Optimal Charging Research Articles

Effect of source and drain electrode positions on the performance of bottom gate organic thin film transistor

In recent years several novel technologies have emerged on commercial level. Organic electronics is one such technology that has created new avenues for the development of flexible, robust, and cost-effective electronic devices. Transistor developed using organic materials are called organic thin film transistors (OTFT). Transistor is elementary component of any electronic device, any variation in the size or performance of the transistors reflects directly on the overall performance of the electronic devices. Bottom gate bottom contact (BGBC) OTFT is commonly employed single gate organic transistor owing to its simple architecture and easy fabrications. Since in organic semiconductor material carrier mobility of charges is lower, hence its overall performance declines. As a result, its application in electronic circuits becomes limited. Therefore, to enhance the performance of BGBC OTFT and facilitate the optimal charge mobilisation inside organic semiconductor layer i.e., OSC, improvement in the architecture of BGBC is deemed necessary. In this research article the performance of BGBC OTFT is analysed for different positions of electrodes viz. source and drain embedded inside the OSC layer. The study signifies the effect of electrode positions on performance optimization of BGBC OTFT without changing the materials, dimensions, or the operating voltage regime. It has been found that for 30 nm thick OSC layer, the electrodes should ideally be placed between 5 nm to 10 nm above the junction of OSC and dielectric layer inside OSC layer for optimal results.

An advanced solid-state RF power source maximizing energy efficiency for optimal superconducting RF cavity charging

Abstract This paper outlines an experimental demonstration of an envelope tracking (ET) technique applied to a kilowatt-level single-ended solid-state power amplifier (SSPA), aimed at enhancing the charging efficiency of superconducting radio frequency (SRF) cavities by reducing reflection power while maintaining a high degree of efficiency. The technique is particularly designed for the pulsed operation of the European Spallation Source (ESS) at a nominal frequency of 352 MHz, with a 5% duty cycle and a pulse width of 3.5 ms. The study introduces an optimal charging scheme using a solid-state-based amplifier to maintain high efficiency, allowing for power ramp-up while minimizing reflections from SRF cavities and optimizing SSPA efficiency. A fast envelope tracking power supply (ETPS) system is implemented for the approximately 300 ms charging time required by the SRF cavities at ESS. The ETPS system, demonstrated on a single module as a proof-of-concept with scalability potential to a 400 kW power station, indicates an overall average efficiency of 70% and a 24% energy saving over traditional vacuum-tube based amplifiers. This demonstrates the ET technique’s effectiveness at the kilowatt level for efficient SRF cavity charging with reduced reflection, offering significant efficiency and energy savings.

An urban charging load forecasting model based on trip chain model for private passenger electric vehicles: A case study in Beijing

The rapid adoption of electric vehicles (EVs) has led to dramatic increase in charging demands that poses great challenges for efficient charging infrastructure rollout and operation. It is crucial to accurately assess charging demand in urban areas to optimize the siting and sizing of charging infrastructure. This paper proposes a novel urban charging load forecasting model for private passenger EVs based on massive operating data of EVs in Beijing. First, the characteristics of travel patterns for private passenger EVs, urban road network, functional area distribution and charging infrastructure distribution within the entire Beijing area are identified. Then a charging load forecasting model that can simultaneously simulate trip chains for EVs is constructed by considering the occupancy states of public charging piles and the interactions among different EVs. Finally, the effectiveness of the proposed charging load forecasting model is verified based on comprehensive test data. Our findings imply that the number of EVs at recharge and the charging power can be reliably predicted with the accuracy of over 84.73 % and 81.92 %, respectively. It provides the foundation for optimal charging infrastructure planning and charging scheduling.

