Battery Energy最新文献

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.

Fluorides are commonly regarded as interfacial additives that enhance the electrochemical stability of solid-state battery electrolytes. In this study, we synthesized lithium borate glassy solid electrolytes and investigated the effect of adding aluminum fluoride (AlF₃) on its stability against lithium metal electrodes. Samples maintained their amorphous nature, with up to 9.20 wt.% of fluorine in the glass. Lithium borate glasses, with and without AlF₃, demonstrated an excellent electrochemical performance, sustaining a stable lithium voltage profile at current densities from 0.01 to 1 mA cm⁻² at 160°C. Notably, the lithium borate glass with the highest lithium ion content achieved the highest relative ionic conductivity and cycled stably for up to 500 h at current densities of 1 mA cm⁻² at 160°C in symmetric LiǀglassǀLi cells. However, the addition of AlF₃ to lithium borate glass significantly compromises its electrochemical stability. In long-term symmetrical cell tests, the AlF₃-containing lithium borate glass exhibited short-circuiting under 0.3 mA cm⁻², revealing unexpectedly poor stability. These findings offer valuable insights for evaluating the impact of fluorine incorporation on the performance of solid-state battery electrolytes.

Lithium batteries constitute a pivotal component in electric vehicles (EVs) owing to their rechargeability and high-power output capabilities. Despite their advantageous features, these batteries encounter longevity challenges, posing disposal complications and an insufficient sustainable supply chain ecosystem to address the growing demand for lithium batteries. One potential solution to address this issue is the implementation of a circular economy model. This study aims to identify and assess the key barriers to optimizing a sustainable supply chain in the lithium-ion battery circular economy using an integrated Gray Multi-Criteria Decision Making approach within the automotive sector. The novelty of this research lies in its application of Gray Possibility Comparison and Gray Possibility of degree to address these uncertainties. By integrating Gray DEMATEL (Decision Making Trial and Evaluation Laboratory) and Gray ANP (Analytic Network Process) methods, this study offers a more flexible and adaptive framework for identifying and analyzing the interrelationships among barriers. The research process involves validating the identified barriers through the Gray Delphi method, followed by the application of Gray DEMATEL to establish the cause-effect relationships among the barriers. Finally, Gray ANP is used to assign weights and prioritize the barriers into primary and secondary categories. The results indicate that the barrier “Lack of supportive policies and standards” holds the highest importance and influence, with a weight of 0.101225.

Silicon-based anodes are among the most appealing possibilities for high-capacity anode materials, considering that they possess a high theoretical capacity. However, the significant volumetric changes during cycling lead to rapid capacity degradation, hindering their commercial application in high-energy density lithium-ion batteries (LIBs). This research introduces a novel organic-inorganic cross-linked binder system: sodium alginate-lithium borate-boric acid (Alg-LBO-BA). This three-dimensional network structure effectively buffers the volumetric changes of Si particles, maintaining overall electrode stability. LBO serves as prelithiation agent, compensating for irreversible lithium consumption during SEI formation, and the Si−O−B structure offers a plethora of Lewis acid sites, enhancing lithium-ion transport and interfacial stability. At a current activation of 0.2 A g⁻¹, the optimized silicon anode shows an initial coulombic efficiency (ICE) of 91%. After 200 cycles at 1 A g⁻¹, it retains a reversible capacity of 1631.8 mAh g⁻¹ and achieves 1768.0 mAh g⁻¹ at a high current density of 5 A g⁻¹. This study presents a novel approach to designing organic-inorganic binders for silicon anodes, significantly advancing the development of high-performance silicon anodes.

Two-dimensional (2D) lead halide perovskites (LHPs) have captured a range of interest for the advancement of state-of-the-art optoelectronic devices, highly efficient solar cells, next-generation energy harvesting technologies owing to their hydrophobic nature, layered configuration, and remarkable chemical/environmental stabilities. These 2D LHPs have been categorized into the Dion-Jacobson (DJ) and Ruddlesden-Popper (RP) systems based on their layered configuration respectively. To efficiently classify the RP and DJ phases synthetically and reduce reliance on trial/error method, machine learning (ML) techniques needs to develop. Herein, this work effectively identifies RP and DJ phases of 2D LHPs by implementing various ML models. ML models were trained on 264 experimental data set using 10-fold stratified cross-validation, hyperparameter optimization with Optuna, and Shapley Additive Explanations (SHAP) were employed. The stacking classifier efficiently classified RP and DJ phases, demonstrating a minimal variation between the sensitivity and specificity and achieved a high Balance Accuracy (BA) of (0.83) on independent test data set. Our best model tested on 17 hybrid 2D LHPs and three experimental synthesized 2D LHPs aligns well experimental outcomes, a significant advance in cutting edge ML models. Thus, this proposed study has unlocked a new route toward the rational classification of RP and DJ phases of 2D LHPs.