Ionics最新文献

The battery’s health status is fundamental to battery health management. Accurately estimating the health status of lithium-ion batteries is crucial for ensuring their safe, reliable, and long-term operation. In this paper, a novel method for estimating the health state of lithium-ion batteries, which is based on grey relational analysis (GRA), Savitzky Golay (SG) filter, and Elman neural network enhanced by sparrow search algorithm (SSA). Firstly, multiple representative health features (HFs) are extracted from the charge and discharge curves. In order to reduce computational complexity, the GRA method is employed for feature analysis and screening, resulting in reasonable, highly relevant, and explanatory HFs. Secondly, to improve the correlation by reducing unstable factors in HF curves, SG filter is utilized for noise reduction and data smoothing, effectively mitigating the influence of data noise and short-term fluctuations resulting from capacity regeneration. Thirdly, in order to accurately estimate the state of health (SOH) of lithium-ion batteries, a SOH estimation model based on SSA-Elman neural network is proposed. The neural network characteristics are optimized to effectively mitigate the issue of Elman network being prone to local optima. Finally, the proposed method’s effectiveness is validated by comparing it with several other methods using NASA dataset. The results show that the RMSE and MAE of the model are controlled within 0.0045 and 0.0038 respectively, and the R² is maintained above 99.79%, which significantly improves the accuracy and reliability of SOH estimation.

In the quest for safer energy storage devices, researchers have been diligently exploring solid polymer electrolytes in recent years. This study explores the development of solid biopolymer electrolytes through solution casting, utilizing cellulose acetate blended with various concentration of LiBr. Inclusion of LiBr salt makes the membrane amorphous, confirmed using XRD. Cellulose acetate–LiBr complexation is confirmed by FTIR. The thermal stability of membranes is analyzed using TGA and DSC. From DSC, the low glass transition temperature (51 °C) of the membrane 1 g cellulose acetate with 0.6 wt.% LiBr leads to the flexibility of the membrane which helps to increase the Li-ionic conductivity. Thermal stability of the highest Li-ionic conducting membrane CALB3 indicates that if the battery is constructed with this membrane, it will be thermally stable up to 60 °C. The membrane with composition 1 g cellulose acetate with 0.6 wt.% LiBr is confirmed as the highest Li-ion-conducting membrane with ionic conductivity value 3.16 × 10⁻³ S cm⁻¹ among all prepared membranes. The electrochemical stability window of the highest Li-ion-conducting membrane is 2.2 V given by linear sweep voltammetry. The membrane CALB3 having high Li-ion conductivity is used as electrolyte to construct primary Li-ion battery. The primary Li-ion battery shows an open-circuit voltage of 1.68 V and the performance of battery for various loads has been measured. Rechargeable Li-ion battery (coin cell) is constructed using the high Li-ion-conducting membrane as electrolyte. The constructed coin cell behaves like a pseudocapacitor.

Supercapacitors have emerged as advanced energy storage solutions, characterized by their rapid charge–discharge abilities, elevated power density, and remarkable cycling stability. This study focuses on improving electrochemical performance of proton-conducting alginate-based biopolymer electrolytes (BBPEs) through the addition of glycolic acid (GA) and ethylene carbonate (EC) as plasticizers for potential application in symmetric supercapacitors. Two distinct systems were developed: System I, composed of alginate with GA, and System II, which further includes ethylene carbonate EC as a plasticizer. The plasticized system demonstrated a notable improvement in ionic conductivity, which led to enhanced electrochemical properties, such as a stable potential window of 1.85 V and excellent cycling stability over 10,000 cycles. The fabricated supercapacitors for System II exhibited a specific capacitance of 19.05 F g⁻¹ and energy density of ~ 6.20 Wh kg⁻¹, with a power density of ~ 212 W kg⁻¹. These findings highlight the potential of alginate-based BBPEs for use in sustainable and efficient energy storage applications.

As a new cathode material for sodium-ion batteries, Na_2+2xFe_2-x(SO₄)₃ has attracted significant attention due to its high working voltage and low preparation cost. However, its inherently low conductivity limits its practical application. To address this issue, we synthesized a high-capacity Na_2+2xFe_2-x(SO₄)₃/C/CNTs-S cathode material using ball milling, spray drying, and low-temperature sintering, with ascorbic acid and carbon nanotubes as composite carbon sources, and sodium dodecyl sulfate (SDS) as milling aid. With the introduction of SDS into the ball milling process, the prepared cathode material has uniform primary particle size (40–50 nm in diameter) and regular particle morphology, which improves the dispersion of carbon nanotube powder in the precursor solution and reduces its loss during subsequent synthesis. After spray drying, NFS precursor particles are closely connected to carbon nanotubes, and the effective presence of carbon nanotubes during sintering inhibits the excessive growth of NFS particles and greatly improves the electrochemical performance and conductivity of NFS. The test results showed that the prepared Na_2+2xFe_2-x(SO₄)₃/C/CNTs-S cathode material had a discharge-specific capacity of 101.3 and 78.8 mAh g⁻¹ at 0.05C and 5C, respectively. After 500 cycles at 2C, the capacity retention rate was 94.8%.

