Search Results (375)

Zinc oxide (ZnO) nanostructures were synthesized via a hydrothermal method by systematically varying the reaction time (6–24 h) while maintaining all other parameters constant. The morphological evolution progressed from nanoparticles to nanoneedles, nanoflakes, and nanoplates with increasing reaction duration. X-ray diffraction and Raman spectroscopy confirmed the formation of hexagonal wurtzite ZnO for all samples, accompanied by a gradual shift in the preferred growth orientation from the c-axis to the a-axis. The optical characterization revealed a pronounced dependence of the band gap and the defect density on the synthesis time, with the nanoflakes obtained at 12 h exhibiting a narrowed band gap of 2.9 eV and an enhanced visible light absorption. The photocatalytic degradation of methylene blue followed zero-order kinetics, where the ZnO nanoflakes achieved the highest rate constant (k₀ = 0.01893 min⁻¹). The enhanced activity is attributed to the combined effects of a reduced band gap, an increased surface area, the coexistence of ZnO/Zn(OH)₂ phases, and a defect-assisted charge separation. Full article

Lithium-ion batteries are widely used in portable devices and electronic vehicles (EVs) due to their excellent performance. Because of their internal chemistry, these batteries have non-linear characteristics, their parameters being dependent on temperature and varying over time due to aging. Since electric vehicles are marketed in different regions of the globe with different climates, this has led to increased attention to the problem of the reduced performance of EVs in colder environments. The purpose of this research is to study the effects of preconditioning on Li-ion cells and determine the need for preconditioning in EVs that operate under low-temperature conditions. Additionally, based on the results, alternative coping strategies are also suggested which can be used instead of preconditioning when this is not a viable option. Given this, the 18650 Li-ion cells studied were divided into two categories, cells to be charged/discharged permanently at low temperatures and cells that were to be exposed to the same low temperatures but then preconditioned to ambient temperature before the charge/discharge cycle for a total of 100 performed cycles. It was observed that low temperatures have a direct negative impact on the usable capacity of the cells, accounting for a drop of 8% of the initial value. These effects can be completely negated by preconditioning the cells prior to charging/discharging. After that, the effects of medium-term storage on the capacity of the batteries were investigated to study the possible recovery in the capacity of the cells. Finally, the need for preconditioning the cells is analyzed and alternative methods to mitigate the issues are suggested for equipment where preconditioning is not possible. Full article

The widespread adoption of Electric Vehicles (EVs) is critically dependent on the deployment of efficient charging infrastructure. However, existing facility location models typically treat charging duration as an exogenous parameter, thereby neglecting the traveler’s autonomy to make trade-offs between service time and energy needs based on their Value of Time (VoT). This study addresses this theoretical gap by developing a heterogeneous network design model that endogenizes both charging mode selection and continuous charging duration decisions. A bi-objective optimization framework is formulated to minimize the weighted sum of infrastructure capital expenditure and users’ generalized travel costs. To ensure computational tractability for large-scale networks, an exact linearization technique is applied to reformulate the resulting Mixed-Integer Non-Linear Program (MINLP) into a Mixed-Integer Linear Program (MILP). Application of the model to the Hubei Province highway network reveals a convex Pareto frontier between investment and service quality, providing quantifiable guidance for budget allocation. Empirical results demonstrate that the marginal return on infrastructure investment diminishes rapidly. Specifically, a marginal budget increase from the minimum baseline yields disproportionately large reductions in system-wide dwell time, whereas capital allocation beyond a saturation point yields diminishing returns, offering negligible service gains. Furthermore, sensitivity analysis indicates an asymmetry in technological impact: while extended EV battery ranges significantly reduce user dwell times, they do not proportionally lower the capital required for the foundational infrastructure backbone. These findings suggest that robust infrastructure planning must be decoupled from anticipations of future battery breakthroughs and instead focus on optimizing facility heterogeneity to match evolving traffic flow densities. Full article

Electric vehicles (EVs) are pivotal for enhancing the flexibility of power systems, with vehicle-grid integration (VGI) constituting the fundamental mechanism for their participation in grid regulation. VGI relies on multi-source information from EVs, charging infrastructure, traffic network, power grid, and meteorology. However, ineffective data integration mechanisms have resulted in data silos, which impede the realization of synergistic value from multi-source data fusion. To address these issues, this paper develops a quadripartite evolutionary game model that incorporates data providers, data users, government, and data service platforms, overcoming the limitation of traditional tripartite models in fully capturing the complete data value chain. The model systematically examines the cost–benefit dynamics and strategy evolution among stakeholders throughout the data-sharing process. Leveraging evolutionary game theory and Lyapunov stability criteria, sensitivity analyses were conducted on key parameters, including data costs and government subsidies, on the MATLAB platform. Results indicate that multi-source data integration accelerates system convergence and facilitates a multi-party equilibrium. Government subsidies as well as reward and punishment mechanisms emerge as critical drivers of sharing, with an identified subsidy threshold of ε_S = 0.02 for triggering multi-source integration. These key factors can also accelerate system convergence by up to 79% through enhanced subsidies (e.g., reducing stabilization time from 0.29 to 0.06). Importantly, VGI data sharing represents a non-zero-sum game. Well-designed institutional frameworks can achieve mutually beneficial outcomes for all parties, providing quantitatively supported strategies for constructing incentive-compatible mechanisms. Full article

