Battery Storage Capacity Research Articles

ОЦІНКА НЕРІВНОМІРНОСТІ ВІДБОРУ ЕЛЕКТРОЕНЕРГІЇ В СИСТЕМАХ РОЗПОДІЛУ ТА ВИКОРИСТАННЯ ЕЛЕКТРОЕНЕРГІЇ З АКУМУЛЯТОРНИМИ БАТАРЕЯМИ

The peculiarities of calculating the energy capacity of storage batteries in systems of distribution and use of electricity with time-varying loads at different intensities of their discharge are considered. It is shown that the assessment of the unevenness of electricity selection in systems with storage batteries can be made due to the use of such an integral indicator as the modified Friese power, which is considered an indicator of the operation of a specific element in the system (assessment of the overall system energy efficiency). The dependencies of the unevenness of the battery discharge are obtained in the form of a dimensionless function, which allows you to get a visual representation of the assessment of the level of unevenness of electricity consumption by loads in three-dimensional space depending on the duration of the intervals in which the power draw is constant, and the intensity of the battery discharge during these intervals. An integral characteristic of the assessment of unevenness of the battery discharge, which is an indicator of the energy efficiency of the battery as an element of the system, is introduced. References 19, Figures 4, Table 1.

Performance Ratio of Photovoltaic Energy Sources in the Application of E-bike Charging Station

Solar energy converted by the use of photovoltaic modules, requires testing the performance of the conversion results, to determine the efficiency and performance ratio, when unloaded and loaded. The implementation of this photovoltaic energy source, the researcher aims to use the load of the e-bike at the e-bike charging station with a peak power capacity of 200 Wp PV and a battery storage capacity of 200 Ah 12 V. The test results obtained that PV provides 69% of the load power needs with a peak power of 98.67W at 12.25 WIB, while the battery supplies 31%, with a total PV energy of 0.68 kWh, and the Performance Ratio (PR) of solar panels was obtained at 85.7%. The load of the e-bike itself absorbs 437.6 Wh of energy from SoC charging (62%-100%) with a charging duration of 3.42 hours, where the charging of the e-bike in planning requires 576 Wh of energy for 4.5 hours from the SoC (50%-100%).

Fleet Control With IoT Using TLS Certificates And SIM7000G GPS Device

Companies that apply technology to monitor mobile units face several challenges such as obtaining the best precision in geolocation coordinates, having good communication quality and security in data transmission, detecting route deviations, avoiding delays in their journeys, among others. This article proposes a real-time transport fleet monitoring system that uses the Internet of Things (IoT) to contribute to some of these challenges and improve the overall user experience of the collected data. A model is presented that includes a proposal to reinforce security in data transit with MQTT using Transport Layer Security (TLS) and a study carried out with the purpose of detecting which GPS coordinate acquisition device is more precise. 368 tests were carried out in five different coordinates with SIM800, ESP32, SIM7000G considering altitude, latitude, longitude, speed, number of satellites and sending time. The device that provided the best results in coordinate accuracy was the SIM7000G, even meeting the requirements for battery life and storage capacity due to the demand required to execute TLS security certificates.

Improving efficiency of wireless charging system in electric vehicle using a hybrid ultracapacitor-battery energy storage approach

This research aims to enhance the efficiency of wireless charging systems in electric vehicles by integrating a hybrid ultracapacitor-battery energy storage solution. Traditional standalone battery-based energy storage systems in wireless charging often face sub-optimal charging efficiency, resulting in extended charging times and reduced energy transfer efficiency. To address this limitation, we propose a hybrid approach that combines the rapid charging capability of ultracapacitor (supercapacitor) with the long-term storage capacity of batteries. The optimal charging range is 0 cm to 2 cm, and the combined output voltage and current are 5 V to 12 V and 0.63 A, respectively. This hybrid energy storage system will significantly boost electric vehicles (EVs) charging efficiency. Our research involves experimental evaluation and data analysis to assess crucial parameters, including charging efficiency, energy transfer efficiency, and charging time. The experimental results are validated and compared against existing battery-only systems, shedding light on the advantages and limitations of the hybrid approach. This study contributes to the optimization of wireless charging systems, enhancing energy transfer efficiency, and promoting the broader adoption of wireless charging technology in electric vehicles.

