Charging Process Research Articles

Optimization of formation charging process based on energy conservation in vanadium redox flow battery

Optimization of formation charging process based on energy conservation in vanadium redox flow battery

Impact and optimization of vehicle charging scheduling on regional clean energy power supply network management

Driven by the global energy transition, the widespread use of electric vehicles has profoundly reshaped the transportation landscape and thrown many problems to the power system, and coordinating their charging needs with renewable energy generation has become a key part of ensuring the stable operation of regional clean energy power supply networks. This study focuses on the problem of vehicle charging dispatch to make a breakthrough, deeply analyzes the effect and efficiency of the clean energy grid, and then proposes a series of targeted measures to effectively improve the operational efficiency and reliability of the energy system. The comprehensive model integrates electric vehicle charging stations, distributed photovoltaic power generation systems, wind farms, and battery energy storage devices and enables the charging process to be accurately controlled with real-time monitoring and intelligent algorithms. In particular, the demand forecasting model based on machine learning effectively solves the dilemma of matching the charging load with a clean energy supply. Experimental data strongly confirms that the optimization strategy has led to a 15% reduction in peak load on the grid, a 23% increase in the proportion of clean energy consumption, and a 10% reduction in total electricity consumption. For policymakers, these achievements can be used as a guide to help formulate energy policies and build a framework for adapting to the development of new energy. For practitioners, they serve as a guide to energy planning, grid dispatch, and technology research and development to improve effectiveness. The research promotes the growth of green energy, optimizes the energy structure, lays the foundation for a low-carbon and environmentally friendly society, affects the economy, environment, culture, and other fields, and becomes a key force driving sustainable development.

A Real‐Time Adaptive Machine Learning Charging and Neural Network Balancing Mechanism of Lithium‐Ion Battery Pack

ABSTRACTIn this article, a real‐time novel adaptive deep neural network (A‐DNN) charging scheme is proposed which increases the life of the batteries by controlling the heating impact inside the battery. The input variables used in the charging algorithm are state of charge (SoC), state of health (SoH), voltage (V), current (I), and temperature (T) which makes the algorithm adaptive toward the temperature deviation and reduces the peak overshoot of the temperature at different SoH of the batteries. The parameters of the battery 1‐RC model are estimated by the forgetting factor recursive least square (FF‐RLS) method. The SoC and SoH are estimated by the dual‐particle filter (D‐PF) algorithm. Furthermore, a DNN balancing mechanism sensitive to SoC and SoH is developed to avoid the fault in the battery during the charging process. The A‐DNN charging algorithm is compared with the constant current constant voltage (CC‐CV), constant current pulse charging (CC‐PC), and deep neural network (DNN) charging algorithms at 40°C, 45°C, and 50°C. The A‐DNN outperforms in terms of peak temperature, incremental life, and charging time of the batteries at 45°C. The proposed charging methodology reduces the economic cost of the EVs by increasing the life of the battery by 34.41% at 45°C as compared to the other algorithms.

Remaining useful life prediction of lithium-ion battery based on AConvST-LSTM-Net-TL

Abstract The accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries can significantly enhance the safety and reliability of these batteries, thereby reducing operational risks. However, numerous existing methodologies operate under the assumption that both training and testing data adhere to the same distribution pattern, hindering the application of successful laboratory-based models to different target batteries. Hence, this study introduces a transfer learning model for predicting lithium-ion battery life, utilizing an attention mechanism-driven convolutional neural network (ACNN) in conjunction with a spatiotemporal Long Short-Term Memory network (ST-LSTM). Initially, the deep charging process data is leveraged for battery capacity estimation, where a Generalized Additive Model (GAM) is employed to delineate the nonlinear trend of battery capacity decay and characterize the capacity degradation. Following feature extraction, the Maximum Mean Discrepancy (MMD) value is computed to assess the distribution disparity between the two domains. Subsequently, the AConvST-LSTM-Net computes capacity estimations, loss functions for both source and target domains, merging these with the MMD value to formulate a comprehensive loss function. In conclusion, by employing transfer learning, the refined model is adapted to the target domain dataset, enabling the accurate prediction of the RUL of lithium-ion batteries. This approach is validated using the NASA lithium-ion battery dataset and the National Big Data Alliance for New Energy Vehicles (NDANEV), demonstrating the superior accuracy and robustness of the AConvST-LSTM-Net-TL model in comparisn to other prediction methods.