An improved deep Q-network approach for charging sequence scheduling with optimal mobile charging cost and charging efficiency in wireless rechargeable sensor networks

Mobile charging is a practical solution to overcome energy constraints in wireless rechargeable sensor networks (WRSNs). It utilizes a mobile charger (MC) to charge sensors based on the wireless power transfer technique via a charging scheduling scheme. However, how to schedule MC to achieve higher charging efficiency with less mobile charging cost, while ensuring network functionality remains a challenge. Specifically, the mobile charging cost is the total energy consumed by the MC during the charging task. This paper studies the charging sequence scheduling problem with optimal mobile charging cost and charging efficiency (CSCE), considering dynamic changes in sensor energy consumption. Moreover, we model the CSCE as a multi-objective mixed integer nonlinear programming. To address this issue, we propose an improved deep Q network approach for CSCE (IDQN-CSCE), where the MC acts as an agent to explore the WRSN and determine a charging policy based on the charging demand of sensors. IDQN-CSCE employs Q-learning and deep Q-networks (DQN) to train the Q-table and DQN network simultaneously. Charging actions are selected based on probability, and the probability of using Q-learning decreases as iteration steps increase. Additionally, we design a novel reward function consisting of a weighted sum of rewards and penalties fed back by the environment after performing a charging action. Simulation results show that IDQN-CSCE achieves higher charging efficiency than other baseline approaches with less mobile charging cost.

Optimizing the Journey: Dynamic Charging Strategies for Battery Electric Trucks in Long-Haul Transport

Battery electric trucks (BETs) represent a well-suited option for decarbonizing road freight transport to achieve climate targets in the European Union. However, lower ranges than the daily distance of up to 700 km make charging stops mandatory. This paper presents an online algorithm for optimal dynamic charging strategies for long-haul BET based on a dynamic programming approach. In several case studies, we investigate the advantages optimal strategies can bring compared to driver decisions. We further show which charging infrastructure characteristics in terms of charging power, density, and charging station availability should be achieved for BETs in long-haul applications to keep the additional time required for charging stops low. In doing so, we consider the dynamic handling of occupied charging stations for the first time in the context of BET. Our findings show that, compared to driver decisions, optimal charging strategies can reduce the time loss by half compared to diesel trucks. To keep the time loss compared to a diesel truck below 30 min a day, a BET with a 500 kWh battery would need a charging point every 50 km on average, a distributed charging power between 700 and 1500 kW, and an average charger availability above 75%. The presented method and the case studies’ results’ plausibility are interpreted within a comprehensive sensitivity analysis and subsequently discussed in detail. Finally, we transformed our findings into concrete recommendations for action for the efficient rollout of BETs in long-haul applications.

Hybrid Charging Station for Authentic Electric Vehicle

Electric vehicles powered by batteries are becoming increasingly popular around the world. Several causes are driving this trend, including the need to reduce air and noise pollution and reliance on fossil fuels. To have a better understanding of how batteries behave in various situations. It is vital to be aware of certain battery performance factors in certain circumstances. A battery management system includes a battery fuel gauge, an optimal charging algorithm, and circuitry for cell and thermal balance. It estimates essential states and parameters of the battery system, such as battery impedance, battery capacity, state of charge, state of health, power decline, and remaining useful life, using three non-invasive measures from the battery: voltage, current, and temperature. This paper reviews several papers published regarding EV charging types, methods, BMS, state of charge

Energy management of hybrid energy storage system in electric vehicle using hybrid methodology

ABSTRACT Electric vehicles operate in a dynamic environment with constantly changing driving conditions, such as varying speeds, terrains, and traffic patterns. Adapting an energy management (EM) strategy to these conditions to maximise efficiency is a significant challenge. Achieving optimal energy management must also consider the cost implications. This manuscript proposes a hybrid technique for the optimum charging capability of electric vehicles (EVs) with a hybrid energy storage system (HESS), such as an electric vehicle, battery, and supercapacitor (SC). The objective is to maximise energy utilisation efficiency, extend the lifespan of energy storage devices, improve overall vehicle performance, and minimise charging costs. The WSO is incorporated to generate the dataset of possible input parameters of HESS. Using the accomplished WSO dataset, the SNN is trained and forecasts the optimum input parameters of Hybrid ESS. The proposed method is implemented in the MATLAB platform and compared with existing methods. The proposed method supercapacitor power is 4.3 × 104W, proposed battery power is 7 × 104 W, supercapacitor current is 90 A and the proposed battery current is 20 A. The proposed method is higher than other existing methods like RFA, WHO and DCNN methods.