Traditional routing algorithms optimizing for distance or travel time are inadequate for electric vehicles (EVs), which require energy-aware planning considering battery constraints and charging infrastructure. This work presents an energy-optimal routing system for EVs that integrates personalized consumption modeling with real-time environmental data. The system employs a Long Short-Term Memory (LSTM) neural network to predict State-of-Charge (SoC) consumption from real-world driving data, learning directly from spatiotemporal features including velocity, temperature, road inclination, and traveled distance. Unlike physics-based models requiring difficult-to-obtain parameters, this approach captures nonlinear dependencies and temporal patterns in energy consumption. The routing framework integrates static map data, dynamic traffic conditions, weather information, and charging station locations into a weighted graph representation. Edge costs reflect predicted SoC drops, while node penalties account for traffic congestion and charging opportunities. An enhanced A* algorithm finds optimal routes minimizing energy consumption. Experimental validation on a Nissan Leaf shows that the proposed end-to-end SoC estimator significantly outperforms traditional approaches. The model achieves an RMSE of 36.83 and an $R^2$ of 0.9374, corresponding to a 59.91% reduction in error compared to physics-based formulas. Real-world testing on various routes further confirms its accuracy, with a Mean Absolute Error in the total route SoC estimation of 2%, improving upon the 3.5% observed for commercial solutions. Full article

Against the backdrop of China’s dual-carbon goals, traditional road transportation has relatively high carbon emissions and is in urgent need of a low-carbon transition. The intermittency of photovoltaic (PV) power generation and the stochastic nature of electric vehicle (EV) charging demand introduce significant uncertainty for PV-energy storage-charging systems in highway service areas. Existing approaches often struggle to balance economic efficiency and reliability. This study develops a min-max-min robust optimization model for a full-route PV-energy storage-charging system. A box uncertainty set is used to characterize uncertainties in PV output and EV load, and a tunable uncertainty parameter is introduced to regulate risk. The model is solved using a column-and-constraint generation (C&CG) algorithm that decomposes the problem into a master problem and a subproblem. Strong duality, combined with a big-M formulation, enables an alternating iterative solution between the master problem and the subproblem. Simulation results demonstrate that the proposed algorithm attains the optimal solution and, relative to deterministic optimization, achieves a desirable trade-off between economic performance and robustness. Full article

The dynamic growth of the electrified vehicle (xEV) market, including both electric and hybrid vehicles, has increased the demand for advanced Battery Management Systems (BMS). From an energy-systems perspective, xEV batteries act as distributed energy storage units that strongly interact with power grids, renewable generation, and charging infrastructure, making their efficient control a key element of low-carbon energy systems. Traditional BMS methods face challenges in accurately estimating key battery states and parameters, especially under dynamic operating conditions. This review systematically analyzes the progress in applying artificial intelligence, machine learning, and other advanced computational and data-driven algorithms to improve the performance of xEV battery management with a particular focus on energy efficiency, safe utilization of stored electrochemical energy, and the interaction between vehicles and the power system. The literature analysis covers key research trends from 2020 to 2025. This review covers a wide range of applications, including State of Charge (SOC) estimation, State of Health (SOH) prediction, and thermal management. We examine the use of various methods, such as deep learning, neural networks, genetic algorithms, regression, and also filtering algorithms, to solve these complex problems. This review also classifies the research by geographical distribution and document types, providing insight into the global landscape of this rapidly evolving field. By explicitly linking BMS functions with energy-system indicators such as charging load profiles, peak-load reduction, self-consumption of photovoltaic generation, and lifetime-aware energy use, this synthesis of contemporary research serves as a valuable resource for scientists and engineers who wish to understand the latest achievements and future directions in data-driven battery management and its role in modern energy systems. Full article

