Charge Temperature Research Articles

Feasibility analysis of partially insulated Nb3Sn EU DEMO toroidal field coil

Abstract The advent of no-insulation (NI) winding technique in high temperature superconducting magnets has marked a milestone, demonstrating enhanced thermal stability and mechanical integrity in various high-field applications. There have been reports on numerical and experimental results of the application of NI-class winding techniques to low temperature superconducting (LTS) magnets, showing improved thermal stability such as significant reduction of training, stable behavior during over-current operations, and even recovery after heater-fired quench as a result of current bypass near a hot spot. Thus, the NI approach might offer potential advantage to large scale LTS fusion magnet such as reduction of peak coil terminal voltage or decrease of the copper fraction within the conductor to achieve a more compact design. However, it is essential to firstly assess the potential risks associated with NI approach to large scale LTS fusion magnet to identify any critical barriers. This paper explores the feasibility of partially insulated Nb3Sn EU DEMO toroidal field (TF) coil over a wide range of contact resistivity through two distinct partial insulation winding schemes: (1) inter-turn NI and (2) inter-layer NI scheme. Our investigation encompasses electromagnetic, structural, and thermal-hydraulic analyses to evaluate current distribution, magnet charging time, stress distribution, and temperature rise within the winding pack during magnet charging. The analysis numerically confirms the existence of a feasible design space for a partially insulated TF coil, which enables stable charging and normal operation of the magnet.

Accessing Different Protein Conformer Ensembles with Tunable Capillary Vibrating Sharp-Edge Spray Ionization.

Capillary vibrating sharp-edge spray ionization (cVSSI) has been used to control the droplet charging of nebulized microdroplets and monitor effects on protein ion conformation makeup as determined by mass spectrometry (MS). Here it is observed that the application of voltage results in noticeable differences to the charge state distributions (CSDs) of ubiquitin ions. The data can be described most generally in three distinct voltage regions: Under low-voltage conditions (<+200 V, LV regime), low charge states (2+ to 4+ ions) dominate the mass spectra. For midvoltage conditions (+200 to +600 V, MV regime), higher charge states (7+ to 12+ ions) are observed. For high-voltage conditions (>+600 V, HV regime), the "nano-electrospray ionization (nESI)-type distribution" is achieved in which the 6+ and 5+ species are observed as the dominant ions. Analysis of these results suggests that different pathways to progeny nanodroplet production result in the observed ions. For the LV regime, aerodynamic breakup leads to low charge progeny droplets that are selective for the native solution conformation ensemble of ubiquitin (minus multimeric species). In the MV regime, the large droplets persist for longer periods of time, leading to droplet heating and a shift in the conformation ensemble to partially unfolded species. In the HV regime, droplets access progeny nanodroplets faster, leading to native conformation ensemble sampling as indicated by the observed nESI-type CSD. The notable observation of limited multimer formation and adduct ion formation in the LV regime is hypothesized to result from droplet aero breakup resulting in protein and charge carrier partitioning in sampled progeny droplets. The tunable droplet charging afforded by cVSSI presents opportunities to study the effects of the droplet charge, droplet size, and mass spectrometer inlet temperature on the conformer ensemble sampled by the mass spectrometer. Additionally, the approach may provide a tool for rapid comparison of protein stabilities.

State of Health Estimation for Lithium-Ion Batteries Using Enhanced Whale Optimization Algorithm for Feature Selection and Support Vector Regression Model

Evaluating the state of health (SOH) of lithium-ion batteries (LIBs) is essential for their safe deployment and the advancement of electric vehicles (EVs). Existing machine learning methods face challenges in the automation and effectiveness of feature extraction, necessitating improved computational efficiency. To address this issue, we propose a collaborative approach integrating an enhanced whale optimization algorithm (EWOA) for feature selection and a lightweight support vector regression (SVR) model for SOH estimation. Key features are extracted from charging voltage, current, temperature, and incremental capacity (IC) curves. The EWOA selects features by initially assigning weights based on importance scores from a random forest model. Gaussian noise increases population diversity, while a dynamic threshold method optimizes the selection process, preventing local optima. The selected features construct the SVR model for SOH estimation. This method is validated using four aging datasets from the NASA database, conducting 50 prediction experiments per battery. The results indicate optimal average absolute error (MAE) and root mean square error (RMSE) within 0.41% and 0.71%, respectively, with average errors below 1% and 1.3%. This method enhances automation and accuracy in feature selection while ensuring efficient SOH estimation, providing valuable insights for practical LIB applications.

