Electric Charge Research Articles

Solar Powered Electric Vehicle Charging Station With Integrated Battery Storage System

ABSTRACTThe shift towards electrical vehicles (EVs) can be an important alternative to internal combustion engines for sustainable energy solutions. However, increased EV adoption will increase the charging demand, and there will be a load on the grid electricity. Integrating solar photovoltaic systems with EV charging infrastructure will not only support environmental goals, but also ensure a more resilient and self‐sufficient energy system. A standalone PV system is a good option to reduce the stress on the grid for charging EVs. This present work pivots on the design and performance assessment of a solar photovoltaic system customized for an electric vehicle charging station in Bangalore, India. For this purpose, we have used the PVsyst software to design and optimize a standalone PV system with battery energy storage for EV charging stations. The result shows that 51.1 kWp PV system will be sufficient to meet the energy demand of the charging station by producing 98 313 kWh array energy. The proposed system showed a good average performance ratio of 68.90%. This study shows that the integration of standalone solar photovoltaic systems with EV charging stations is crucial in India and other countries to alleviate grid stress and promote sustainable energy use. This approach not only supports the transition to cleaner transportation but also enhances energy security and reduces dependency on fossil fuels.

Electric Vehicle Charging Station Recommendations Considering User Charging Preferences Based on Comment Data

User preferences are important for electric vehicle charging station (EVCS) recommendations, but they have not been deeply analyzed. Therefore, in this study, user charging preferences are identified and applied to EVCS recommendations using a hybrid model that integrates LightGBM and singular value decomposition (SVD). In the model, LightGBM is used to predict user ratings according to users’ comments regarding charging orders, and the feature importance reported by each user is output. Then, a co-occurrence matrix between users and charging stations (EVCSs) is constructed and decomposed using SVD. Based on the decomposed results, the final evaluated scores of each user for EVCSs can be calculated. Upon ranking the EVCSs according to the scores, the EVCS recommendation results are obtained, taking into account the users’ charging preferences. The sample data consist of 28,306 orders from 508 users at 241 charging stations in Linyi, Shandong, China. The experimental results show that the proposed hybrid model outperforms the benchmark models in terms of precision, recall, and F1 score, and its F1 score can be increased by 96% compared with that of the traditional item-based collaborative filtering method with charging counts for EVCS recommendations.

Enhancing Flexibility and Reliability in Smart Distribution Networks: A Self-Healing Approach

This paper presents a pioneering approach that integrates a Self-Healing System through a Smart Distribution Network, which employs a two-step implementation of a comprehensive Energy Management algorithm and a multi-agent system with an autonomous distributed regeneration method. The novel method improves network operation through optimized management of local microgrids, which include diverse components comprising renewable energy sources, electric vehicle charging infrastructure, and energy storage systems. The first phase concentrates on minimizing network operation costs by adhering to system utilization restrictions and optimal load distribution, while the second phase addresses the optimization of regeneration processes, which decreases discrepancies between recoverable priority loads and switching operations. The evaluation introduces a stochastic programming approach to model and manage uncertainties, and utilizes the Kantorovich method and the roulette wheel mechanism to improve reliability. ...

Demand management of plug-in electric vehicle charging station considering bidirectional power flow using deep reinforcement learning

The recent development in plug-in electric vehicle technology has increased its popularity. Plug-in electric vehicles are widely used for their environment-friendly nature and they contribute to the reduction in global warming. With the increasing number of plug-in electric vehicles, charging coordination becomes essential for managing the charging station demand. The random charging behaviour of plug-in electric vehicles makes it a difficult task. This paper proposes a novel demand management strategy of charging stations. The proposed strategy supports the grid and reduces its burden during peak load. Deep-Q network based deep reinforcement learning is used in the proposed strategy. It is a value based deep reinforcement learning algorithm that approximates the Q value function using deep neural network. The deep reinforcement schedules the charging and discharging of plug-in electric vehicles to optimize the cost and manage the charging station load. Deep reinforcement learning enhances charging coordination by dynamically optimizing charging schedules according to real-time conditions and user preferences, thereby increasing efficiency and better integration with the grid. The reward function in deep reinforcement learning is designed based on the power price and demand of the charging station. A discount factor is introduced based on the service time to make the charging coordination efficient. A case study using dynamic pricing is carried out to validate the proposed strategy. The results prove that the proposed strategy optimizes charging and discharging costs and manages charging station demand efficiently. It is also observed that the proposed strategy is fast and incurs less computational cost.