Thermo-economic multi-objective optimization of the liquid air energy storage system

Liquid Air Energy Storage (LAES) is a promising energy storage technology for large-scale application in future energy systems with a higher renewable penetration. However, most studies focused on the thermodynamic analysis of LAES, few studies on thermo-economic optimization of LAES have been reported so far. Thus, this work focuses on the multi-objective optimization of LAES by using non-dominated sorting genetic algorithm (NSGA-II), taking the round trip efficiency (RTE) and economic indicators as the optimization objectives. Three major results were obtained from this work. First, the NSGA-II-based optimization framework is able to determine the optimal design and operational parameters of LAES under different configurations and scenarios, including the optimal charging and discharging pressure, heat transfer areas, and mass flow rates of hot and cold storage media etc. It is shown that the increase of energy efficiency by 9 % ~ 14 % and a decrease of exergy destruction by 16 % were seen. Secondly, the design and operational guidelines of LAES can be derived from the optimization. The optimal charging pressure was found to vary between 13 and 15 MPa, and the optimal discharging pressure is between 9 and 12 MPa, depending on the machines' efficiencies and pinch points of heat exchangers of a LAES system. Finally, the Pareto Fronts of capital expenditure, energy efficiency and the occupied space energy density of a LAES system provide system operators good operational and investment advice. Specifically, the RTE of the LAES increasing by 1 % is at the cost of increasing the capital cost by 0.5–1 %. In this study, if the investment budget is over 48 M£, a LAES system with three-stage compressors and four-stage turbines presents better trade-off between RTE and investment cost than the three-stage and four-stage systems. Overall, this work provides, for the first time, an NSGA-II-based optimization framework capable of determining the optimal design and operational parameters of LAES, informing the stakeholders of the design guidelines and investment guidance under different scenarios.

Enhancing charge transport and photoluminescence characteristics via transition metals doping in ITO thin films

Abstract This study presents the synthesis and characterization of pristine and transition metal (Co, Fe, and Zr)-doped indium tin oxide (ITO) thin films fabricated via RF magnetron sputtering. The effect of transition metal doping on ITO thin films properties was comprehensively examined using numerous spectroscopic and microscopic methods such as XRD, FTIR, and SEM coupled with EDX, UV–visible and PL spectroscopy, and four-probe and J–V measurements. The cubic crystal structure of the materials was confirmed through XRD spectroscopy, while FTIR results validated the existence of chemical bonds, signified by sharp peaks at 608 cm−1 and 667 cm−1 in the fingerprint region. SEM imaging revealed a granular-like agglomerated structure, with EDX confirming the elemental composition of the samples. The incorporation of Co, Fe, and Zr ions into ITO is aimed at improving photoconductivity and the optical bandgap, with the ultimate objective of enhancing performance in photovoltaic applications. Our findings showed a significant decrease in optical transmission in the visible spectrum. The bandgap also experienced a minor decrease from 3.67 eV to 3.53 eV. Analysis of the photoluminescence spectra exhibited the majority of emission peaks in the UV region, ascribed to electronic transitions occurring via band-to-band and band-to-impurity interactions within the ITO. Electrical measurements indicated lower resistance, higher current flow, and increased carrier concentration in transition metals–doped ITO compared to the undoped ITO, with Zr-doped ITO exhibiting the highest conductivity and optimal charge flow among all dopants. These promising findings in terms of optical, structural, and electrical attributes signal the potential of these materials for photovoltaic system applications.

Research on the effect of the refrigerant charge in a variable capacity heat pump

Current energy efficiency regulations have led to the development of heat pumps equipped with variable speed compressors (VSC) and electronic expansion valves (EEV). To reduce the amount of refrigerant charge, heat pump manufacturers are developing units without a liquid receiver. As a result, the amount of refrigerant charge has a direct impact on the unit performance and needs to be optimized. Previous studies have shown the existence of an optimum refrigerant charge that maximizes the COP at a given operating condition. However, with VSC it is not clear if the optimum charge at a given compressor speed is the adequate one at other speed values. This paper analyzes the effects of the refrigerant charge amount on the performance of a brine-to-water heat pump equipped with a VSC and an EEV. For a given compressor speed, the variation of the COP with the charge showed similar trends and an optimum refrigerant charge could be identified. The charge for the maximum COP changes slightly with the compressor speed. The seasonal coefficient of performance (SCOP) is proposed as a better metric for evaluating the optimum charge. Results show that the SCOP is quite stable with the refrigerant charge.

