Search Results (193)

The competitive retention of pollutants in water tables determines their environmental fate and guides routes for their removal. To distinguish the fine differences in competitive binding at zeolite adsorption centers, a group of neonicotinoid pesticides is compared, relying on theoretical (energy of adsorption, orientation, charge distribution) and experimental (spectroscopic and thermogravimetric) analyses for quick, inexpensive, and reliable screening. The MOPAC/QuantumEspresso platform was used for theoretical calculation, indicating close adsorption energy values for acetamiprid and imidacloprid (−2.2 eV), with thiamethoxam having a lower binding energy of −1.7 eV. FTIR analysis confirmed hydrogen bonding, among different dipole-dipole interactions, as the dominant adsorption mechanism. Due to their comparable binding energies, when the mixture of all three pesticides is examined, comparative adsorption capacities are evident at low concentrations, owing to the excellent adsorption performance of the FAU zeotype. At higher concentrations, competition for adsorption centers occurs, with the expected thiamethoxam binding being diminished due to the lower bonding energy. The catalytic impact of zeolite on the thermal degradation of pesticides is evidenced through TG analysis, confirming the adsorption capacities found by UV/VIS and HPLC/UV measurements. Detailed analysis of spectroscopic results in conjunction with theoretical calculation, thermal profiles, and UV detection offers a comprehensive understanding of neonicotinoids’ adsorption and can help with the design of future adsorbents. Full article

This study aims to develop an optimal underwriting strategy for affordable (H1 and M1) and premium (L1 and M2) electric vehicles (EVs), balancing financial risk and sustainability commitments. The research is motivated by regulatory pressures, risk management needs, and sustainability goals, necessitating an adaptation of traditional underwriting models. The study employs a modified Delphi method with industry experts to identify key risk factors, including accident risk, repair costs, battery safety, driver behavior, and PCAF carbon impact. A sensitivity analysis was conducted to examine premium adjustments under different risk scenarios, categorizing EVs into four risk segments: Low-Risk, Low-Carbon (L1); Medium-Risk, Low-Carbon (M1); Medium-Risk, High-Carbon (M2); and High-Risk, High-Carbon (H1). Findings indicate that premium EVs (L1 and M2) exhibit lower volatility in underwriting costs, benefiting from advanced safety features, lower accident rates, and reduced carbon attribution penalties. Conversely, budget EVs (H1 and M1) experience higher premium fluctuations due to greater accident risks, costly repairs, and higher carbon costs under PCAF implementation. The worst-case scenario showed a 14.5% premium increase, while the best-case scenario led to a 10.5% premium reduction. The study recommends prioritizing premium EVs for insurance coverage due to their lower underwriting risks and carbon efficiency. For budget EVs, insurers should implement selective underwriting based on safety features, driver risk profiling, and energy efficiency. Additionally, incentive-based pricing such as telematics discounts, green repair incentives, and low-carbon charging rewards can mitigate financial risks and align with net-zero insurance commitments. This research provides a structured framework for insurers to optimize EV underwriting while ensuring long-term profitability and regulatory compliance. 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

As electric vehicles (EVs) become increasingly integrated into urban mobility, the load on electrical grids increases, prompting innovative energy management strategies. This paper investigates the deployment of vehicle-to-grid (V2G) services at the University of Rome Tor Vergata, leveraging high-resolution floating car data (FCD) to forecast and schedule energy transfers from EVs to the grid. The methodology follows a four-step process: (1) vehicle trip detection, (2) the spatial identification of V2G in the campus, (3) a real-time scheduling algorithm for V2G services, which accommodates EV user mobility requirements and adheres to charging infrastructure constraints, and finally, (4) the predictive modelling of transferred energy using ARIMA and LSTM models. The results demonstrate that substantial energy can be fed back to the campus grid during peak hours, with predictive models, particularly LSTM, offering high accuracy in anticipating transfer volumes. The system aligns energy discharge with campus load profiles while preserving user mobility requirements. The proposed approach shows how campuses can function as microgrids, transforming idle EV capacity into dynamic, decentralised energy storage. This framework offers a scalable model for urban energy optimisation, supporting broader goals of grid resilience and sustainable development. Full article