Observation of floating surface state in obstructed atomic insulator candidate NiP2

Obstructed atomic insulator is recently proposed as an unconventional material, in which electric charge centers localized at sites away from the atoms. A half-filling surface state would emerge at specific interfaces cutting through these charge centers and avoid intersecting any atoms. In this article, we utilized photoemission spectroscopy and density functional theory calculations to study one of the obstructed atomic insulator candidates, NiP2. A floating surface state with large effective mass that is close to the Fermi level and isolated from all bulk states is resolved on the (100) cleavage plane, implying better catalytic activity in this plane than the previously studied surfaces. Density functional theory calculation results elucidate that this floating surface state is originated from the obstructed Wannier charge centers, albeit underwent surface reconstruction. Our findings not only shed lights on the study of obstructed atomic insulators, but also provide possible route for development of new catalysts.

Improved binary quantum-based Elk Herd optimizer for optimal location and sizing of hybrid system in micro grid with electric vehicle charging station

In recent times, there has been increasing interest in renewable power generation and electric vehicles within the domain of smart grids. The integration of electric vehicles with hybrid systems presents several critical challenges, including increased power loss, power quality issues, and voltage deviations. To tackle these challenges, researchers have proposed various techniques. Effective management of energy systems is essential for maximizing the benefits of integrating a hybrid system with a microgrid at an electric vehicle charging station. This research specifically aims to optimize the location and sizing of such a hybrid system within the microgrid. Additionally, an improved binary quantum-based Elk Herd optimizer approach is proposed. This approach addresses for optimally managing renewable energy sources and load uncertainty. The proposed system also considers the stochastic nature of electric vehicles and operational restrictions, encompassing diverse charging control modes. The proposed technique performance is implemented in MATLAB platform and compared against existing approaches. The analysis demonstrates the effectiveness in achieving optimal location and sizing for a hybrid system with an electric vehicle charging station. Additionally, the proposed approach contributes to minimizing power loss, electricity costs, and average waiting time. Furthermore, the proposed approach reduces computing time, net present cost, and emissions are 12.5 s, 1.1×106 dollar, 2.21×108 g year−1, respectively.

Multi-objective model for electric vehicle charging station location selection problem for a sustainable transportation infrastructure

The transportation industry mostly depends on conventional vehicles, leading to significant adverse effects on the environment. The widespread usage of electric vehicles can be seen as a relief for this problem. However, the success of electric vehicles largely depends on the availability and proper deployment of charging station infrastructure. It is crucial for cities to strategically select suitable locations for charging stations with adequate capacity levels to promote sustainable and environmentally-friendly transportation options. Hence, in this study, a multi-objective model is proposed for the electric vehicle charging station location selection and capacity allocation problem. The model aims to maximize customer satisfaction, minimize total risk, and minimize costs as key objective functions. To manage the demand effectively, the region of interest is divided into grids. The proposed multi-objective model is applied to the European side of Istanbul and solved by using AUGMECON2 technique. Finally, computational analyses are presented based on scenarios including different demand values. These analyses provide valuable insights into the effectiveness of the proposed model and its implications for achieving sustainable transportation in Istanbul.

Design and evaluation of a standalone electric vehicles charging station for a university campus in Argentina

The increasing popularity of electric vehicles in recent years has led to a growing demand for charging stations. In this context, universities are an ideal setting for their installation, as they have a large number of students, professors, and staff who could benefit from them and, at the same time, it serves as teaching material to raise awareness in the use of renewable energy. This work presents the design and proposal of an electric vehicle charging station for the campus of the Universidad Nacional de Rafaela (UNRaf). The station will be located in an area of the campus where the construction of more buildings and sport facilities is planned. This area will not be connected to the electrical grid and instead, will have an energy storage system to guarantee supply. The station will have the capacity to simultaneously charge 4 bicycles and 2 light electric vehicles, with an average energy demand of 0.786 kWh per hour. Homer Pro software was used for the calculations. The most economically viable option was a 100% renewable solution powered only by solar energy. It is expected to consist of a 15-kW solar system that will produce 22,922 kWh/year and a bank of 30 batteries of 3 kWh plus a single battery of 1 kWh. The installation of the electric vehicle charging station on the UNRaf campus will contribute to promoting the adoption of sustainable transportation, which will help reduce greenhouse gas emissions without using the public power grid.

