and fast-charging solid-state lithium metal batteries (SSLMBs) often subjects practical devices to internal thermal loads, making high-temperature operation a common operational condition rather than an isolated scenario. To address the interfacial degradation and dendrite growth accelerated by such thermomechanical stresses, we developed a composite gel electrolyte (CGE) by incorporating an optimal concentration of active Li_6.4La₃Zr_1.4Ta_0.6O₁₂ (LLZTO) into a fluoropolymer network. The abundant Lewis acidic sites on the LLZTO surfaces promote competitive solvation decoupling by interacting with anions, thereby modulating the primary solvation sheath of Li⁺. This localized modulation lowers the lithium-ion migration activation energy to 0.248 eV and facilitates a dual-interfacial passivation mechanism. Specifically, a rigid, inorganic-rich solid electrolyte interphase (SEI) forms to suppress morphological instability at the lithium anode, while an organic-dominated cathode electrolyte interphase (CEI) enhances the oxidative stability up to 4.3 V. As a result, symmetric cells demonstrate stable electrodeposition for over 450 h at 80 °C and 0.5 mA cm⁻². Furthermore, NCM811/Li full cells utilizing this CGEs exhibit significantly improved thermal resilience and cycling stability. Full article

The integration of photovoltaic (PV) generation with bidirectional electric vehicle (EV) fast-charging systems offers a promising pathway toward sustainable transportation and grid support. However, the dynamic coupling between maximum power point tracking (MPPT) perturbations and grid-side power quality presents a fundamental challenge in such multi-converter architectures. This paper addresses this challenge through a coordinated design and optimization framework for a grid-connected, PV-assisted bidirectional off-board EV fast charger. The system integrates a 184.695 kW PV array via a DC-DC boost converter, a common DC link, a three-phase bidirectional active front-end rectifier with an LCL filter, and a four-phase interleaved bidirectional DC-DC converter for the EV battery interface. A comparative evaluation of three MPPT algorithms establishes the Fuzzy Logic Variable Step-Size Perturb & Observe (Fuzzy VSS-P&O) as the optimal strategy, achieving 99.7% tracking efficiency with 46 μs settling time. However, initial integration of this high-performance MPPT reveals system-level harmonic distortion, with grid current total harmonic distortion (THD) reaching 4.02% during charging. To resolve this coupling, an Artificial Bee Colony (ABC) metaheuristic algorithm performs coordinated optimization of all critical PI controller gains. The optimized system reduces grid current THD to 1.40% during charging, improves DC-link transient response by 43%, and enhances Phase-Locked Loop (PLL) synchronization accuracy. Comprehensive validation confirms robust bidirectional operation with seamless mode transitions and compliant power quality. The results demonstrate that system-wide intelligent optimization is essential for reconciling advanced energy harvesting with stringent grid requirements in next-generation EV fast-charging infrastructure. Full article

This study presents an integrated simulation framework that combines agent-based transport modeling with probabilistic load-flow analysis to quantify power system loading of long-haul heavy-duty electrification. The approach is applied to a case study considering fully electrified road freight in the Skåne region in Sweden, using high-resolution transport demand data and the actual power grid model used by the grid owner in the study area. The synthetic freight population covers the full long-haul truck segment intersecting Skåne. Both public en-route fast charging and end-of-trip depot charging are considered. The analysis reveals two fundamentally different charging demand profiles: a heavily fluctuating profile for public en-route charging, accounting on average for 82% of the total daily charging energy, and a stable profile for end-of-trip depot charging, covering on average the remaining 18%. The latter is achieved through a Linear Programming (LP) optimization model that flattens the load by scheduling charging across depot stay windows. These profiles serve as inputs to a probabilistic load-flow simulation that computes loading distributions for substation transformers. The simulation results show that in 4 of the 43 primary substations studied, the maximum transformer loading exceeds 100% following the introduction of truck charging, with peak loading at the most affected substation rising from 99% to 159%. This stress is primarily caused by the public charging demand, which peaks from late morning to noon, aligning with the early stages of logistics operations. However, there is no clear correlation between the magnitude of the truck charging load and the impact on transformer loading, since this is also highly dependent on local grid conditions. These findings highlight the value of integrated transport-energy simulations for planning resilient infrastructure and guiding targeted grid reinforcements. Full article