A comprehensive power management strategy for the effective sizing of a PV hybrid renewable energy system with battery and H2 storage

In the present work a detailed Power Management Strategy (PMS) of a Photovoltaic Hybrid Renewable Energy System (PV-HRES) with battery, H2 storage/re-electrification, and diesel generator (DG) back-up has been developed. The PV-HRES was regarded to employ commercial PV modules, battery cells, and power electronics, as well as commercial alkaline electrolyzer (EL) and PEM fuel cell (FC), both regarded to variably operate following the fluctuations of PV-power excesses/shortages. The scope was to demonstrate the operability of the PMS and to utilize the PMS for the rational sizing of the PV array, the batteries' bank and the hydrogen storage capacity, for a given load and a solar irradiation annual profile. The annual operation of the PV-HRES was simulated for 440 combinations of PV-arrays and battery/H2 storage capacities, with constant EL and FC capacity, aiming for: (i) exclusively renewable power generation, (ii) rational PV-array sizing and (iii) annually balanced H2 generation/consumption. The operation algorithm was based upon the battery's State of Energy (SOE) variation between 20 and 95 % of their nominal energy capacity (BNEC). Below 20 %, the PV-energy deficits were covered by the FC, in case of sufficient stored H2, whereas beyond 95 %, energy excesses were converted to H2, in case of H2 storage volume availability. Increased PV-arrays and batteries led to the exhaustion of H2 storage volume and to significant dumped renewable energy waste (up to 23 % of the PV generation), whereas decreased PVs and battery capacities led to H2 exhaustion, throughout the annual operation. Out of the 440 examined combinations, 40 were found to simultaneously: i) nullify the DG generation, for exclusively renewable energy generation, ii) prevent both battery and H2 storage volume exhaustion, thus eliminating excessive energy wastes and rationalizing the PV-array size, and iii) balancing the annual hydrogen generation/consumption. To fulfill the 1.48 kW average load demand, the analysis pointed out an optimum PV-HRES of 10.23 kWp nominal PV power, 120 kWh battery storage capacity, and 4 m3 H2 storage volume, at 100 bars maximum pressure; for which the annual PV generation exceeded by 25.8 % the annual load demand.

Optimal scheduling of smart home appliances with a stochastic power outage: A two-stage stochastic programming approach

This study primarily concentrates on enhancing the scheduling of electric appliances within a smart home equipped with a photovoltaic solar array and a storage battery capable of redistributing excess electricity to the grid. Furthermore, the investigation takes into consideration the unpredictability of a power outage, analyzing the scheduling of these appliances to minimize consumer electricity expenses. To tackle this issue, a two-stage stochastic programming methodology is developed to effectively model the uncertainty surrounding both the onset time and duration of power outages. Additionally, a sample average approximation algorithm (SAA) is devised to efficiently address the problem. Through extensive computational experiments, the outcomes demonstrate that the SAA yields shorter CPU processing times, albeit without guaranteeing optimal solutions. The total average percent deviation of the SAA's upper bound from its lower bound and the optimal solution stands at 4.16 % and 4.5 %, respectively. Moreover, it is demonstrated that utilizing the stochastic approach, as opposed to the deterministic one, can enhance solution quality by 6.2 %. Furthermore, a comprehensive sensitivity analysis is provided, focusing on probability distribution functions of outage start time and duration, alongside an analysis of the solar panel and battery storage capacities. It is revealed that when adopting a Normal distribution instead of a Uniform distribution, the performance of the SAA experiences a slight decline.