A smart and intelligence based controlling algorithms for maximum power extraction and effective battery management in EV systems

AbstractAn economically appealing alternative to the use of fossil fuels in many nations is the electric vehicle (EV). Commercial EVs have a battery that powers the motor while also being charged by solar photovoltaic (PV). The original contribution of this research work is to implement a smart and advanced methodologies for accomplishing the objectives of MPPT control and battery management in EVs. Here, a novel spotted hyena optimized (SHO)‐MPPT controlling algorithm is applied to get the most out of the electricity generated by PV panels to meet EV energy requirements. A modified high gain super‐lift Luo converter has been implemented to improve and regulate the output voltage of PV for optimal battery charging and motor control. Then, an adaptive dynamic encoded network controller is designed to improve the voltage regulation and boosting performance of the DC‐DC converter. Consequently, a Henry gas solubility optimization is deployed to perform an effective battery management in EVs by optimally estimating the state of charge, which helps to increase the charging efficiency of EVs. Moreover, a three‐phase voltage source inverter has also been used to lower harmonic levels and deliver high‐quality output power to the EV motor. The results of the proposed MPPT controlling, converter controlling, and battery management strategies are verified in this study using a complete simulation and comparison analysis, where the parameters such as IV‐PV characteristics, motor torque, speed, total harmonic distortions (THDs), and so forth are taken into account for performance validation. By using the proposed controlling methodology, the THD is reduced to 1.09% with the improved vehicle speed of 3000 rpm, and overall performance efficiency of 99%.

Experimental investigation of a bypass two-circuit cycle refrigerator-freezer with low GWP zeotropic refrigerants

Based on the pursuit of lower temperatures and environmentally friendly refrigerants in domestic refrigerator-freezers, this paper investigates the performance of a hydrocarbon mixture R600/R290 in a bypass two-circuit cycle applied in −40 °C frost-free refrigerator-freezer. With the aim of achieving a temperature below −40 °C in the freezer compartment and saving energy consumption in the entire system, experiments are conducted to explore the optimal composition and charge of R600/R290. The results indicate that when the total charge of R290/R600 is 77 g and the mass fraction is 43 %/57 %, the freezer compartment can reach −40.6 °C and has a low daily energy consumption of 1.514 kW h/24h at the ambient temperature of 32 °C. In addition, the effect of fan voltage, which is used to characterize the airflow rate in the freezer compartment, on the system performance at different ambient temperatures is also studied. Under the premise that the charge of R290/R600 is 77 g and the mass fraction is 48 %/52 %, when the fan voltage is 7.5 V, the system daily energy consumption is the lowest. At this time, the system daily energy consumption at 25 °C and 32 °C ambient temperatures is 1.161 kW h/24h and 1.553 kW h/24h, respectively.

Multi-product airport competition and optimal airport charges

This paper examines the importance of commercial revenue on optimal airport charges in a Hotelling-type duopoly airports competition. Each airport offers multi-products to heterogeneous consumers (airlines and passengers) and sets commercial and landing charges and serves. The airport-airline bundle competes for leisure and business passengers. The setting of landing charges under different regulatory regimes is investigated. We demonstrate that in the leisure travel market, which ignores schedule delay cost, the optimal landing fee is invariant to the regulatory scheme, and concession revenue is determined by an airport’s home market size. In the business travel market, the optimal landing charge is smaller if concession revenue is included in setting the landing fee than if it is not included. In the former case, increasing passenger volume does not guarantee increases in airports’ aeronautical revenue, and a negative impact may exist if the weight of concession profit out of total profit is small.