Application of LoRa Technology in Monitoring Solution for Photovoltaic Systems

Solar array systems with photovoltaic (PV) modules are extensively utilized in remote fields, agriculture, military, and various other sections. However, the challenge lies in the long distances between these installations and end-users, necessitating remote monitoring systems. In this study, we leverage Long Range (LoRa) wireless communication technology due to its extended range, cost-effectiveness, and user-friendly nature. The study focuses on the design and implementation of a Monitoring System (MS) tailored for PV applications, utilizing LoRa technology. The primary objective is to enhance the efficiency, reliability, and maintenance of PV installations by continuously monitoring system health and performance. The MS encompasses sensors, microcontrollers, and processors to collect and process data on PV, battery banks, load, and grid parameters, including voltage, current, temperature, and state of charge (SoC). Processed data is transmitted via LoRa to a centralized control station for analysis and decision-making. LoRa's attributes, such as long-range coverage, low power consumption, and obstacle penetration, make it particularly suitable for remote PV installations. We provide a detailed overview of the system architecture, components, communication protocols, as well as data processing algorithms and battery health estimation techniques. Experimental evaluation conducted on a test PV setup demonstrates the MS's effectiveness in monitoring battery health, anomaly detection, and facilitating remote maintenance in the normal and up-normal conditions and modes. Simulation on the MATLAB/Simulink platform validates the system's functionality under various operating conditions, ensuring robust performance.

State of charge and surface temperature estimation of lithium-ion batteries on the basis of a fractional-order equivalent circuit-thermal coupling model

State of charge and surface temperature estimation of lithium-ion batteries on the basis of a fractional-order equivalent circuit-thermal coupling model

A data-driven early warning method for thermal runaway during charging of lithium-ion battery packs in electric vehicles

Abstract In recent years, thermal runaway during charging of lithium-ion batteries has become a critical issue. This problem has emerged as a significant barrier to the development of power batteries for electric vehicles (EVs). This paper addresses this challenge from a data-driven perspective by proposing a temperature prediction model for thermal runaway during charging of EV lithium-ion batteries. The model leverages both Long Short-Term Memory (LSTM) and Transformer algorithms to account for the time-series characteristics of batteries charging. The charging data under varying capacities and ambient temperatures are extracted using the Newman-Tiedemann-Gaines-Kim (NTGK) model for lithium-ion batteries, which is then used to optimize the accuracy of the hybrid algorithm through training. Additionally, real-world EV charging data is collected to further validate the temperature prediction model. Experimental results demonstrate that the proposed model achieves superior prediction accuracy compared to both single models and convolutional (CNN) hybrid models. Based on this model, a residual-based early warning method incorporating a sliding window approach is proposed. The experimental findings indicate that when the residual of the predicted charging temperature for EVs lithium-ion batteries exceeds the warning threshold, preemptive termination of charging effectively prevents thermal runaway.

Hydrogen driven HCCI engine combustion and performance indices: a numerical investigation