Standard Methods for Evaluating Electrostatic Spray Charge and Deposition of Conductive Liquids Used for Surface Disinfection

Standard Methods for Evaluating Electrostatic Spray Charge and Deposition of Conductive Liquids Used for Surface Disinfection

Microstructure evolution of CdZnTe crystals irradiated by heavy ions

Consideration of irradiation effects is important for in-orbit operation and radiation protection in aerospace applications. The configuration of irradiation defects and their impact on deteriorating the carrier transport properties of CZT crystals need to be clarified. In this study, the microdefect evolution mechanism of CdZnTe (CZT) crystals under 10 MeV Chlorine (Cl) ion irradiation at flux of 1×1010∼1×1012 n·cm-2 are investigated. The stacking faults first appear in the CZT crystal at 4.5×10-4 displacement per atom (dpa), which are categorized into slip-type, gap-type and vacancy-type. In order to hinder the further expansion of stacking fault, S-shaped stacking fault dipole appears. When the dpa reaches 4.5×10-2, the local phase transition occurs in the damage zone to form CuPt-A ordered phase with ZnTe/CdTe periodic arrangement because the fluctuation in local composition and strain caused by cascade collisions. The deterioration of electric field distribution and charge collection caused by irradiation damage layer leads to the decrease of γ-ray detection ability. The energy resolution (ER) of the CZT detector for γ-ray can be maintained at 30.6% when the ion flux accumulates to 1×1012 n·cm-2, revealing high irradiation hardness. Consequently, these findings provide a theoretical basis for the radiation damage recovery of CZT detectors, further demonstrating that CZT crystals are the most competitive new generation of room-temperature nuclear radiation detection materials, and can provide radiation protection strategies for similar compound semiconductor materials.

Load forecasting based on multi-core learning Support Vector Machine (SVM)

Abstract The development of smart grids requires enhanced data integration, robust risk assessment, and dynamic response optimization. In this paper, a multi-core learning Support Vector Machine (SVM) model is presented to improve the accuracy and efficiency of load and photovoltaic output forecasting. The model leverages kernel function optimization and parallel computing frameworks to handle large-scale data efficiently. Additionally, a comprehensive risk assessment system is developed to quantify risks such as overvoltage, undervoltage, line overload, and load loss in distribution networks. An adaptive genetic algorithm-based risk control model is also proposed, optimized in two stages—day-ahead and intra-day—to achieve minimal comprehensive risk through real-time adjustments in distributed power output and electric vehicle charging strategies. Furthermore, an integrated virtual synchronous control online verification method for source-network-load-storage is introduced, enhancing system response speed and control accuracy. These innovations collectively provide a solid theoretical foundation and technical support for the efficient and safe operation of smart grids, addressing the increasing demands of modern energy systems.

ChatEV: Predicting electric vehicle charging demand as natural language processing

The increasing popularity of electric vehicles (EVs) in recent times has introduced considerable load conditions for urban power grids and transportation systems, which highlights the importance of accurately predicting charging demand to enhance charging efficiency. However, current forecasting methods still face challenges in effectively aligning diverse data and generating accurate predictions that can be applied to unseen scenarios. To overcome the challenges, this work introduces a novel perspective: employing large language models (LLMs) as EV charging demand predictors. First, we reformulate the prediction task into a text-to-text format, enabling seamless and effective alignment of various features within a unified language semantic space. Subsequently, we fine-tune a LLM using a meta-learning framework to adapt it specifically for EV charging prediction. Through comprehensive evaluations, it has been demonstrated that the proposed model, ChatEV, achieves outstanding performance in EV charging demand forecasting, particularly in scenarios with limited data.