This study presents the synthesis, physicochemical characterization, and biological evaluation of a novel ternary nickel(II) complex with isoleucine and 1,10-phenanthroline ligands, [Ni(Phen)(Ile)₂]∙6H₂O, designed as a potential antitumor agent. Single-crystal X-ray diffraction revealed a monoclinic structure (C2-space group) with an octahedral Ni(II) coordination involving Phen and Ile ligands. A Hirshfeld surface analysis highlighted intermolecular interactions stabilizing the crystal lattice, with hydrogen bonds (H···H and O···H/H···O) dominating (99.1% of contacts). Density functional theory (DFT) calculations, including solvation effects (in water and methanol), demonstrated strong agreement with the experimental geometric parameters and revealed higher affinity to the water solvent. The electronic properties of the complex, such as HOMO−LUMO gaps (3.20–4.26 eV) and electrophilicity (4.54–5.88 eV), indicated a charge-transfer potential suitable for biological applications through interactions with biomolecules. Raman and infrared spectroscopic studies showed vibrational modes associated with Ni–N/O bonds and ligand-specific deformations, with solvation-induced shifts observed. A study using ultraviolet–visible–near-infrared absorption spectroscopy demonstrated that the complex remains stable in solution. In vitro cytotoxicity assays against MCF-7 (breast adenocarcinoma) and HCT-116 (colorectal carcinoma) cells showed dose-dependent activity, achieving 47.6% and 65.3% viability reduction at 100 μM (48 h), respectively, with lower toxicity to non-tumor lung fibroblasts (GM07492A, 39.8%). Supporting the experimental data, we performed computational modeling to examine the pharmacokinetic profile, with particular focus on the absorption, distribution, metabolism, and excretion properties and drug-likeness potential. 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

The growing integration of renewable energy and electric vehicle loads in parks has intensified the intermittency of photovoltaic (PV) output and demand-side uncertainty, complicating energy storage system design and operation. Meanwhile, under carbon neutrality goals, the energy system must balance economic efficiency with emission reductions, raising the bar for storage planning. To address these challenges, this study proposes a two-stage robust optimization method for shared energy storage configuration in a park-level integrated PV–storage–charging system (PV-SESS-CS). The method considers the uncertainties of PV and electric vehicle (EV) loads and incorporates carbon emission reduction benefits. First, a configuration model for shared energy storage that accounts for carbon emission reduction is established. Then, a two-stage robust optimization model is developed to characterize the uncertainties of PV output and EV charging demand. Typical PV output scenarios are generated using Latin Hypercube Sampling, and representative PV profiles are extracted via K-means clustering. For EV charging loads, uncertainty scenarios are generated using Monte Carlo Sampling. Finally, simulations are conducted based on real-world industrial park data. The results demonstrate that the proposed method can effectively mitigate the negative impact of source-load fluctuations, significantly reduce operating costs, and enhance carbon emission reductions. This study provides strong methodological support for optimal energy storage planning and low-carbon operation in park-level PV-SESS-CS. Full article