Fault tolerant & priority basis task offloading and scheduling model for IoT logistics

The Internet of Things (IoT) has connected millions of devices to the internet for communication and computation purposes. Due to constraints such as limited battery power, processing capabilities, and storage capacity, IoT devices often rely on remote fog nodes for task execution, a practice known as task offloading. The NP-hard nature of offloading and scheduling IoT tasks onto fog nodes poses significant challenges. Current IoT task offloading often relies on single parameters like device MIPS, RAM, or battery power. Similarly, task allocation on fog nodes frequently prioritizes VM MIPS. To address energy consumption and task failures in IoT systems, robust fault tolerance, efficient task offloading, and effective scheduling are crucial. This paper introduces a novel Fault-Tolerant, Priority-Based Task Offloading and Scheduling Model (FP-TOSM) for IoT logistics. Utilizing the Analytic Hierarchy Process (AHP) for multi-criteria decision-making, IoT tasks are prioritized. Tasks below a specified threshold are executed locally, while those within a defined range are offloaded to a fog node. Tasks exceeding this range are delegated to the cloud. The Euclidean formula is used to determine the proximity between the IoT logistics vehicle and fog nodes for offloading decisions. Dynamic re-clustering fault tolerance mechanisms are utilized to manage task failures after offloading. Successful offloaded tasks are allocated to suitable VMs in the cloud and fog nodes. The proposed strategy selects the most suitable VM for offloaded tasks using a multi-criteria decision-making process to reduce energy consumption, SLA violations, and execution costs. The model's performance is evaluated through simulations in iFogSim2. Results demonstrate reductions in energy consumption by up to 5.8 % and 10.4 %, decreases in SLA violations by up to 25.2 %, and a 16.28 % improvement in response time compared to an ACO-based model with a response time of 17.8 %. Additionally, the task failure ratio shows a significant reduction of up to 22.1 %. These findings highlight the effectiveness of FP-TOSM in enhancing efficiency and reliability within fog computing environments.

Resource Adequacy and Integration of Renewables in Light of US, EU, and Pakistan’s Evolving Power Sector

This study investigates resource adequacy and renewable energy integration in the United States, European Union, and Pakistan amid global energy market liberalization and greenhouse gas reduction efforts. It explores how these regions are adapting to the surge in renewable sources like wind and solar, which, despite their financial and environmental benefits, challenge resource adequacy and the economic viability of traditional energy sources. In the US and EU, significant improvements have been introduced in wholesale electricity markets and capacity accreditation mechanisms, which enhanced the large-scale deployment of renewables. This shift has prompted a reevaluation of resource adequacy, leading to the increased deployment of battery storage and demand response. Presently, gas-based generation is largely upholding resource adequacy; however, future trends indicate a move towards greater consumer participation, energy efficiency, and utility-scale storage, with a decline in fossil fuel use. Pakistan aims to adopt a liberalized market structure by balancing competitive markets with legacy contracts. Public pressure is driving a shift from costly fossil-based generation to renewables. Similarly, a trend in the rise of behind-the-meter solar generation can be witnessed. In the future, Pakistan may also experience resource adequacy challenges. It will likely need to implement battery storage, demand response, and modern capacity accreditation tools, by drawing lessons from developed markets.

Facile synthesis of hollow Zn-Co-S and Zn-Co-P nanostructures supported on Ti3C2Tx MXene nanosheets for high-performance lithium-ion capacitors