ABSTRACT Interest in hydrogen-powered engines is growing due to their potential to reduce greenhouse gas emissions and decrease reliance on fossil fuels. This research examines how the start of combustion (SOC) impacts the performance of homogeneous charge compression ignition (HCCI) engines using hydrogen exclusively. Using numerical simulations with a 0D multi-zone HCCI model, the study investigates parameters such as compression ratio (CR), equivalence ratio (ER), and intake charge temperature (ICT) to assess their influence on engine performance. The results uncover hydrogen’s unique combustion behaviour, characterised by its physiochemical properties and low-temperature combustion. The study identifies an optimal ER range of 0.1 to 0.3, with various ICTs and it is playing a pivotal role in promoting auto-ignition and accelerating combustion. However, an ER of 0.4 and ICT of 393 K produces relatively higher indicated thermal efficiency, which is affected by knocking due to a higher rate of pressure rise, constraining the ER range. Additionally, the research explores the potential of charge dilution to expand the ER range in HCCI mode. These insights provide valuable guidance for enhancing the operation of hydrogen-fuelled HCCI engines, facilitating the transition to cleaner and more efficient propulsion systems in transportation.

Optimal Charging Current Protocol with Multi-Stage Constant Current Using Dandelion Optimizer for Time-Domain Modeled Lithium-Ion Batteries

This study utilized a multi-stage constant current (MSCC) charge protocol to identify the optimal current pattern (OCP) for effectively charging lithium-ion batteries (LiBs) using a Dandelion optimizer (DO). A Thevenin equivalent circuit model (ECM) was implemented to simulate an actual LiB with the ECM parameters estimated from the offline time response data obtained through a hybrid pulse power characterization (HPPC) test. For the first time, DO was applied to metaheuristic optimization algorithms (MOAs) to determine the OCP within the MSCC protocol. A composite objective function that incorporates both charging time and charging temperature was constructed to facilitate the use of DO in obtaining the OCP. To verify the performance of the proposed method, various algorithms, including the constant current-constant voltage (CC-CV) technique, formula method (FM), particle swarm optimization (PSO), war strategy optimization (WSO), jellyfish search algorithm (JSA), grey wolf optimization (GWO), beluga whale optimization (BWO), levy flight distribution algorithm (LFDA), and African gorilla troops optimizer (AGTO), were introduced. Based on the OCP extracted from the simulations using these MOAs for the specified ECM model, a charging experiment was conducted on the Panasonic NCR18650PF LiB to evaluate the charging performance in terms of charging time, temperature, and efficiency. The results demonstrate that the proposed DO technique offers superior charging performance compared to other charging methods.

Research on the Correlation between the Barrel Temperature and the Parameters of Interior Ballistic and Charge

Abstract Aiming at the problem that the barrel life of a large calibre artillery is obviously lower than the international advanced level, and the thermal erosion inside the barrel is serious, based on modern interior ballistic theory and heat transfer theory, a simulation of one-dimensional two-phase flow interior ballistic, heat exchange process between gas-solid two-phase flow and the barrel is carried out. The influence of maximum pressure in bore, charge, propellant explosion temperature, barrel structure and other parameters on the barrel temperature is obtained, and the correlation between the barrel temperature and parameters of interior ballistic and charge is analyzed, providing direction for the design of low-ablation interior ballistic and charge.

Study on predicting the stability of penetrating projectile charges via machine learning methods

Abstract Due to the limitations of current theoretical research, numerical simulation, and experimental analysis for predicting the stability of penetrating projectile charge, in this work, we propose a method to predict the stability of charges by using machine learning methods. Three models are selected, including Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting Tree (XGBoost). A practical sample dataset with inputs of the charge type, temperature and speed, and outputs of whether the charge is stabilized or not is constructed to train the model. The results indicate that using machine learning methods to predict the stability of charges is a feasible research direction.

Analysis of a Coupled Calcium Oxide-Potassium Carbonate Salt Hydrate based Thermochemical Energy Storage System

Analysis of a Coupled Calcium Oxide-Potassium Carbonate Salt Hydrate based Thermochemical Energy Storage System

Insights into the Lithium Solvation Environments of Localized High Concentration Electrolytes by NMR and Effects on NMC811/Gr Cell Performance