Multi-agent intelligent optimization approach for optimal wireless rapid charging and SOC estimation of hybrid electric vehicle

Multi-agent intelligent optimization approach for optimal wireless rapid charging and SOC estimation of hybrid electric vehicle

Development of an efficient vehicle-to-grid method for massive electric vehicle aggregation

The growing adoption of renewable energy and electric vehicles (EVs) has contributed to environmental sustainability; nevertheless, integration of these products into the power grid has become complex owing to their unpredictable nature and variable energy demands. A significant challenge lies in the realization of large-scale, coordinated control of EVs to serve as an alternative to traditional energy storage systems. This challenge is underscored by the complexity of optimization in large-scale cooperative control problems and the difficulty in reducing such problems to an easily manageable and practical level in real-world application. In response to these challenges, a practical mechanism for the integration of EVs into a vehicle-to-grid concept is proposed in this study. In this approach, the constraints involved in merging multiple EVs into a fictitious clustered energy storage unit, which are often neglected, are given renewed focus. An iterative multi-stage optimization method is introduced that includes an EV aggregation clustering model, multi-tier optimization model, and recursive framework. Here, marked efficacy for larger EV fleets is demonstrated for this method, providing optimal charging and discharging schedules for each vehicle while a high degree of precision is maintained. With the proposed technique, validated through numerous case studies using historical data, the global optimum solution is largely approximated, with a marginal deviation of 4 %. In addition, robustness of the model is demonstrated under varying pricing scenarios, with a computation time that increases in a linear manner, rather than exponentially, as the number of EVs increases. Meanwhile, compared with conventional methods, the technique proposed in this study has the capacity to fulfill the charging demands of all users with reduced charging expense, demonstrating the high precision and efficacy of the technique.

Thermodynamics of Metal-Acetate Interactions.

Metal ions play crucial roles in protein- and ligand-mediated interactions. They not only act as catalysts to facilitate biological processes but are also important as protein structural elements. Accurately predicting metal ion interactions in computational studies has always been a challenge, and various methods have been suggested to improve these interactions. One such method is the 12-6-4 Lennard-Jones (LJ)-type nonbonded model. Using this model, it has been possible to successfully reproduce the experimental properties of metal ions in aqueous solution. The model includes induced dipole interactions typically ignored in the standard 12-6 LJ nonbonded model. In this we expand the applicability of this model to metal ion-carboxylate interactions. Using 12-6-4 parameters that reproduce the solvation free energies of the metal ions leads to an overestimation of metal ion-acetate interactions, thus, prompting us to fine-tune the model to specifically handle the latter. We also show that the standard 12-6 LJ model significantly falls short in reproducing the experimental binding free energy between acetate and 11 metal ions (Ni(II), Mg(II), Cu(II), Zn(II), Co(II), Cu(I), Fe(II), Mn(II), Cd(II), Ca(II), and Ag(I)). In this study, we describe optimized C4 parameters for the 12-6-4 LJ nonbonded model to be used with three widely employed water models (Transferable Intermolecular Potential with 3 Points (TIP3P), Simple Point Charge Extended (SPC/E), and Optimal Point Charge (OPC) water models). These parameters can accurately match the experimental binding free energy between 11 metal ions and acetate. These parameters can be applied to the study of metalloproteins and transition metal ion channels and transporters, as acetate serves as a representative of the negatively charged amino acid side chains from aspartate and glutamate.

Directed ultrafast conformational changes accompany electron transfer in a photolyase as resolved by serial crystallography

Charge-transfer reactions in proteins are important for life, such as in photolyases which repair DNA, but the role of structural dynamics remains unclear. Here, using femtosecond X-ray crystallography, we report the structural changes that take place while electrons transfer along a chain of four conserved tryptophans in the Drosophila melanogaster (6-4) photolyase. At femto- and picosecond delays, photoreduction of the flavin by the first tryptophan causes directed structural responses at a key asparagine, at a conserved salt bridge, and by rearrangements of nearby water molecules. We detect charge-induced structural changes close to the second tryptophan from 1 ps to 20 ps, identifying a nearby methionine as an active participant in the redox chain, and from 20 ps around the fourth tryptophan. The photolyase undergoes highly directed and carefully timed adaptations of its structure. This questions the validity of the linear solvent response approximation in Marcus theory and indicates that evolution has optimized fast protein fluctuations for optimal charge transfer.