Lithium-ion capacitors (LICs) represent a novel energy storage solution, merging the high energy storage capacity of lithium-ion batteries with the adequate power density of supercapacitors. However, the electrochemical performance of lithium-ion capacitors is notably constrained by the inherent imbalance between the charge storage mechanisms and electrochemical reaction dynamics of their anodes and cathodes. In this study, a dual-shell hollow structured ZnCo2S4 was prepared via a two-step hydrothermal method and compared with spherical ZnCo2P4 prepared by calcination. Subsequently, they were respectively anchored onto two-dimensional Ti3C2Tx nanosheets, which possess abundant surface functional groups, through electrostatic adsorption, forming layered semi-encapsulated framework structures. The introduction of MXene significantly enhances the conductivity of the material, restricts the expansion of ZnCo2S4 and ZnCo2P4 during the Li+ insertion/extraction process, and inhibits their aggregation. Under a three-electrode configuration, ZnCo2S4/Ti3C2Tx exhibits excellent rate performance (1004 C·g−1 at 10 A·g−1) and cycling stability (95 % capacity retention after 5000 cycles at 2 A·g−1). The discharge capacity of the ZnCo2S4/Ti3C2Tx LIB is up to 1360 mAh·g−1 at 0.1 A·g−1, and the discharge capacity of the ZnCo2P4/Ti3C2Tx LIB is 1121 mAh·g−1 at 0.1 A·g−1. As an anode material for LIC, ZnCo2S4/Ti3C2Tx demonstrates excellent specific capacitance and rate performance (105 F·g−1 at 0.5 A·g−1). At a power density of 337.10 W·kg−1, ZnCo2S4/Ti3C2Tx//AC LIC exhibits a significant energy density of 136.25 Wh·kg−1. At a current density of 2 A·g−1, ZnCo2S4/Ti3C2Tx//AC LIC retains 89 % of its capacity after 5000 charge-discharge cycles, demonstrating excellent stability for practical applications. ZnCo2P4/Ti3C2Tx//AC LIC exhibits an energy density of 45.20 Wh·kg−1 at a power density of 623.07 W·kg−1, and after 5000 cycles at 2 A·g−1, its capacity retention rate reaches 80 %. These findings suggest that ZnCo2S4/Ti3C2Tx and ZnCo2P4/Ti3C2Tx composites are promising anode materials for LICs.

Optimizing and investigating the charging time of phase change materials in a compact-latent heat storage using pareto front analysis, artificial neural networks, and numerical simulations

There's a growing emphasis on adopting eco-friendly energy sources to mitigate greenhouse gas emissions and foster sustainability. While renewable energy sources like solar power offer numerous benefits, they have some limitations. Additionally, storing electricity generated from solar panels can be costly and challenging due to limitations in battery technology and storage capacity. Therefore, phase change material-based thermal energy storage systems offer a promising solution to these challenges. These systems also play a crucial role in enhancing buildings' energy efficiency and sustainability. The dimensionality of these systems affects their performance. Traditional systems often rely on fixed dimensions, leading to non-uniform thermal conditions within the storage medium. To address these challenges, adopting a compact-latent heat storage (C-LHS) mechanism was recommended herein. Besides, some fins were inserted into the C-LHS system to alter the heat transfer dynamics within the device. Three of the critical parameters of the fins were changed to examine their impact on the charging time. Artificial neural networks and genetic algorithms were employed to determine the optimal positioning of fins to minimize the duration of material to get both part (melt fraction of 0.8) and full (melt fraction of 1) melting capacities. Here, the primary aim was to present a predictive model capable of accurately forecasting the charging time of the material. In all the presented finned samples, the entire PCM melted in less than 15,110 s (4 h and 12 min) and was able to fully utilize the energy absorption capacity in latent form, whereas in the non-finned system, only 68.6 % of the material managed to melt within the entire duration of the investigation (5 h). After analyzing the data, two optimal configurations of OS1 and OS2 with the minimum time for melt fractions of 0.8 and 1 were introduced. In the OS2 configuration, it took 16,200 s (4 h and 30 min) to absorb 4929 kJ of energy. The non-finned sample absorbed only 3250 kJ of energy during the same period. In General, OS2 achieved total energy absorption 51.6 % faster than the non-finned sample. Therefore, the introduced system has the potential to increase absorbed energy in the rest of the daylight hours to further amounts by increasing the number of units and employing a larger volume of material.