Alzheimer’s disease (AD), a progressive neurodegenerative disorder, is marked by the pathological accumulation of amyloid-β plaques and tau neurofibrillary tangles, both of which disrupt neuronal communication and function. Emerging evidence highlights the role of extracellular vesicles (EVs) as key mediators of intercellular communication, particularly in the propagation of pathological proteins in AD. Among the regulatory factors influencing EV composition and function, neuraminidase 1 (NEU1), a lysosomal sialidase responsible for desialylating glycoproteins has gained attention for its involvement in EV glycosylation. This review explores the role of NEU1 in modulating EV glycosylation, with particular emphasis on its influence on immune modulation and intracellular trafficking pathways and the subsequent impact on intercellular signaling and neurodegenerative progression. Altered NEU1 activity has been associated with abnormal glycan profiles on EVs, which may facilitate the enhanced spread of amyloid-β and tau proteins across neural networks. By regulating glycosylation, NEU1 influences EV stability, targeting and uptake by recipient cells, primarily through the desialylation of surface glycoproteins and glycolipids, which alters the EV charge, recognition and receptor-mediated interactions. Targeting NEU1 offers a promising therapeutic avenue to restore EV homeostasis and reduces pathological protein dissemination. However, challenges persist in developing selective NEU1 inhibitors and effective delivery methods to the brain. Furthermore, altered EV glycosylation patterns may serve as potential biomarkers for early AD diagnosis and monitoring. Overall, this review highlights the importance of NEU1 in AD pathogenesis and advocates for deeper investigation into its regulatory functions, with the aim of advancing therapeutic strategies and biomarker development for AD and related neurological disabilities. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper presents the first real-time quantum-enhanced electricity pricing framework for large-scale EV charging networks, marking a significant departure from existing approaches based on mixed-integer programming (MILP) and deep reinforcement learning (DRL). The proposed framework incorporates renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The objective is to dynamically determine spatiotemporal electricity prices that reduce system peak load, improve renewable utilization, and minimize user charging costs. A rigorous mathematical formulation is developed, integrating over 40 system-level constraints, including power balance, transmission limits, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber-resilience. Real-time electricity prices are treated as dynamic decision variables influenced by station utilization, elasticity response curves, and the marginal cost of renewable and grid electricity. The model is solved across 96 time intervals using a quantum-classical hybrid method, with benchmark comparisons against MILP and DRL baselines. A comprehensive case study is conducted on a 500-station EV network serving 10,000 vehicles, coupled with a modified IEEE 118-bus grid and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar/wind profiles are used to simulate realistic conditions. Results show that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% gain in renewable utilization, and up to 30% user cost savings compared to flat-rate pricing. Network congestion is mitigated at over 90% of high-traffic stations. Pricing trajectories align low-price windows with high-renewable periods and off-peak hours, enabling synchronized load shifting and enhanced flexibility. Visual analytics using 3D surface plots and disaggregated bar charts confirm structured demand-price interactions and smooth, stable price evolution. These findings validate the potential of quantum-enhanced optimization for scalable, clean, and adaptive EV charging coordination in renewable-rich grid environments. Full article

This paper addresses the challenges of efficient electric vehicle (EV) charging integration in Direct Current (DC) microgrids (MGs), particularly the impact of intermittent EV loads on power sharing and voltage regulation. Traditional droop control methods suffer from inherent trade-offs between performance indices of parallel distributed energy resources (DERs), which in turn results in improper source utilization. We propose a novel decentralized piece-wise droop control (PDC) approach with voltage compensation for EV charging to overcome this limitation and to minimize the unequal cable resistance effect on power sharing. This strategy dynamically optimises the droop characteristics based on EV charging load profiles, partitioning the droop curve to optimize power sharing accuracy and voltage stability considering the constraints of maximum allowable voltage deviation and loading. Simulation and experimental results demonstrate significant improvements in power sharing, enhanced DER utilization, and voltage deviations consistently within 2.5% when compared with traditional strategies. PDC offers a robust solution for enabling efficient and reliable EV charging in MGs, as it is not sensitive for EV load prediction errors and measurement noise. Full article

The Renewable Energy Community (REC) has emerged in Europe, encouraging the use of renewable energy sources (RESs) within localities, bringing social, economic, and environmental benefits. RESs are characterized by various loads, including household consumption, storage systems, and the increasing integration of electric vehicles (EVs). EVs offer opportunities for distributed RESs, such as photovoltaic (PV) systems, which can be economically advantageous for RECs whose members own EVs and charge them within the community. This article focuses on the integration of PV systems and the management of energy loads for different participants—consumers and prosumers—along with a small EV charging setup in the REC. A REC consisting of a multi-unit building is examined through a mathematical and numerical model. In the model, hourly PV generation data are obtained from the PVGIS tool, while residential load data are modeled by converting monthly electricity bills, including peak and off-peak details, into hourly profiles. Finally, EV hourly load data are obtained after converting the data of voltage and current data from the charging monitoring portal into power profiles. These data are then used in our mathematical model to evaluate energy fluxes and to calculate self-consumed, exported, and shared energy within the REC based on energy balance criteria. In the model, an energy management system (EMS) is included within the REC to analyze EV charging behavior and optimize it in order to increase self-consumption and shared energy. Following the EMS, it is also suggested that the number of EVs to be charged should be evaluated in light of energy-sharing incentives. Numerical results have been reported for different seasons, showing the possibility for the owners of EVs to charge their vehicles within the community to optimize self-consumption and shared energy. Full article