The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address uncertainties such as fluctuating solar irradiance, grid instability, and dynamic load demands. A MATLAB-R2023a/Simulink-R2023a model was developed to simulate the charging process using real-time adaptive control. The fuzzy logic controller (FLC) automatically updates the PID gains by evaluating the error and how quickly the error is changing. This adaptive approach enables efficient voltage regulation and improved system stability. Simulation results demonstrate that the proposed fuzzy–PID controller effectively maintains a steady charging voltage and minimizes power losses by modulating switching frequency. Additionally, the system shows resilience to rapid changes in irradiance and load, improving energy efficiency and extending battery life. This hybrid approach outperforms conventional PID and static control methods, offering enhanced adaptability for renewable-integrated EV infrastructure. The study contributes to sustainable mobility solutions by optimizing the interaction between solar energy and EV charging, paving the way for smarter, grid-friendly, and environmentally responsible charging networks. These findings support the potential for the real-world deployment of intelligent controllers in EV charging systems powered by renewable energy sources This study is purely simulation-based; experimental validation via hardware-in-the-loop (HIL) or prototype development is reserved for future work. Full article

Lithium-ion batteries (LIBs) are vital components in electric vehicles (EVs) and battery energy storage systems (BESS). Accurate estimation of the state of charge (SOC) and state of health (SOH) depends heavily on precise battery modeling. This paper examines six commonly used equivalent circuit models (ECMs) by deriving their impedance transfer functions and comparing them with measured electrochemical impedance spectroscopy (EIS) data. The particle swarm optimization (PSO) algorithm is first utilized to identify the ECM with the best EIS fit. Then, thirteen bio-inspired optimization algorithms (BIOAs) are employed for parameter identification and comparison. Results show that the fractional-order R(RQ)(RQ) model with a mean absolute percentage error (MAPE) of 10.797% achieves the lowest total model fitting error and possesses the highest matching accuracy. In model parameter identification using BIOAs, the marine predators algorithm (MPA) reaches the lowest estimated MAPE of 10.694%, surpassing other algorithms in this study. The Friedman ranking test further confirms MPA as the most effective method. When combined with an Internet-of-Things-based online battery monitoring system, the proposed approach provides a low-cost, high-precision platform for rapid modeling and parameter identification, supporting advanced SOC and SOH estimation technologies. Full article

Electric vehicles (EVs) have gained significant popularity globally during the past decade. This is mostly due to their reduced emissions of hydrocarbons and greenhouse gases. Electric vehicles acquire their electricity via wireless energy transmission, thereby circumventing the challenges associated with conventional techniques. The coils that transmit and receive signals deteriorate in performance and age as temperatures increase. Under extreme conditions, this may result in fire hazards and further safety issues. This article examined the electromagnetic and thermal dispersion of a magnetically coupled coil model for electric vehicles. This paper studied the electromagnetic and temperature distribution of the magnetically coupled coil model for electric vehicles. The coils were designed utilizing ANSYS software, with boundary conditions and pertinent parameters configured accordingly. The transmitter and receiver coils were identical in dimensions, with an inner diameter of 100 mm, an outer diameter of 295 mm, and an air gap of 60 mm between them. The magnetic coil was simulated and analyzed using copper as a material. In the aligned positions, the coupling coefficient between the transmitter and receiver coil was 0.168, its maximum temperature was 16.92 °C, and it was lower for the safety of the human body. An actual prototype was built to confirm the simulation results and to establish that the methodology employed in this research is applicable to the design of magnetic coils for a wireless charging system for electric vehicle models. Full article

With the discovery of amorphous oxide semiconductors, a new era of electronics opened. Indium gallium zinc oxide (IGZO) overcame the problems of amorphous and poly-silicon by reaching mobilities of ~10 cm²/Vs and demonstrating thin-film transistors (TFTs) are easy to manufacture on transparent and flexible substrates. However, mobilities over 30 cm²/Vs have been difficult to reach and other materials have been introduced. Recently, polycrystalline In₂O₃ has demonstrated breakthroughs in the field. In₂O₃ TFTs have attracted attention because of their high mobility of over 100 cm²/Vs, which has been achieved multiple times, and because of their use in scaled devices with channel lengths down to 10 nm for high integration in back-end-of-the-line (BEOL) applications and others. The present review focuses first on the material properties with the understanding of the bandgap value, the importance of the position of the charge neutrality level (CNL), the doping effect of various atoms (Zr, Ge, Mo, Ti, Sn, or H) on the carrier concentration, the optical properties, the effective mass, and the mobility. We introduce the effects of the non-parabolicity of the conduction band and how to assess them. We also introduce ways to evaluate the CNL position (usually at ~E_C + 0.4 eV). Then, we describe TFTs’ general properties and parameters, like the field effect mobility, the subthreshold swing, the measurements necessary to assess the TFT stability through positive and negative bias temperature stress, and the negative bias illumination stress (NBIS), to finally introduce In₂O₃ TFTs. Then, we will introduce vacuum and non-vacuum processes like spin-coating and liquid metal printing. We will introduce the various dopants and their applications, from mobility and crystal size improvements with H to NBIS improvements with lanthanides. We will also discuss the importance of device engineering, introducing how to choose the passivation layer, the source and drain, the gate insulator, the substrate, but also the possibility of advanced engineering by introducing the use of dual gate and 2 DEG devices on the mobility improvement. Finally, we will introduce the recent breakthroughs where In₂O₃ TFTs are integrated in neuromorphic applications and 3D integration. Full article