Magnetic Gear Wireless Power Transfer System: Prototype and Electric Vehicle Charging

This paper investigates the potential of a magnetic gear wireless power transfer (WPT) system for electric vehicle (EV) charging, with the advantages of low-frequency operation, low foreign object interference, low electromagnetic emissions, and high misalignment tolerance. The study explores the novel impact of Halbach arrays that enhance the flux density in desirable locations while decreasing the flux in undesirable locations, which provides the benefit of decreased foreign object attraction. The initial prototype results demonstrate that the Halbach system can transmit approximately 34.65 W with a transfer efficiency of 64% across a gap of 104 mm. The Halbach system is experimentally compared to a conventional magnet arrangement, which achieved a maximum power transfer of 88 W over 104 mm. The Halbach system is applied to a personal mobility EV to enable wireless charging at low frequency. The axial design of this WPT system has the unique benefit of a 360° radial coupling angle that maintains constant, near-maximum levels of power transfer and efficiency. This full circle coupling angle allows the personal EV to park in any direct vicinity of the charger and achieve the same level of charging given a certain distance. This study delivers important contributions to advancing a low-frequency wireless EV charging technology based on magnetic gears, that sets the stage for future innovations focused on optimizing efficiency, increasing safety, and simplifying the charging process.

Impact of Subsurface Oxygen on CO2 Charging Energy Changes in Cu Surfaces.

Subsurface oxygen in oxide-derived copper catalysts significantly influences CO2 activation. However, its effect on the molecular charging process, the key to forming the CO2δ- intermediate, remains poorly understood. We employ many-body perturbation theory to investigate the impact of the structural factors induced by the subsurface oxygen on the charged activation of CO2. By computing the molecular single-particle state energy of the electron-accepting orbital on the Cu (111) surface, we examined how this molecular quasi-particle (QP) energy changes with the varying vicinity of adsorption and multiple-subsurface oxygen configuration. We demonstrate that subsurface oxygen impairs CO2 charging, with its presence and coverage being influential factors. However, we remark that density functional theory calculations do not predict such an excitation energy discrepancy induced by subsurface oxygen. The nonlocal potential proves to be substantial for accurate excitation energy predictions yet is not sensitive to minor atomic structural changes. More importantly, state delocalization and hybridization are critical for determining QP energy. These insights are enlightening for designing atomic architectures to optimize catalytic performance on modified surfaces.

Design and Operation of a Novel Cross Fin in Hot-Water Production System for Buildings

The importance of phase change heat storage (PCHS) in solar thermal applications is limited by the low thermal conductivity of phase change materials (PCMs) and the uneven temperature distribution during heat transfer. This study proposes to use composite fins for heat exchange in the PCHS module and integrate them into a hot-water production system (HWPS) for building heating. The effectiveness of the novel fin structure is assessed through thorough numerical simulations and experimental validation. An examination of melting fractions, temperature distribution, and flow characteristics of the molten PCMs across various fin structures indicates that increasing the lengths and quantities of the cross fins can alleviate the challenge of incomplete melting at the end of the charging process. Notably, expanding the surface area of the cross fins results in a 7.37-fold increase in the average thermal storage rate and a 781.25% enhancement in the average temperature response compared to the original design. These findings show that the new composite fin design greatly improves the heat storage performance of an HWPS, which is of great significance for building energy conservation.

Controllable Synthesis and “Fast‐charging” Mechanism of Hollow‐Micropencil Co3V2O8 from Perspective of Ion Diffusion in Crystal Structure

AbstractCo3V2O8 as a bimetallic oxide with “fast‐charging” capacity presents some potential advantages, comprising decent theoretical specific capacity, favorable crystal structure, and intrinsic safety. Presently, although extensive research of Co3V2O8 as an anode material are reported, the formed Co° from Co3V2O8 after the initial discharging is not oxidized to CoO during the charging process, and the ion diffusion mechanism are not well clarified. The lithium‐storage process of Co3V2O8 and the diffusion mechanism of lithium ion in Co3V2O8 are studied in detail in this work. A hollow‐micropencil Co3V2O8 prepared through the hydrothermal method without using surfactants or templates demonstrates a high “fast‐charging” capability even at the current densities as high as 1.0–2.0 A g−1. The electrochemical kinetic results illustrate that the hollow‐micropencil Co3V2O8 presents low charge transfer resistance. The distinctive lithium ion storage sites as well as diffusion paths are predicted. The in situ X‐ray diffraction analysis is adopted to confirm that the formed Co° after the initial discharging is not oxidized to CoO during the delithiation process. The work not only helps to understand the lithium insertion mechanisms of Co3V2O8 but also offers new means to develop “fast‐charging” anode material for lithium‐ion batteries with remarkable electrochemical performance.