Advanced battery technologies require advanced electrolytes to support high voltage and long cycle life at a wide range of temperatures and charging rates, and enhanced safety. The lithium solvation environment within the electrolyte can have a large impact on cell performance, influencing ion transport in the electrolyte as well as ion transfer at the electrode-electrolyte interfaces.1 By optimizing lithium-ion solvation, cell kinetics can be improved to facilitate low temperature and fast charge performance, while also promoting stable electrode interfaces during high temperature operation. Localized high concentration electrolytes (LHCEs)- composed of lithium salt(s), solvents, and a non- or minimally-solvating diluent -are designed to enhance the local concentration of salt around the lithium ion. These anion-involved lithium solvation environments can increase ion transport in the electrolyte and lead to more stable, inorganic-rich interfacial species.2 While LHCEs have shown promise in improving performance for lithium metal, high voltage, fast charge, and low temperature applications,2,3 further understanding of solvation in LHCEs and development of new diluent molecules is needed. The development of diluents should include solvation and physical property characterization, as well as performance testing in battery cells.Previous studies have demonstrated that nuclear magnetic resonance spectroscopy (NMR) can be used to provide insight into the solvation environments of electrolytes with standard carbonate solvents as well as novel solvent species.4,5 In this work, we utilized NMR to build understanding of the cation (Li: 7Li-NMR), anion (PF6: 19F-NMR), and solvent (carbonates: 13C-NMR) solvation environments of standard electrolytes as a function of salt concentration. We then applied this understanding to NMR analysis of electrolytes with a known diluent to establish a baseline of how solvation changes for LHCEs. Finally, this body of knowledge was used to investigate a novel fluorinated ether species, KDC-403, proposed as a diluent for LHCEs. Furthermore, we demonstrate improved low temperature cycling and more stable fast charge cycling with reduced lithium plating in electrolytes containing KDC-403, supporting previous studies that conclude LHCEs can provide superior battery performance.

Investigating Low Temperature Degradation and Its Impact on the Performance and Safety of Lithium-Ion Batteries

Lithium-ion batteries are at the forefront of facilitating the modern-day industrial revolution: a transition from petrochemical energy storage to specialised carbon-free energy storage. Batteries hold a crucial role in storing energy generated by renewables outside of peak energy demands, whilst also enabling an extended portable energy supply for applications such as electronic devices, aerospace applications and electric vehicles. Increasing the energy densities of our batteries is one of the main avenues these technologies are taking to ensure the demands of the green transition are met. In recent years, numerous high-profile failures of lithium-ion batteries have been reported1, contributing to wider concerns about the safety of high energy density cells. Inadequate management of such systems can be costly for both human health and financial damages. To accommodate these more energy-dense cells, manufacturers need to provide better assurances in safe cell operations.Apprehensions in cell safety can mainly be attributed to a cell’s ability to enter thermal runaway, a process that is most often caused by an internal short circuit (ISC). Such an event can be triggered by three different modes of abuse: thermal, mechanical, and electrical. At the anode, metallic lithium can precipitate onto the surface via three main conditions: overcharge, high charging currents, and low charging temperatures. Each of which creates a saturation of intercalated lithium-ions in the crystallographic active sites near the anode surface. This lowers the anodes surface potential until it is sufficiently low enough for lithium plating to occur. This work investigates cell function at low temperatures and how the resulting degradation affects their response to abusive conditions.Various ageing regimes were applied to a set of commercial lithium-ion cells and, by monitoring their electrochemical behaviour, carrying out ex-situ characterisation of the aged negative electrodes, and employing X-ray computed tomography (CT), this work evaluates the decline in performance observed at low temperatures. Subsequent accelerated rate calorimetry (ARC) and operando ultra-high-speed synchrotron tomography studies highlight the diminished thermal stability these aged cells possess as the states of degradation become more advanced and reveal the mechanisms through which failure occurs. These findings demonstrate why improved cell architecture and real-time management systems are necessary to realise commercial success in future battery applications. Figure 1

Detecting Internal Short Circuits in Commercial Sodium Ion Batteries By Measuring Their Cryo-Resistance