Physical and electrochemical performances of novel tellurium composite films as anode applications in energy storage systems

Portable electronic devices and electric cars use lithium-ion batteries, but clumping lithium alloys limit their lifespan. Due to their strong electronic conductivity, volumetric capacity, and high energy density, researchers are conducting research on electrochemical metal cells utilizing tellurium for high-performance batteries. Theoretically, lithium-tellurium batteries can improve energy densities three times more than lithium-ion batteries. However, metal-tellurium faces challenges such as low rate capability, unclear redox reactions, intermediate dissolution, and electrode volume changes. This study explores the enhancement of the energy storage capacity of next-generation batteries by fabricating coated electrode films as novel anodes from tellurium, silicon, and graphene. Physical, thermal, and morphological analysis of composite material are investigated by XRD, TEM, TGA, DSC, SEM, UV, and XPS analyses, revealing its rigidity as well as durability through its crystal structure alignment and thermal stability. In electrochemical analysis (CV) at various scan rates, samples that exhibit consistent and high specific capacity (Cp) values at different scan speeds (25, 50, and 100 mV/s) indicate excellent ability to store and maintain charge. Decreasing Cp values with increasing scan rates indicate that the speed of cycling limits the charge transfer kinetics and electrode performance. In EIS, the charge transfer resistances (Rct) for the pure Te, Te + Gr, Te + Si + Gr, and Te + Si samples are 759.07 Ω, 4.21 Ω, 36.39 Ω, and 164.90 Ω, respectively. The Te + Gr sample has the lowest Rct, indicating the best charge transfer efficiency at the electrode contact, whereas the Te + Si + Gr sample has a comparatively lower Rct, indicating better charge transfer kinetics. The combined result exhibits the synergistic impact of tellurium, silicon, and graphene in enhancing the energy storage capacity of future batteries across the industry.

Multiple energy planning in the energy hub considering renewable sources, electric vehicles and management in the daily electricity market with wind multi-objective optimization algorithm

This article presents a decision-making framework leveraging randomness to manage short-term intelligent energy hub (EH) within regenerated power systems integrating EH input electricity and natural gas. EH outputs, addressing electricity and heat needs, are managed via battery storage systems (BSS) and electric vehicle (EV) fleets in regulation markets. The energy hub operator optimizes decisions concerning natural gas and energy, minimizing costs in electricity supply and thermal energy carriers across day-ahead (DA) markets and regulations. Emphasizing the benefits of demand-side management planning in cost reduction, the model incorporates a conditional value at risk (CVaR) method to assess and manage uncertainties. Moreover, leveraging electric vehicle battery storage capacity is explored to mitigate wind and solar energy production uncertainties. An energy optimization strategy based on a Multi-Objective Wind-Driven Optimization (MOWDO) is proposed, demonstrating the effectiveness of the model and method across various system conditions and scenarios. Results indicate that increasing the number of EVs from 2000 to 10,000 reduces the expected cost by 1.5 % and the CVaR by 1.5 %. Additionally, optimizing the BSS discharge cost coefficient from $45/MWh to $15/MWh decreases the expected cost by 0.5 % and the CVaR by 0.3 %. The system achieves a 20 % reduction in peak-hour electricity purchases by leveraging the BSS and EV fleet during high price conditions. These findings underscore the positive impact of demand-side management programs in enhancing renewable resource utilization amidst uncertainty.

Wormhole attack detection and mitigation model for Internet of Things and WSN using machine learning.

The Internet of Things (IoT) is revolutionizing diverse sectors like business, healthcare, and the military, but its widespread adoption has also led to significant security challenges. IoT networks, in particular, face increasing vulnerabilities due to the rapid proliferation of connected devices within smart infrastructures. Wireless sensor networks (WSNs) comprise software, gateways, and small sensors that wirelessly transmit and receive data. WSNs consist of two types of nodes: generic nodes with sensing capabilities and gateway nodes that manage data routing. These sensor nodes operate under constraints of limited battery power, storage capacity, and processing capabilities, exposing them to various threats, including wormhole attacks. This study focuses on detecting wormhole attacks by analyzing the connectivity details of network nodes. Machine learning (ML) techniques are proposed as effective solutions to address these modern challenges in wormhole attack detection within sensor networks. The base station employs two ML models, a support vector machine (SVM) and a deep neural network (DNN), to classify traffic data and identify malicious nodes in the network. The effectiveness of these algorithms is validated using traffic generated by the NS3.37 simulator and tested against real-world scenarios. Evaluation metrics such as average recall, false positive rates, latency, end-to-end delay, response time, throughput, energy consumption, and CPU utilization are used to assess the performance of the proposed models. Results indicate that the proposed model outperforms existing methods in terms of efficacy and efficiency.