The increasing adoption of electric vehicles (EVs) is driving the need for more efficient, scalable, and flexible charging infrastructures. Among the most promising solutions are reconfigurable multi-point multi-power (MPMP) charging stations, which enable dynamic power allocation across multiple charging points operating at discrete power levels. This paper introduces a novel power management strategy for MPMP stations based on Pareto optimization, aiming to minimize the average charging time while ensuring fairness and efficiency. The method dynamically allocates power among charging points to minimize the average charging time across all connected EVs, while adhering to system constraints and the varying charging profiles required to preserve battery health. The proposed approach was validated through simulations in a dynamic scenario involving six EVs with heterogeneous battery capacities and charging profiles. Results demonstrated that the Pareto-based strategy achieved a significantly lower expected average charging time when compared to the first-come first-served strategy (FCFS). Full article

This study investigated the impact of electric vehicle (EV) chargers on residential electrical systems through a real-world case study in a condominium located in Bangkok, Thailand. A two-week field measurement was conducted to analyze load profiles, current and voltage behavior, phase symmetry, and harmonic distortion during EV charger operation. The results show that single-phase charging dominated usage patterns, leading to phase imbalance and significant neutral current flow. Voltage unbalance was quantified using the maximum deviation method, with an average value of 0.535 percent and a peak of 2.18 percent observed during charging activity. A harmonic distortion analysis revealed a substantial increase in current total harmonic distortion (THD) during active charging, with values rising to between 15 and 20 percent. These findings highlight nonlinear loading effects that may reduce power quality and pose risks to electrical equipment and system stability. In retrofitted electrical infrastructures, these effects are often exacerbated by design limitations and the absence of coordinated load management. This study’s findings offer practical insights for engineers, facility managers, and policymakers in designing EV-ready residential systems that are both efficient and resilient. Full article

This paper presents an optimization model for the charging infrastructure of electric vehicles (EV) designed to minimize installation costs, maximize the utilization of photovoltaic energy, reduce dependency on the electrical grid, and optimize charging times. The model utilizes methodologies such as Linear Optimal Power Flow (LOPF) to align EV charging schedules with the availability of renewable energy sources. Key inputs for the model include Photovoltaic (PV) production profiles, EV charging demands, specifications of the chargers, and the availability of grid energy. The framework integrates installation costs, grid energy consumption, and charging duration into a weighted objective function, ensuring energy balance and operational efficiency while adhering to budgetary constraints. Five distinct optimization scenarios are analyzed to evaluate the trade-offs between cost, charging duration, and reliance on various energy sources. The simulation results obtained from Cadi Ayyad University validate the model’s effectiveness in balancing costs, enhancing charging performance, and increasing dependence on solar energy. This approach provides a comprehensive solution for the development of sustainable and cost-effective EV charging infrastructure. Full article

The production of electric vehicle (EV) batteries is playing an increasingly significant role in the decarbonization of the mobility sector. In order for EV batteries to be competitive against internal combustion engines, it is crucial to maximize the primary and secondary life cycles of batteries. This necessitates a battery management system that can ensure performance, safety, and longevity. State of Charge (SoC) estimation is important for such a system, as it ensures efficiency of the battery’s performance, and it is necessary for the prediction of the battery’s health and lifespan. Existing SoC estimation methods heavily depend on laboratory tests, which are both costly and time consuming. Additionally, the simulated nature of laboratory settings cannot guarantee robustness when the same method is applied to field data collected from real-world scenarios. A suitable alternative to this problem is the use of data-driven approaches. The goal of this work is the estimation of SoC with a real-world dataset using neural networks. Furthermore, we demonstrate how explainable AI (xAI) and importance estimate can be applied to inform what signals and which parts of a signal are important for SoC estimation. This helps to reduce redundancy, and it provides more information regarding the relationships within battery cells that are otherwise obscured by the complexity of the battery. The methods that we used resulted in a mean squared error (MSE) of as low as 3 × ${10}^{-4}$, and the information provided by xAI suggested that it is possible to discard up to 25% of the input profile whilst retaining similar performance. Full article