Ultra-high output triboelectric nanogenerator based on synergies of material modification and charge pumping

On the road to large-scale industrialization of triboelectric nanogenerators (TENGs), higher output is the constant quest. Here, inspired by the ubiquitous synergies in nature, an ultra-high-output TENG is proposed based on the synergistic effect of internal modification of materials along with the external integration of a charge pumping module. The carefully chosen poly (vinylidene fluoride-co-hexafluoropropylene) (P(VDF-HFP)) film possesses strong polarization and high permittivity, which leads to excellent triboelectric properties, and can be further enhanced by doping BaTiO3 (BTO) nanoparticles. Meanwhile, the elaborately designed and optimized charge pumping module endows it with the ability to pump affluent charges. As a consequence, a TENG based on 8 µm P(VDF-HFP)/BTO 1 wt% composite film can achieve an ultra-high total transfer charge density (TTCD) of 3.5 mC/m2 under optimal charge pumping conditions. Compared to the output of the P(VDF-HFP)/BTO-based TENG (0.1 mC/m2) and the output of the P(VDF-HFP)-based TENG under charge pumping (2.6 mC/m2), the output of the P(VDF-HFP)/BTO-based TENG under charge pumping is greater than the sum of the above two devices, which indicates that the proposed synergistic effect for designing an ultra-high-output TENG is remarkable and successful. Moreover, a novel charge transfer pattern based on synergistic effect is proposed, expounding the role inherent modifications of P(VDF-HFP) and external integration of charge pumping modules play. This work presents a concept that is at the forefront of contemporary designs for ultra-high output TENGs, providing insight into the preparation of high-performance TENGs in a state-of-the-art manner.

Exergy analysis of packed and fluidized bed thermal energy storage systems

This work presents a detailed exergy analysis that compares two different thermal energy storage (TES) systems that use particles as energy storage media and air as the heat transfer fluid: packed and fluidized beds. The study permits the optimization of both TES systems in terms of exergy storage capacity and quantifies the irreversibilities that appear in both cases. In addition, the influence of air flow rate and particle size has been quantified.The results indicate that when focusing solely on the storage process, there is an optimal charging time that minimizes the exergy destruction in both cases: topt∼mscp,s/ṁacp,a. Although the exergy destruction of the fluidized bed during the beginning of the charging process is higher than that in the packed bed, at the end of the process, the total exergy destruction in both beds is similar. However, in the full storage–recovery process the total exergy destruction is doubled in the fluidized case, with an entropy generation number of approximately 0.6 in comparison with a value of 0.3 for the packed bed. Besides, a sensitivity analysis varying the particle size and the air flow rate indicates that the particle size has a much higher influence on the fluidized bed case due to the change in the minimum fluidization velocity and its influence on the heat transfer rate. Finally, a case study is presented using the storage bed for building heating, and an exergy analysis concludes that the fluidized bed generates 534W/K of entropy, more than three times the entropy generated in the packed bed (161W/K), due to the higher pressure drop needed to fluidize the particles.

Spatial-temporal load forecasting of electric vehicle charging stations based on graph neural network

Accurate forecasting of the load of electric vehicle (EV) charging stations is critical for EV users to choose the optimal charging stations and ensure the safe and efficient operation of the power grid. The charging load of different charging stations in the same area is interrelated. However, forecasting the charging load of individual charging station using traditional time series methods is insufficient. To fully consider the spatial-temporal correlation between charging stations, this paper proposes a new charging load forecasting framework based on the Adaptive Spatial-temporal Graph Neural Network with Transformer (ASTNet-T). First, an adaptive graph is constructed based on the spatial relationship and historical information between charging stations, and the local spatial-temporal dependencies hidden therein are captured by the spatio-temporal convolutional network. Then, a Transformer network is introduced to capture the global spatial-temporal dependencies of charging loads and predict the future multilevel charging loads of charging stations. Finally, extensive experiments are conducted on two real-world charging load datasets. The effectiveness and robustness of the proposed algorithm are verified by experiments. In the Dundee City dataset, the MAE, MAPE, and RMSE values of the proposed model are improved by approximately 71%, 90%, and 67%, respectively, compared to the suboptimal baseline model, demonstrating that the proposed algorithm significantly improves the accuracy of load forecasting.