Given the growing interest in contaminant detection, research has addressed the functionalization behavior of graphene quantum dots (GQDs) with thiol (-SH) and amino (-NH₂) groups to optimize and improve the selective detection of heavy metals in wastewater. Implementing Density Functional Theory (DFT), the interactions between the functionalized GQDs and hydrated metals such as Cr, Cd, and Pb were simulated. The results showed that GQDs with thiol groups exhibited a high affinity for metals such as Pb and Cd, with an energy gap (Eg) of 0.02175 eV in the interaction with Pb, showing optimized reactivity. On the other hand, amino-modified GQDs presented a higher Eg, indicating a lower reactivity and efficacy in contaminant identification. Furthermore, this study evaluated electronic properties such as the energy gap and total dipole moment (TDM), resulting in the -SH-functionalized GQDs showing a higher TDM, which presented a greater interaction capacity with these metals. Likewise, the electrostatic potential maps (MEPs) provided information on the charge distribution when adsorbing metals, an important parameter to understand electronic interactions. These results showed that the modification of GQDs improved the detection of heavy metals, although limitations in the DFT method used are recognized and the need for experimental studies is suggested to validate the results and investigate other functional modifications. Full article

The increasing demand for reliable DC fast-charging stations in electric vehicle (EV) infrastructure necessitates efficient fault detection mechanisms to ensure operational stability and user safety. This paper will present the development of a diagnostic method for identifying open-circuit faults and short-circuit faults in DC charging stations by leveraging Total Harmonic Distortion (THD) analysis combined with a Second-Order Generalized Integrator (SOGI). The proposed approach uses the THD method to detect anomalies in the current and voltage waveforms, while the Frequency Locked Loop (FLL) serves to track the frequency of the grid and keep the SOGI tuned to it, and SOGI-FLL provides the rectifier with the capability of tracking the frequency, amplitude, voltage, and phase of the grid and monitoring these parameters of the grid. The ability to measure the THD is the kernel of the detection of faults. Detailed simulation confirms the method’s high sensitivity and robustness in detecting open/short circuit faults with minimal false positives. This technique offers a cost-effective, non-invasive diagnostic solution suitable for modern DC charging systems, contributing to improved reliability and efficiency of EV charging infrastructure. Full article

To operate electric vehicle (EV) fleets in a safe and efficient manner, many companies have been deploying charging infrastructures (CIs) at their premises. Forecasting of different system parameters of a CI, such as how many charging points will be occupied during the day, can help create accurate charge plans. In this paper, we deal with the applicability of continuous Nowcasting, i.e., frequently executed short-term forecasts, to predict the next few data points based on the past and current situation in a CI. Specifically, we forecast the number of charging EVs over a rolling two-hour horizon using XGBoost and LSTM. In the experiments, we apply different weighting schemes to emphasize the relevance of the most recent observations combined with different multi-horizon forecasting strategies. Experimental results using a real-world dataset show that a linear weighting schema combined with a direct forecasting strategy using XGBoost achieves the lowest RMSE value of 0.906 for the 15 min forecasting horizon when predicting the number of active charging stations. For the 2 h horizon, the best RMSE of 2.545 is achieved with XGBoost using the strategy Direct, but with an exponential weighting strategy. We then illustrate how short-term predictions can be used to improve the operational efficiency of an example CI by dynamically adjusting power limits based on the latest prediction results. Full article

Due to the environmental impacts of greenhouse gas emissions from traditional combustion vehicles, governments worldwide are encouraging the transition to electric vehicles (EVs). However, as EV use increases, user-related charging challenges have become evident. To identify possible solutions to improve EV charging management from a user-centered perspective, a state-of-the-art study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Optimization systems and artificial intelligence (AI) methods applied to decision-making were compared and a growing trend towards the implementation of artificial intelligence in current applications was identified. This study investigates in more depth the application of AI in multi-agent systems for energy management in EV charging. It provides a critical review of charging stations, focusing on aggregator-based models that operate within a multi-agent system in smart grids. This analysis adopts the vehicle owner’s perspective and considers the charging duration of the EV as a parameter. This article identifies significant gaps in how existing research addresses individual electric vehicle users, noting a lack of consideration for energy management and system connectivity to support EV recharging locations. This work presents solutions to these gaps by using aggregators and multi-agent systems to represent charging stations, facilitate user access, and improve energy management. Full article