Aqueous ammonium-ion battery capacity boosted with electrode preparation

Orbital electron tuning and crystal engineering improves the energy storage capacity of non-flammable batteries.

(Invited) Revolving the Mechanical Stress in Batteries by Using Microcantilever

Although the generation of mechanical stress in the anode material is suggested as a possible reason for electrode degradation and fading of storage capacity in batteries, only limited knowledge of the electrode stress and its evolution is available at present. Here, we show real-time monitoring of the interfacial stress of a few-layer MoS2 system under the sodiation/desodiation process using microcantilever electrodes. During the first sodiation with a voltage plateau of 1.0 to 0.85 V, the MoS2 exhibits a compressive stress (2.1 Nm−1), which is substantially smaller than that measured (9.8 Nm−1) during subsequent plateaus at 0.85 to 0.4 V due to the differential volume expansion of the MoS2 film. The conversion reaction to Mo below 0.1 V generates an anomalous compressive stress of 43 Nm−1 with detrimental effects. These results also suggest the existence of a separate discharge stage between 0.6 and 0.1 V, where the generated stress is only approximately one-third of that observed below 0.1 V. This approach can be adapted to help resolve the localized stress in a wide range of electrode materials, to gain additional insights into mechanical effects of charge storage, and for long-lifetime battery design.

Research on the capacity of charging stations based on queuing theory and energy storage scheduling optimization sharing strategy

The rapid development of electric vehicles (EVs) has led to the continuous expansion of charging infrastructure, but it has also resulted in the low utilization of urban charging stations. Numerous cities have made the decision to open to the public electric bus (EB) charging stations for EVs in an effort to address the issue of charging difficulty for EVs in urban areas and increase the use of charging stations. However, when EB charging stations are open to the public, they inevitably create a load on the power grid during peak hours, and also increase the queuing time for EV users. To address these issues, this paper proposes an operational model where EVs can use the EB charging station from 6:00 AM to 8:00 PM daily, while EBs can charge at other times. In order to reduce grid load during periods of peak electricity demand and lower electricity costs, the model makes use of energy storage facilities to charge during off-peak hours and discharge during peak hours. Queue times are also decreased by optimizing the number of chargers using the M/M/s/K queuing model. The research results indicate that during peak hours at the charging station, the probability of electricity consumption exceeding the storage battery's capacity is only 3.562 %. After five years of operation, the charging station has saved 5.6610 % on electricity costs. Taking the K1 bus route in Jinan, Shandong Province as a case study, it was found that the optimal configuration involves 22 chargers. This operational model and energy storage strategy provide a feasible solution for EB charging stations, contributing positively to the sustainable operation of charging stations.

Construction of Bi/Bi2O3 particles embedded in carbon sheets for boosting the storage capacity of potassium-ion batteries

Bismuth-based materials have attracted interest in potassium-ion batteries (PIBs). However, the large volume expansion prevents further use of bismuth-based materials for potassium storage. This work employs a two-step synthesis method to innovatively synthesize of Bi/Bi2O3 nanoparticles assembled on N-doped porous carbon sheets (Bi/Bi2O3@CN). The layered structures with uniformly shaped and N-doped porous carbon skeleton buffer the expansion of Bi and the Bi/Bi2O3 particles increase the capacity of potassium storage. In brief, the Bi/Bi2O3@CN served as anode in half-cell of PIBs have a good rate capacity of more than 234.7 mAh/g at 20 A/g. The specific capacity retention was 73 % compared with 322.16 mAh/g at 1 A/g, demonstrating good holding capacity for diverse current densities. The cycle also displays 163 mAh/g after 1500 cycles at 2 A/g in the KPF6 metal salt solution, showing its potential as one of the anode materials in PIBs.