Experimental Characterization of Reversible Oil-Flooded Twin-Screw Compressor/Dry Expander for a Micro-Scale Compressed Air Energy Storage System

The reversible use of a volumetric machine as a compressor and expander shows potential for micro-scale compressed air energy storage systems because of lower investment costs and higher operational flexibility. This paper investigates experimentally the reversible use of a 3 kW oil-flooded twin-screw compressor as an expander for a micro-scale compressed air energy storage system to assess its operation while minimizing operating costs and the need for adjustments. As a result, the oil injection was only implemented in the compressor operation since the oil takes part in the compression process, while its use appears optional in expander operation. The results indicate that the compressor exhibited an efficiency in the range of 0.57–0.80 and required an input power from 1 kW up to 3 kW. These values decreased for the expander, whose efficiency was in the range of 0.24–0.38 and the delivered power between 300 and 1600 W. The experimental data allow assessing the operation of such machine in a hypothetical micro-scale compressed air energy storage. The calculation revealed that this machine may operate in this energy storage asset and deliver up to 90% of the power recovered in the charging process when the temperature of the stored air is 80 °C.

Tailoring a High Loading Atomic Zinc with Weak Binding to Sodium Toward High-Energy Sodium Metal Batteries.

Single-atom materials provide a platform to precisely regulate the electrochemical redox behavior of electrode materials with atomic level. Here, a multifield-regulated sintering route is reported to rapidly prepare single-atom zinc with a very high loading mass of 24.7 wt.% by significantly improved diffusion kinetics and stronger charge transfer between zinc and nitrogen atoms. X-ray absorption near edge structure (XANES) spectra for Zn K-edges during the charge and discharge process verify the stable single-atom zinc structure and the reversible slight reduction and oxidation of Zn sites, which is much different from the previous report of the alloying reaction process. This result suggests atomic Zn acts as an active sites through weak binding with sodium to regulate the Na ion fluxes. Finally, Cu foil coated with a ≈2µm layer of such material exhibits a high Coulombic efficiency of ≈99.99% up to 1700 cycles at 1mAhcm-2. An ultra-low overpotential of 3mV and an unprecedented life span of over 3200h in a symmetrical cell is achieved. Due to the very thin coating layer, anode-free sodium battery fabricated by Na3V2(PO4)3 cathode displays a prominent energy density of 320WhKg-1, demonstrating strong potential in practical application.

Hydrogel Electrolyte-Mediated In Situ Zn-Anode Modification and the Ru-RuO2/NGr-Coated Cathode for High-Performance Solid-State Rechargeable Zn-Air Batteries.

This work aims to deal with the challenges associated with designing complementary bifunctional electrocatalysts and a separator/membrane that enables rechargeable zinc-air batteries (RZABs) with nearly solid-state operability. This solid-state RZAB was accomplished by integrating a bifunctional electrocatalyst based on Ru-RuO2 interface nanoparticles supported on nitrogen-doped (N-doped) graphene (Ru-RuO2/NGr) and a dual-doped poly(acrylic acid) hydrogel (d-PAA) electrolyte soaked in KOH with sodium stannate additive. The catalyst shows enhanced activity and stability toward the two oxygen reactions, i.e., oxygen reduction and evolution reactions (ORR and OER), with a very low potential difference (ΔE) of 0.64 V. The computational insights bring out the electronic factors contributing to the enhanced catalytic activity of Ru-RuO2/NGr based on the charge density difference (CDD) between the interfaces. The disadvantages of the existing solid-state RZABs, such as their limited lifespan brought on by passivation, dendritic growth, corrosion, and shape change, have also been taken into account. The introduction of the stannate additive to the electrolyte induced an in situ Zn-anode modification, which subsequently improved the interfacial stability of the ZABs and, hence, the battery life cycles. The experimental observations reveal that, during the charging process, the Sn nanoparticles enable the homogeneous Zn deposition on the surface of the anode. Thus, the in situ Zn-anode surface modification assisted in achieving a high-rate cycle capability, viz., the homemade catalyst-based system exhibited continuous charge-discharge cycles for 20 h at a current density of 2.0 mA cm-2, with each cycle lasting for 5 min.

Buckled-layer K1.39Mn3O6: a novel cathode for potassium-ion batteries.

A novel buckled-layer K1.39Mn3O6 cathode was synthesized, featuring P2-oxygen stacking and P3-K sites. The presence of two independent manganese sites significantly influences the charge distribution and diffusion pathways of potassium ions during the charge and discharge processes, resulting in exceptional electrochemical performance. A reversible capacity of 105 mA h g-1 at 100 mA g-1 and remarkable rate capability of 70 mA h g-1 at 1 A g-1 were achieved, surpassing those of most previously reported layered transition metal oxide cathodes. The findings facilitate the development of innovative cathode materials.