Non-Arrhenius grain growth in SrTiO3: Impact on grain boundary conductivity and segregation

In this study, a correlation between the conductivity, space charge layers, solute drag and non-Arrhenius grain growth in SrTiO3 was investigated. Strontium titante is known for its non-Arrhenius grain growth where grain growth rates decrease by orders of magnitude with increasing temperatures between 1350°C and 1425°C. Here, undoped SrTiO3 was sintered and annealed at temperatures below and above the grain growth transition. The influence of the annealing temperature and time on the conductivity and space charge layers at grain boundaries (GBs) in SrTiO3 was investigated by electrochemical impedance spectroscopy (EIS). STEM-EDS analysis indicates the presence of GBs with qualitative different cationic segregation in SrTiO3. A distortion of the GB semi-circle in the impedance plots was found which was attributed to the presence of multiple GB types with different electric properties and space charge layers. The presence of two GB types as indicated by impedance analysis corresponds well with previous results, where solute drag and segregation was supposed to cause the non-Arrhenius grain growth behavior of SrTiO3.

An Optimal Two-Dimensional Maintenance Policy for Self-Service Systems with Multi-Task Demands and Subject to Competing Sudden and Deterioration-Induced Failures

Self-service systems, such as electric vehicle charging piles (EVCPs), are typically deployed without on-site personnel. While frequent maintenance ensures high service revenue, it also leads to significant maintenance setup costs. Therefore, balancing service revenue and maintenance costs is essential for profit maximization. In this paper, we develop a maintenance policy optimization framework to maximize the profit rate of a fleet of self-service systems. First, we propose a maintenance policy that ensures sufficient functional systems while preventing high corrective maintenance costs. Next, we model the fleet's state transition process and its profit rate by characterizing two unique failure-induced demand-and-system interactions: demand switching and stepwise demand arrival rates, where the demands involve multiple tasks and systems are subject to multiple failure modes with non-constant occurrence rates. We develop a Tabu-search algorithm with random exploration to optimize the maintenance policy. Building on this, we investigate the impacts of key model parameters through a case study of thirteen EVCPs in Hong Kong and draw implications for profit maximization.

Automatic control algorithm for V2G frequency response mode of electric vehicle charging station

Abstract To solve the problem of excessive fluctuation of the V2G load of the electric vehicle charging pile, this paper proposes an automatic control algorithm for the V2G frequency response mode of the electric vehicle charging pile. The FPGA is used as the core processing unit to collect and process the V2G frequency data of the charging pile; the multiple linear regression model is used to predict the charging and discharging loads, and the automated two-layer control model is established; the sparrow search algorithm is used to optimize the weights and thresholds of the BP neural network to realize the control of the V2G frequency response mode. The experimental results show that the load prediction error of this method is small. After the automatic control, the load fluctuation of the charging pile is small, and the electric vehicle charging pile’s V2G frequency response control effect is better.

Hybrid regret-based p-robust and distributionally robust optimization models for electric vehicle charging station network design

Hybrid regret-based p-robust and distributionally robust optimization models for electric vehicle charging station network design

Biomass-Driven Polygeneration Coupled to Power-to-X: An Energy and Economic Comparison Between On-Site Electric Vehicle Charging and Hydrogen Production

The power-to-X strategy for passenger car applications offers a viable solution for using the surplus electrical power from renewable energy sources instead of exporting it to the grid. The innovative system proposed in this study allocates surplus electrical power from a building-integrated biomass-based Combined Cooling Heating and Power (CCHP) system to on-site applications and evaluates the energetic and economic benefits. The system comprises two key components: a 50 kW electric vehicle (EV) charging station for EVs and a 50 kW alkaline electrolyzer system for on-site hydrogen production, which is later dispensed to fuel cell electric vehicles (FCEVs). The primary goal is to decrease the surplus of electricity exports while simultaneously encouraging sustainable transportation. The system’s economic viability is assessed through two scenarios of fuel (e.g., biomass) supply costs (e.g., with and without fuel market costs) and compared to the conventional approach of exporting the excess power. The key findings of this work include a substantial reduction in surplus electricity exports, with only 3.7% allocated for EV charging and 31.5% for hydrogen production. The simple payback period (SPB) is notably reduced, enhancing economic viability. Sensitivity analysis identifies the optimal hydrogen system, featuring a 120 kW electrolyzer and a 37 kg daily hydrogen demand. The results underscore the importance of prioritizing self-consumed energy over exports to the national grid, thereby supporting integrated renewable energy solutions that enhance local energy utilization and promote sustainable transportation initiatives.