Sodium-ion batteries (SIB) are considered a more cost-effective and environmentally friendly alternative to the commonly used lithium-ion batteries. They are expected to reach energy densities up to 150 Wh/kg and thus to be suitable for applications where currently lithium iron phosphate (LFP) batteries are used. While research on SIBs started over 50 years ago, only recently have SIBs been commercially available. We systematically investigated two different cell types and, among others, cycled these cells with different load rates. Both cell types use hard carbon as anode and layered oxide, namely NaFe1/3Ni1/3Mn1/3, as cathode material. We classified these cells as high-power (SIBHP), and high-energy (SIBHE) types based on their maximum charge currents.We found that some of these cells showed a sudden increase in temperature accompanied by a voltage drop during charge. The charge capacity was strongly increased, while the discharge capacity showed no, or only a very small difference compared to the previous discharge capacity. Thus, this end-of-charge temperature rise leads to a strong decrease in coulombic efficiency, which was measured to be decreased by only a few percent points up to 40%. This effect happens at lower state-of-charges when the environment temperature decreases or the charge rate increases.While we could detect this effect during higher charge rates close to the limits of the datasheets for both cell types, for SIBHP, we could detect that pausing before the test can have a strong negative influence. We found several cells which, after being stored for at least 24h at 25°C, have shown this temperature rise already at a charge current of 0.5C, which is only 10% of the maximum allowed value.To evaluate the cause of this effect, we opened the cells under Argon atmosphere. We could see that the cells which have shown this end-of charge temperature rise had, in contrast to cells which have not shown such behavior, deposits on the anode which we interpret as sodium. These were more clearly recognizable than similar pictures of lithium plating presented in literature. Furthermore, some of the anodes have already shown structural changes. We thus assume that the sodium plating leads to sodium dendrites which causes internal short circuits. This theory is supported by the fact that cells can show for instance over 10 cycles a difference in charged and discharged capacity which is 300% of the initial capacity of the cell (measured for the SIBHP). Thus, a sodium consuming effect could not sufficiently explain this phenomenon.To proof our theory, we have chosen 3 cells per type which were known to show the effect of sudden temperature rise. These cells were at rest at room temperature for more than 24h. We put these cells into liquid nitrogen for at least 10 minutes. We monitored their voltage and could see that it drops to 0V within the first 2 minutes. Then, directly after removing the cells from the liquid nitrogen, we measured their resistance using a multimeter. Results have shown that the cells which have shown the temperature rise, had significant lower cryo-resistances (for instance 10 kΩ versus 1 MΩ in the case of SIBHP). Then, we charged the SIBHP with 0.5C at 25°C, while the SIBHE were charged with 1C at 20°C. Now, we stopped the test when the temperature rise was more 2K/min. Directly after end of the test, we freezed the cells again as described above. Now, the measured cryo-resistance was strongly decreased down to 30 Ω (in case of SIBHP). After pausing for 1h at 25°C we measured cryo-resistance again and found values in between those before and after the charging of the cells. This can be explained such that during charging internal short circuits due to dendrites build. However, when the cell is stored at room temperature, this short circuits degrade over time. As a result, the cryo-resistance increases again, as the internal short circuits reduce.Concluding, we could show that the commercially available sodium-ion cells we tested show sodium plating, which can lead to a sudden temperature increase during charging. This effect could be shown to be a result of internal short circuits. This was done by freezing the cells electrolyte using liquid nitrogen and subsequently measuring their cryo-resistance. Lastly, we measured that these internal short circuits degrade over time when storing the cell at room temperature. Figure 1

Investigating the impacts of charge composition and temperature on ammonia/hydrogen combustion in a heavy-duty spark-ignition engine

Investigating the impacts of charge composition and temperature on ammonia/hydrogen combustion in a heavy-duty spark-ignition engine