Metal–organic frameworks (MOFs) are unique microporous materials being explored for a wide range of applications. Their porosity and high surface areas can readily be exploited for guest–host interactions, separations, and photochemical catalysis, but many suffer from poor charge separation and fast electron–hole recombination. Introducing structural defects, such as missing linkers or metal nodes, can create unsaturated metal sites and alter band structure, conductivity, and light absorption, improving photocatalytic performance. UiO-66-NH₂ and MIL-125-NH₂ are water-stable, visible-light-absorbing MOFs well suited for photocatalytic degradation of organic dyes. In this work, the influence of defect engineering on photocatalytic properties of MOFs was investigated using formic and acetic acid modulators with UiO-66-NH₂ and variable temperature with MIL-125-NH₂ during synthesis. The resulting materials were characterized by XRD, FTIR and SEM/EDS. Defect states were tracked using N₂ adsorption/BET analysis and UV–Vis spectroscopy. Photocatalytic activity was evaluated by monitoring Rhodamine B (RhB) degradation in aqueous solution under simulated solar irradiation. It was found that increased temperature beyond 120 °C during synthesis promotes mesopore formation and decreases the bandgap in MIL-125-NH₂, resulting in a more photoactive material. Defective MIL-125-NH₂ synthesized at 150 °C showed the most defects and proved to be the best photocatalyst investigated in this study. Formic acid modulation in UiO-66-NH₂ generated smaller crystallites that slightly increased the bandgap; however, the surface area decreased proportionally with the amount of formic acid used. The decreased surface area and observed enhancement in photocatalytic degradation of RhB suggest that formic acid introduces defects into the UiO-66-NH₂ framework that enhance photocatalytic properties. UiO-66-NH₂ treated with acetic acid resulted in larger crystals, increased bandgaps, and increased surface areas, suggesting that acetic acid simply modulates growth rather than imparting defects to the framework. Full article

In recent years, DC-DC partial power converters (PPCs) have become increasingly important in fast-charging architectures for electric vehicles (EVs). Their key feature is that only a fraction of the energy delivered to the battery is processed by the PPC, while the rest is transferred directly, bypassing the conversion stage. This reduces DC-DC conversion losses and improves overall charging efficiency. However, the nonlinear dynamics of these converters can limit performance, especially with model-based controllers such as proportional–integral (PI) controllers. This paper proposes a data-driven control framework for EV fast-charging stations using a DC-DC PPC that is controlled by deep reinforcement learning (DRL). A value-based deep Q-network (DQN) directly selects switching actions and jointly regulates the partial-voltage and output current. The control problem is formulated as a discrete-time Markov decision process, and a two-stage transfer learning scheme ensures safe, efficient deployment. Firstly, the DQN agent is trained in a high-fidelity simulation and then fine-tuned with a small set of experimental data to capture parasitic and modeling errors. The controller is integrated into a constant-current–constant-voltage (CC-CV) charging algorithm and validated over a full charging cycle of a 60 kWh EV battery. The proposed control scheme exhibits a settling time of approximately 2 ms in response to current reference variations while maintaining steady-state errors below 2% in current regulation and below 1% in partial voltage regulation. Simulation results show that the proposed DRL controller has a small steady-state tracking error and improved robustness to reference changes compared with conventional PI and sliding mode controllers. The low computational cost of the trained DQN policy also enables real-time execution on embedded platforms for EV charging. Full article

A correct description of quarkonia production and kinematics is still one of the most challenging assignments for Quantum Chromodynamics. This document presents a study of the ${\rm Y}$(1S), (2S) and (3S) mean transverse momentum ($⟨p_{\mathrm{T}}^{{\rm Y}}⟩$) as a function of the charged particle multiplicity ($N_{\mathrm{Track}}$) in proton–proton collisions at $\sqrt{s}$ = 7 TeV generated with Pythia 8.312 CUETP8M1 tune. The comparison to real data collected by the CMS experiment indicates that the agreement is much better for the excited states than for the ground state. The observed fast increase in the $⟨p_{\mathrm{T}}^{{\rm Y}}⟩$ at small values of $N_{\mathrm{Track}}$ is mainly due to the contribution from the away region. Furthermore, when computing the $⟨p_{\mathrm{T}}^{{\rm Y}}⟩$ from jetty and isotropic events, a clear $p_{\mathrm{T}}$ hardening is observed in jetty events. Finally, analyzing the fragmentation of jets containing an ${\rm Y}$(nS), a new method is proposed to test the new quarkonia shower present in the Monte Carlo event generator. Full article

Lithium-ion batteries are widely used across diverse applications due to their high energy density, long cycle life, and fast charging capabilities. As battery-powered systems become increasingly critical, accurate estimation of the Remaining Useful Life (RUL) is essential for ensuring reliability, safety, and effective maintenance planning. This work investigates Evidential Deep Learning (EDL) for data-driven RUL estimation and introduces a novel risk-aware loss function designed to enhance both predictive accuracy and uncertainty quantification in the End-of-Life (EoL) region, where precise and trustworthy predictions are most needed. Using a publicly available dataset of lithium iron phosphate (LFP) cells, we benchmark the proposed approach against a baseline Conv–LSTM model, Monte Carlo (MC) Dropout, and Deep Ensembles. The results show that integrating the risk-aware loss into the EDL framework substantially improves the calibration of predictive uncertainty while achieving state-of-the-art accuracy near EoL. Unlike MC Dropout and Deep Ensembles, which exhibit increasing or unstable uncertainty as degradation accelerates, the proposed EDL model demonstrates a consistent reduction in uncertainty and significantly higher reliability in late-stage predictions. The findings indicate that the risk-aware evidential framework offers a reliable and computationally efficient solution for battery RUL estimation, enabling more informed decision-making in both safety-critical and consumer-oriented applications. Full article