Unraveling the competition between charge and energy transfer in 0D/2D nanographene-graphene heterojunctions

The charge and energy transfer processes in photoexcited 0D/2D donor/graphene heterojunctions occur through multiple different pathways. A donor deexcitation event occurring in the most prevalent Förster energy transfer mechanism (strongly favored over Dexter transfer in van der Waals heterojunctions) prevents charge transfer from taking place, thus creating a competition between the two processes. By applying a robust computational approach, we describe the two processes from first principles and quantify their rates using Förster and Marcus theories. We consider nanojunctions where the donor are nanographenes with varying size and symmetry, and discern important trends, e.g., the symmetry-induced quenching, or the enhancement due to increased size. We observe that heterojunctions where nanographenes do not have a center of symmetry show decreased photoinduced hole and energy transfer rates, which can then be recovered by increasing the delocalization length, whereas for centrosymmetric nanographenes both hole and energy transfer processes are enhanced. Nevertheless, the hole transfer rate dominates over the energy transfer process, providing a new computation-driven design principle for obtaining a high-charge transfer junction with minimized contribution of the competing energy transfer.

AI-Driven Optimization of Renewable Energy Storage Systems in Smart Cities Using Improved Binary Genetic Algorithm

Today, the utilization and management of renewable energy have become integral to the development of smart cities. This paper explores the application of Artificial Intelligence (AI) in analyzing energy storage and renewable energy systems within smart city contexts. We introduce a joint optimization method that combines two-stage input feature selection and parameter training in traditional prediction models. This method integrates physical, statistical, and AI modeling techniques. Additionally, we conduct comprehensive experiments on feature input selection using a Binary Genetic Algorithm (BGA) and two-stage model parameter optimization based on a Natural Evolution Strategies (NES). The experimental results provide strong support for the conclusions of this study. Finally, we simulate the full charge process of energy storage in a smart city and predict the full charge data for batteries B0005, B0006, B0007, and B0029. The results indicate that the NES-BGA-ELM approach outperforms the BGA-ELM method in terms of error reduction.

DESIGN AND DEVELOPMENT OF THREE-DEGREES-OF-FREEDOM ROBOTIC ARM FOR ELECTRIC VEHICLE CHARGING STATION

The exponential growth of the electric vehicle (EV) industry requires the development of more efficient and easily accessible charging infrastructures. The existing manual charging procedures provide significant difficulties, especially for those with impairments. The objective of this project was to overcome these difficulties by designing and developing a robotic arm that is particularly designed for automating the process of charging electric vehicles. The project aimed to improve operational efficiency and user accessibility by designing a three-degrees-of-freedom robotic arm using CAD software, SOLIDWORKS, and prototyping it using 3D printing technology. The main approaches employed were thorough Finite Element Analysis (FEA) to guarantee the structural integrity to confirm the operational dependability. The primary results indicate that the robotic arm design successfully automates the charging process, resulting in a substantial reduction in the human labor needed from users and an improvement in the accuracy of the charging connection. These results not only showcase the arm's capacity to completely transform EV charging methods but also emphasize its flexibility in different vehicle settings, indicating a wide range of possible uses in automated systems in the future.

Research on the Fast Charging Strategy of Power Lithium-Ion Batteries Based on the High Environmental Temperature in Southeast Asia

To address the problem of excessive charging time for electric vehicles (EVs) in the high ambient temperature regions of Southeast Asia, this article proposes a rapid charging strategy based on battery state of charge (SOC) and temperature adjustment. The maximum charging capacity of the cell is exerted within different SOCs and temperature ranges. Taking a power lithium-ion battery (LIB) with a capacity of 120 Ah as the research object, a rapid charging model of the battery module was established. The battery module was cooled by means of a liquid cooling system. The combination of the fast charging strategy and the cooling strategy was employed to comprehensively analyze the restrictions of the fast charging rate imposed by the battery SOC and temperature. The results indicate that when the coolant flow rate was 12 L/min and the inlet coolant temperature was 22 °C, the liquid cooling system possessed the optimal heat exchange capacity and the lowest energy consumption. The maximum temperature (Tmax) of the battery during the charging process was 50.04 °C, and the charging time was 2634 s. To lower the Tmax of the battery during the charging process, a charging rate limit was imposed on the temperature range above 48 °C based on the original fast charging strategy. The Tmax decreased by 0.85 °C when charging with the optimized fast charging strategy.