Numerical Investigation of G–V Measurements of metal – A Nitride GaAs junction

In this study, a Schottky diode consisting of Au/GaN/GaAs was fabricated using a radiofrequency nitrogen plasma source. The voltage-conductance characteristics (G/ω-V) of this diode structure were investigated at room temperature. To interpret the changes in the electrical properties of the nitrided GaAs-based Schottky structure, we developed a simulation program. This program employs a numerical model to calculate the G-V characteristics, allowing us to validate the experimental measurements conducted on the Schottky diodes. The geometric model used in our simulation considers not only the GaN layer formed between the metal and GaAs substrate but also the density and distribution of trapped states within the band gap. The program utilizes the numerical resolution of the Poisson and continuity equations to calculate the electrostatic potential and the concentrations of both n and p mobile carriers. These parameters are then used to determine the electric charge, current, capacitance, and conductance. The simulation results were subsequently compared to the experimental measurements to ensure their accuracy.

Effects of electric and magnetic fields in magnetic mixing in electroosmotic flows

In this paper, mixing efficiency in electroosmotic flow is numerically simulated at the electric double layer region in the presence of a magnetic field. An incompressible and laminar model is adopted to numerically solve the governing equations involving the magnetic field, electric potential fields, concentration distribution for positive and negative ions (Nernst–Planck), and species concentration. To validate the numerical study, an ideal electroosmotic flow with charged walls is simulated and the results are compared with the analytical solution. For the case with a Reynolds number of 0.02, microchannel length of 40 μm, the results show that by applying a magnetic field, the mixing efficiency along the microchannel length increases from 60.5% to 79.67%. It is found that the existence of a magnetic field in the electric double layer has a significant impact on pressure distribution along the microchannel wall, however, its effects on the electric fields (internal and external), the distribution of positive and negative ions, and the net electrical charge density are marginal. In addition, the presence of magnetic field creates two relatively large vortices inside the microchannel. The outcomes of the present study will help to improve mixing efficiency in micro-devices with applications in micro-analysis systems and lab-on-a-chip instruments.

Insights into the ternary substitution Na2.96+xK0.04 V2-xNix(PO4)2.98F0.06 with superior rate capability and near-zero strain property

Na3V2(PO4)3 (NVP) is considered the most favorable cathode for sodium-ion batteries due to its solid NASICON skeleton. Nevertheless, poor intrinsic conductivity is insufficient for its further commercialization. In this paper, we present a promising K/Ni/F co-doped and carbon nanotubes (CNTs) encapsulated Na2.96+xK0.04 V2-xNix(PO4)2.98F0.06 (KNF@CNTsx) cathode materials. The synthesis of KNF@CNTsx is achieved using a facile sol-gel method. The replacement of Na+ with K+ broadens the migratory pathway and supports the crystal skeleton, while F− substitution reduces the average grain dimension, thereby increasing the diffusion rate of Na+. Furthermore, the reduction of resistance by Ni2+ doping enables optimization of the transport of electrical charge, while also facilitating the optimization of chemically-defined properties. Concurrently, the optimal quantity of carbon capping and wrapping of CNTs facilitate the formation of efficacious conductive network, which diminishes the size of grains and shortens the pathway for the diffusion of Na+. This network could also serve as a buffer against crystal deformation. In conclusion, the modified KNF@CNTsx samples exhibit notable enhancements in their conductivity, ion diffusion capacity, and structural stability. Among them, the KNF@CNTs0.04 sample exhibits a capacity for reversibility of 93.3 mAh g−1 at 50C, with a remaining capacity of 68.5 mAh g−1 after 4000 successive long charge-discharge cycles. Ex-situ electrochemical XRD demonstrates the V3+/V4+ redox reaction occurs accompanied by the phase transfer reaction, during which Na+ prefers to diffuse along c-axial in the crustal structure. Moreover, volume alteration of this KNF@CNTs0.04 cellular entity is miniscule and reversible during the charge-discharge process, further confirming the stabilized construction and near-zero strain property. The after-cycled XRD/SEM/XPS certify the improved chemical and structural stability of KNF@CNTs0.04, indicating the constructive effects of K/Ni/F ternary substitution.