Battery Energy最新文献

Aluminum-based aqueous batteries are considered one of the most promising candidates for the upcoming generation energy storage systems owing to their high mass and volume-specific capacity, high stability, and abundant reserves of Al. But the side reactions of self-corrosion and passive film severely impede the advancement of aluminum batteries. Besides, the ideal matched electrolyte system and cathode working mechanism still need to be explored. Herein, a high specific energy aqueous aluminum–manganese battery is constructed by interfacial modified aluminum anode, high concentration electrolyte and layered manganese dioxide cathode. At the anode, in addition to boosting the wettability of the interface between the electrolyte and aluminum electrode, the altered surface of aluminum anode can also retard side reactions. At the same time, high concentration electrolyte (5 mol L⁻¹ Al(OTF)₃) with a broad electrochemical window allows the battery system to attain a specific capacity of 452 mAh g⁻¹ at 50 mA g⁻¹ and an energy density of 542 Wh kg⁻¹, with greatly increased cycle stability. At the cathode, the mechanism investigation reveals that δ-MnO₂ is reduced to soluble Mn²⁺ during the first cycle discharge, whereas Al_xMn_(1−x)O₂ generates during the charging process, acting as a highly reversible active material in the succeeding cycle. This comprehensive study paves the way for the development of aluminum-based energy storage devices.

In recent years, researchers have increasingly sought batteries as an efficient and cost-effective solution for energy storage and supply, owing to their high energy density, low cost, and environmental resilience. However, the issue of dendrite growth has emerged as a significant obstacle in battery development. Excessive dendrite growth during charging and discharging processes can lead to battery short-circuiting, degradation of electrochemical performance, reduced cycle life, and abnormal exothermic events. Consequently, understanding the dendrite growth process has become a key challenge for researchers. In this study, we investigated dendrite growth mechanisms in batteries using a combined machine learning approach, specifically a two-dimensional artificial convolutional neural network (CNN) model, along with computational methods. We developed two distinct computer models to predict dendrite growth in batteries. The CNN-1 model employs standard CNN techniques for dendritic growth prediction, while CNN-2 integrates additional physical parameters to enhance model robustness. Our results demonstrate that CNN-2 significantly enhances prediction accuracy, offering deeper insights into the impact of physical factors on dendritic growth. This improved model effectively captures the dynamic nature of dendrite formation, exhibiting high accuracy and sensitivity. These findings contribute to the advancement of safer and more reliable energy storage systems.

Cobalt nickel sulfide (Ni-Co-S), a typical bimetallic sulfide, is regarded as a promising electrode material for supercapacitors (SCs). In this study, the electrodeposition process is employed to grow vertically aligned Ni-Co-S nanosheets on a carbon film (CF) substrate derived from cotton fabrics. The conductive and porous CF film not only ensures the uniform distribution of Ni-Co-S nanosheets but also offers an efficient pathway for the transportation of electrons and electrolyte ions. The Ni-Co-S nanosheet arrays, characterized by their small thickness and open pores, facilitate to provide a rapid diffusion path for electrolyte ions and expose sufficient active surfaces for charge storage. The synergistic effect resulting from the rational combination of Ni-Co-S nanosheets and the CF film substrate endows the film electrode with a high areal capacitance of 1800 mF cm⁻² at 2 mV s⁻¹ and remarkable mechanical flexibility. Furthermore, when an all-solid-state asymmetric SC device is assembled, a high energy density of 324.1 mWh cm⁻² is achieved at a power density of 2252.4 mW cm⁻².

Layered sodium oxides are considered one of the most promising cathode materials for Na-ion batteries. In article number BTE.70000, Jiming Peng, Youguo Huang, and Sijiang Hu reported in situ structural and electrochemical methods of studying the effect of using different reagents for synthesizing these oxides. The samples synthesized via MnCO₃-based precursors form the Li₂MnO₃ phase at evaluated temperature and perform better than those through MnO₂-based precursors. This study highlights the significance of reagents and milling methods in synthesizing layered oxides and will benefit the broad-scale commercialization of layered sodium oxides.

Ammonia has gained considerable attention as a promising energy carrier due to its high hydrogen content, carbon-free emissions, and ease of storage and transportation compared to hydrogen gas. The electrochemical ammonia oxidation reaction (AOR) is a pivotal process for harnessing ammonia as a sustainable energy source, enabling hydrogen production through ammonia decomposition or electricity generation via direct ammonia fuel cells. NiCu, a transition metal alloy, has shown great potential as an efficient and cost-effective catalyst for AOR. In this study, high-valence Ni and Cu hydroxyl hydroxides were synthesized on nickel foam to form NiCuOOH in the structure of folded nanosheets, serving as an anodic electrocatalyst for AOR. Comprehensive characterization identified high-valence metals as the primary active components. By optimizing the Ni/Cu ratio, the catalyst achieved remarkable performance and stability, reaching a maximum current density of 169 mA cm⁻² at 1.62 V versus RHE, with 0.16 at% Cu delivering high ammonia oxidation activity, and being stable for 48 h at 100 mA cm⁻². Additionally, the catalyst exhibited excellent catalytic activity for the oxygen evolution reaction (OER), attaining a maximum current density of 152 mA cm⁻² at 1.72 V versus RHE. This study presents a cost-effective, high-performance, and easily synthesized bifunctional self-supporting catalyst, offering significant potential for both AOR and OER applications.