Additives Capable of Stably Supplying Anions/Cations for Homogeneous Lithium Deposition/Stripping.

One of the important factors leading to lithium dendrites is a slow lithium-ion mass transport and imbalanced distribution of the Li+ concentration and nuclei sites on the anode surface. To achieve uniform lithium deposition during the charge and discharge process, we introduce a homemade new copolymer (with the quaternary ammonium group N3R+I- on its side chain as the main functional group), named P35, as a functional electrolyte additive to regulate the lithium deposition. Theoretical calculations show that under the strong coordinating interaction between I- and N3R+, P35 preferentially adsorbs onto the lithium foil surface, effectively countering the adsorption of lithium salt anions such as PF6-. Moreover, the positive charge carried by the quaternary ammonium salt group of P35 could interact with PF6- to limit their mobility. Consequently, the dipole interaction on lithium ions is diminished, leading to an enhancement in the transport rate and a decrease in the concentration gradient of lithium ions. Furthermore, a more efficient SEI was formed due to the dual charges electrostatic shield formed by N3R+I-. Li-Li symmetric cells and Li-LiFePO4 full cells assembled with electrolytes with P35 exhibit better rate performance, smaller polarization, and smoother deposition morphology in comparison to the cells without the P35 additive.

Monarch butterfly optimization based energy management system for electric vehicle with interleaved landsman converter

The toxic emissions from combustion engine cars and businesses using fossil fuels have steadily increased over time in today's world of expanding needs and urbanization. Due to some advantages over vehicles with traditional engines, using electric vehicles is the most well-liked and successful alternative. The performance of electric cars is significantly influenced by an energy management system (EMS). In this study, a multifunctional power electronic interface capable of using two sources simultaneously throughout the charging process monarch butterfly optimization (MBO) based EMS for plug-in electric vehicles (PHEV) has been developed. The battery can be charged from the grid or solar photovoltaic (SPV), depending on the need. This study designs an electric vehicle (EV) charging system that uses an interleaved landsman converter followed by a flyback converter. As a result, the suggested converter helps in improving battery safety as well as user safety for the vehicle. The proposed control technique is simulated using system models created with MATLAB/Simulink. The outcomes of the simulation demonstrate that the devised technique offers better control in terms of vehicle stability. A number of simulations are run to evaluate the efficiency and performance of the proposed approach.

Blue Energy Extraction Performance by Polyelectrolyte-Coated Porous Electrodes: Effects of Opposing Polyelectrolyte Interaction in Micropores.

In the capacitive mixing technique, the electrode used to extract blue energy is typically composed of a carbon-based porous electrode material. Polyelectrolyte (PE) surface coating on porous electrodes serves as an intermediate soft layer, which can significantly enhance the energy extraction performance (EEP). Herein, the blue energy extraction performance by using PE-coated electrodes is studied by a statistical thermodynamic theory, with the exploration of the interplay effects between opposing polyelectrolyte interactions and pore size. Because of the interaction between opposing PE coatings, the response of electrostatic properties in the pore to surface potential variations is enhanced during charging/discharging processes, leading to a better EEP. Both the surface charge accumulation and surface potential rise in the charging process can be raised as the results of PE coating. For the cases studied, the extraction efficiency can be promoted up to 45% compared with the cases of bare electrodes. For narrow pores, the PE promotion effects are suppressed by strong pore confinement. The optimal PE coating condition is determined by the competitive results between pore size (H) and polyelectrolyte chain length (N), with that Ĥ ≡ H/(2Nσ) being mostly in [0.1, 0.5], where σ is the PE segment diameter.

Capacity estimation of lithium-ion battery based on charging voltage and GAF-LSTM

ABSTRACT Accurate estimation of the capacity of lithium-ion battery is crutial for the health monitoring and safe operation of electronic equipment. However, it is difficult to ensure a complete charge-discharge cycle because of the randomness of the battery working state under actual working conditions. Insufficient capture of feature information will also lead to low prediction accuracy of the model. Aiming at the above problems, a method for estimating the capacity of lithium-ion battery based on charging voltage, Gramian Angular Fields (GAF) and Long Short-Term Memory Network (LSTM) is proposed. To make up for the randomness of battery data cycle, this method selects a fixed voltage segment from the charging process and extracts three characteristic parameters closely related to capacity decline. GAF is used to transform one-dimensional feature parameters into two-dimensional image structure, and the neglected hidden features are fully mined. It emphasizes the global correlation between features and enhances the ability of feature expression. Then, the feature map is input into the LSTM network for training and capacity estimation. This method is verified on two public datasets. The results show that this method has achieved good prediction effect and proved its superiority.