Off-Grid Emergency Tent Solutions with a Compact Solar Photovoltaic System

In recent years, solar photovoltaic (PV) systems have seen increased utilization for powering a diverse range of applications, from residentials and buildings to off-grid installations, highlighting their growing importance in the global transition towards sustainable development using renewable energy sources. This paper proposes the development of a compact solar PV system for emergency tent systems to aid preparations and rapid response efforts during disasters and unforeseen events. The system aims to support local authorities and communities in mitigating disaster risks due to power outages. The design and implementation of an emergency tent prototype measuring 3´3 m2, with a power consumption of 0.36 kW/day using a 30 W solar PV panel and battery storage capacity of 40 Ah. The system is sufficient for powering the basic functions of dc bulbs and fans as the primary load.

Investigation of the usage of carbon-based two dimensional materials in lithium sulfide (Li–S) batteries

After the synthesis of graphene (Gr), the other two-dimensional (2D) allotropes of carbon based materials have been predicted theoretically or synthesized by experimentally. Two of these nanomaterials are graphyne (Gy) and graphdiyne (Gdy) which are close relatives of Gr and can be synthesized in bulk. While carbon atoms in Gr bonds with sp2 bonds, Gy and Gdy comprise sp and sp2 hybridized carbon atoms with high degrees of π conjugation that features uniformly distributed pores. So, these intrinsic nanopores add distinction to Gy and Gdy with respect to the Gr especially for energy storage applications. Nowadays, the researchers are studying to improve the storage capacity of Lithium-ion batteries (LIBs). However, Lithium–Sulfur (Li–S) batteries can store approximately five times more energy in terms of energy density compared to LIBs. In this study, we have systematically investigated the lithium polysulfides (LixSy; 1≤x≤2,1≤ y ≤ 8) adsorbed on Gr, Gy and Gdy monolayers by means of density functional theory (DFT) calculations. Our results reveal that, the presence of sp bonds and the size of nanopores in the structure extremely affect the adsorption energy of small sized LixSy clusters. However, the adsorption energies obtained with increasing the size of LixSy clusters are almost similar in Gr, Gy and Gdy monolayers. The diffusion barrier energy values obtained for LixSy clusters indicate that the migration of clusters on Gr may be easy but it is hard on Gdy and especially on Gy due to the pore networks in the structures acting as traps to anchor the LixSy clusters. In addition, we have investigated the double adsorption of LixSy clusters on Gr, Gy and Gdy structures to search the shuttle effect. It can be reported that LixSy clusters do not bind each others and do not create long polysulfide chains, which is not requested.

Optimal Capacity and Charging Scheduling of Battery Storage through Forecasting of Photovoltaic Power Production and Electric Vehicle Charging Demand with Deep Learning Models

The transition from internal combustion engine vehicles to electric vehicles (EVs) is gaining momentum due to their significant environmental and economic benefits. This study addresses the challenges of integrating renewable energy sources, particularly solar power, into EV charging infrastructures by using deep learning models to predict photovoltaic (PV) power generation and EV charging demand. The study determines the optimal battery energy storage capacity and charging schedule based on the prediction result and actual data. A dataset of a 15 kWp rooftop PV system and simulated EV charging data are used. The results show that simple RNNs are most effective at predicting PV power due to their adept handling of simple patterns, while bidirectional LSTMs excel at predicting EV charging demand by capturing complex dynamics. The study also identifies an optimal battery storage capacity that will balance the use of the grid and surplus solar power through strategic charging scheduling, thereby improving the sustainability and efficiency of solar energy in EV charging infrastructures. This research highlights the potential for integrating renewable energy sources with advanced energy storage solutions to support the growing electric vehicle infrastructure.