Accurate State of Health (SOH) estimation is critical for battery management systems (BMS) in electric vehicles (EVs). However, the absence of a universal aging model for power batteries presents significant challenges. This study leverages the open-source battery cell data set from the University of Maryland and focuses on private battery packs to address the aging model SOH estimation. Two aging features indicative of capacity degradation are extracted from constant current charging data using incremental capacity analysis (ICA). To handle nonlinearity and feature coupling, a flexible data-driven aging model is proposed, employing dual Gaussian process regressions (GPRs) and transfer learning to enhance model efficiency and accuracy. Adaptive filtering via the Particle filter (PF) further refines the model by integrating aging features and output capacity, resulting in a closed-loop data fusion approach for precise SOH estimation. Battery pack aging experiments validate the proposed method, demonstrating that transfer learning effectively improves estimation accuracy. The proposed method achieves closed-loop SOH estimation with a mean root mean square error (RMSE) of 0.87, underscoring its reliability and precision.

Overdischarge is one of the potential factors that affect the performance and safety of lithium-ion batteries (LIBs) during application. In this study, the aging behavior and thermal safety of LIBs at different overdischarge cut-off voltages are investigated. The results show that overdischarge significantly affects the discharge ability of the battery, with a capacity decay rate of 38.2% at an overdischarge cut-off voltage is 0.5 V. Electrochemical test results indicate that overdischarge accelerates the loss of the active materials and the increase of impedance. Quantitative analysis shows that the conductive loss and lithium inventory loss are the main causes of battery aging. The disassembly images and further physicochemical characterization indicate that with the decrease of overdischarge voltage, the dissolution of copper current collector and the increase of electrode surface attachments intensify. The differential scanning calorimetry test indicates that the thermal stability of the anode is reduced. These aging behaviors lead to the loss of active materials, the damage of the electrode structure, and the increase of gas production inside the overdischarge batteries, which results in the advance of the thermal runaway time, the decrease of the thermal runaway onset temperature and the thermal runaway peak temperature.

Pitch is a promising precursor for preparing carbon materials for anode of sodium-ion batteries. Heteroatom doping is an effective way to increase the sodium storage capacity while constructing reasonable pores and nanosizing the carbon skeleton help to achieve a high-rate performance of anodes. In this work, sulfur-doped carbon nanofibers with lotus root-like axial pores were prepared using coal liquefaction pitch as the main precursor by electrospinning, pre-oxidation, sulfurization, and carbonization. A considerable content of 7.41 wt.% of sulfur was doped into the carbon skeleton after low-temperature gas-phase sulfurization and subsequent carbonization. The as-prepared sulfur-doped porous carbon nanofiber films, used as self-supporting electrodes of sodium-ion batteries, display high specific capacity (528.5 mAh g⁻¹ at 25 mA g⁻¹), high-rate performance (209.3 mAh g⁻¹ at 500 mA g⁻¹) and exceptional cycling stability (96.97% of retention at 500 mA g⁻¹ over 1000 cycles). With desirable flexibility and excellent sodium storage performance, the achieved sulfur-doped porous carbon nanofibers hold great promise for potential applications as self-supporting anodes of sodium-ion batteries.

The bonding across the lattice and ordered structures endow crystals with unique symmetry and determine their macroscopic properties. Crystals with unique properties such as low-dimensional materials, metal-organic frameworks, and defected crystals, in particular, exhibit different structures from bulk crystals and possess exotic physical properties, making them intriguing subjects for investigation. To accurately predict the physical and chemical properties of crystals, it is crucial to consider long-range orders. While GNNs excel at capturing the local environment of atoms in crystals, they often face challenges in effectively capturing longe range interactions due to their limited depth. In this paper, we propose CrysToGraph (Crystals with Transformers on Graph), a transformer-based geometric graph network designed for unconventional crystalline systems, and UnconvBench, a benchmark to evaluate models' predictive performance on multiple categories of crystal materials. CrysToGraph effectively captures short-range interactions with transformer-based graph convolution blocks as well as long-range interactions with graph-wise transformer blocks. CrysToGraph proves its effectiveness in modelling all types of crystal materials in multiple tasks, and moreover, it outperforms most existing methods, achieving new state-of-the-art results on two benchmarks. This work has the potential to accelerate the development of novel crystal materials in various fields, including the anodes, cathodes, and solid-state electrolytes.

This study investigates the advancement of coin cell supercapacitors (SCs) for sustainable, high-performance energy storage by employing biomass-derived date stone activated carbon with various ionic liquid (IL) electrolytes at different temperatures. The research reveals that SCs demonstrate both pseudocapacitive and electrochemical double-layer characteristics. Among the tested ILs, 1-Butyl-3-methylimidazolium trifluoromethanesulfonate (BMIMOTf) emerges as the most effective, achieving an impressive energy density of 129.9 Wh kg⁻¹, a power density of 403.8 W kg⁻¹, and a specific capacitance of 103.9 F g⁻¹ at 0.5 A g⁻¹. After 5000 cycles, the supercapacitor utilizing BMIMOTf maintains 97.3% of its initial capacitance and exhibits a Coulombic efficiency approaching 100%. Additionally, temperature-dependent analyses from room temperature to 50°C reveal that higher temperatures boost the electrochemical performance of the SC, attributed to improved ionic conductivity. This research offers a more comprehensive understanding of how materials and electrolytes interact, emphasizing the capacity of BMIMOTf to foster innovations in eco-friendly energy storage solutions.