Fast Charging Sodium‐Ion Full Cell Operated From −50 °C to 90 °C

AbstractThe application of sodium‐ion batteries (SIBs) within grid‐scale energy storage systems (ESSs) critically hinges upon fast charging technology. However, challenges arise particularly with anodes such as hard carbon (HC), which exhibits a low working plateau (less than 0.1 V vs Na/Na+) and is susceptible to sodium dendrite issues under high current densities. In this study, a cost‐effective SIB system comprising Na2.4Fe1.8(SO4)3 (NFS) cathode, NaTi2(PO4)3 (NTP) anode, and ester‐based electrolyte is assembled to solve the fast‐charging obstacle. Benefiting from the fast sodium‐ion diffusion kinetics and relatively high voltage platform of NTP anode, this full cell can work for 10 000 cycles at 10 C rate with a notable capacity retention of 70.7%. Moreover, this investigation reveals that the full cell can operate safely between ‐50 to 90 °C even with an ester‐based electrolyte, thereby showcasing broad application prospects. This work provides a valuable guidance for designing fast charging and wide temperature SIBs.

Fuzzy Logic Approach for Modeling of Heating and Scale Formation in Industrial Furnaces.

Heating steel charges is essential for proper charge formation. At the same time, it is a highly energy-intensive process. Limiting the scale formed is critical for reducing heat consumption in this process. This paper applies fuzzy logic to model heating and scale formation in industrial re-heating furnaces. Scale formation depends on the temperature of the initial charge, heating time, excess air coefficient value, and initial scale thickness. These parameters were determined based on experimental tests, which are also the inputs in the model of the analyzed process. The research was carried out in walking beam furnaces operating in hot rolling mill departments. To minimize the excess energy consumption for heating a steel charge in an industrial furnace before forming, a heating and scale formation (HSF) model was developed using the fuzzy logic-based approach. The developed model allows for the prediction of the outputs, i.e., the charge's final surface temperature and the scale layer's final thickness. The comparison between the measured and calculated results shows that the model's accuracy is acceptable.

Digital Twin-Based Hydrogen Refueling Station (HRS) Safety Model: CNN-Based Decision-Making and 3D Simulation

This study presents a safety management model for hydrogen refueling stations, integrating digital twin technology and artificial intelligence (AI) to enhance operational safety. Given the risks associated with high-pressure gas handling and potential fires from hydrogen leaks, real-time safety monitoring is crucial. The proposed model is based on a digital twin, a virtual replica of the physical system using real-time data, including temperature, pressure, and state of charge, collected from an actual hydrogen refueling station in Samcheok, Gangwon Province. Out of nine tested machine learning and deep learning algorithms, the convolutional neural network (CNN) demonstrated the highest performance (accuracy: 1, F1 score: 0.993) for risk prediction. Using AI libraries like Scikit-Learn and TensorFlow, the model achieved prediction times of 68 milliseconds, enabling decision-making at intervals of 1 s. Developed with the Unity 3D modeling tool, the digital twin visualizes predicted risk situations, allowing users to quickly identify and respond to potential hazards. This approach offers a robust solution for improving the safety of hydrogen refueling stations.

Advanced Intelligent Anti‐Counterfeiting in a Memory Storage Phosphor Through Response of Charge Carriers to Temperature

AbstractPersistent phosphor as a printing anti‐counterfeiting material has attracted strong attention owing to its inherent luminescent decay process and multi‐mode luminescence characteristics under external field stimulation. However, the dynamic persistent luminescence (PersL) pattern of a single material is only manifested in color changes and still faces the problem of being easily decrypted. Herein, a multi‐color luminescent material with a wide range distribution of traps is developed, which can emit strong white PersL. In addition, the phosphors show an intelligent response of thermo‐stimulated luminescence to ambient temperature via recalling the charging temperature of the traps. The PersL mechanism is proposed based on electron transfer between PbO6 and TbO6 octahedrons through the O bridging. Based on the intelligent response of thermo‐stimulated luminescence to temperature, high‐throughput multi‐channel luminescence anti‐counterfeiting can be achieved on a single phosphor recording layer. This study not only provides further insights into the PersL mechanism, but also opens a new door for constructing intelligent responsive anti‐counterfeiting materials.

Improving the charging performance of latent heat thermal energy storage systems using triply periodic minimal surface (TPMS) structures

Improving the charging performance of latent heat thermal energy storage systems using triply periodic minimal surface (TPMS) structures