Multivalent metal-ion batteries offer a revolutionary solution for large-scale energy storage, utilizing abundant aluminum, zinc, calcium, and magnesium to create cost-effective batteries. The challenge is to develop innovative positive electrode materials that can efficiently transport these ions with an improved diffusion mechanism. In our study, we delve into the atomistic simulation of spinel-structured materials using first-principles calculations and classical molecular dynamic simulations (CMDs). Two promising spinel compounds, ZnM₂O₄, where M represents the transition metal redox elements Mn and Ni, have been theoretically predicted as promising cathode materials for zinc-ion batteries (ZIBs). Their potential in battery technology is explored by precisely calculating fundamental properties such as intercalation–deintercalation voltage, theoretical specific capacity, and ionic dynamics. Zn²⁺ ions are stabilized during diffusion by the Mn³⁺/Mn⁴⁺ redox pair, improving overall electrochemical performance. However, the Ni³⁺/Ni⁴⁺ pair finds it challenging to stabilize Zn²⁺, leading to greater voltages but less effective ionic diffusion, a notable distinction that opens up new possibilities for Mn-based materials. CMDs allow us to simulate ionic behavior at various temperatures, revealing how thermal vibrations and lattice dynamics influence ionic migration. Through these simulations, we investigate the diffusion kinetics of Zn²⁺ ions in these materials, discovering that ZnMn₂O₄ exhibits superior diffusion kinetics compared to ZnNi₂O₄. Our findings highlight that the combination of MD simulations and defect engineering provides a powerful toolkit for predicting and enhancing the performance of battery materials. Strategically lowering the energy barriers improves the intercalation properties of spinel compounds, paving the way for efficient multivalent metal-ion batteries.

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In this paper, functional ether-based ionic liquids with bromide and dicyanamide anions were prepared and characterized. The introduction of ether group into imidazolium cation decreased the relative viscosity of ionic liquids, while increased density and electrical conductivity values of ionic liquids. The refractive index, density, thermal stability, electrical conductivity, and ESW values of ionic liquids decreased with the increase of alkoxyalkyl chain length from methoxyethyl to methoxypropyl, while the relative viscosity of ionic liquid increased from the methoxyethyl to the methoxypropyl group. The refractive index, relative viscosity, density, and thermal stability of bromide ionic liquids were higher than those of dicyanamide ionic liquids, while the electrical conductivity and ESW values of dicyanamide ionic liquids were higher than those of bromide ionic liquids. DFT simulation results demonstrated the introduction of ether group into imidazolium cation increased anion-cation interactions of ionic liquids, resulting as lower HOMO–LUMO (E_gap), lower interaction energy (ΔE), and shorter average hydrogen bond length values. The anion-cation interactions among ionic liquids decreased when ether group changed from methoxyethyl to methoxypropyl. The cation–anion interactions of dicyanamide ionic liquids were much stronger than those of bromide ionic liquids. These results will shed light on the design and preparation of functional ionic liquid electrolytes for the renewable energy storage devices.

LiNi_0.5Mn_1.5O₄ cathode materials have the advantages of high potential, high specific energy, and low cost, but poor structural stability and severe surface side reactions hinder their industrial applications. In this study, a series of LiNi_0.5Mn_1.5-xTi_xO₄ (x = 0, 0.01, 0.03, and 0.05) materials with monocrystal morphology are controllably synthesized using a solid-state method by replacing Mn with trace Ti atoms. XRD, SEM, TEM, Raman, XPS, and electrochemical tests show that the phase component, structure, planes of the octahedral monocrystal, and Mn³⁺ ionic behavior can be effectively regulated to improve their cycle life and elevated-temperature stability. The optimized LiNi_0.5Mn_1.47Ti_0.03O₄ monocrystal cathode material possesses a disordered Fd-3m space group structure, a distinctive chamfered octahedral morphology with the {100} planes, a stable solid-electrolyte interphase film, and a fast Li⁺ ion/electron transport property. As a result, the material can release an initial discharge specific capacity of about 134.88 mAh g⁻¹ at 0.2 C with a coulombic efficiency of 89.4%, capacity retention of 79.5% after 1000 cycles at 25 ℃, and 80.0% after 200 cycles at 55 °C and 1 C and also good rate performance.

Graphical abstract

The progress of energy storage technology crucially depends on the availability of high-performance lithium-ion batteries (LIBs). As a silicon-based composite material, silicon oxide (SiO) exhibits significant theoretical specific capacity and mitigates the volume expansion of pure silicon. However, poor electronic conductivity remains a significant issue, limiting the performance of LIBs. In this study, SiO@SiO₂ composites were synthesized by applying a silane coupling agent as the silicon source to coat silicon oxides onto the surface of micrometer-sized SiO particles using an in situ coating technique within a liquid-phase system. This approach aims to address the problems of volume expansion and stability, thereby enhancing the performance of LIBs. The silicon oxide core provides high capacity, whereas the silica shell serves as a protective layer. The SiO₂ shell, with its greater rigidity compared to a carbon shell, better inhibits volume expansion, thereby extending the battery’s service life. The results showed that when the mass of the silane coupling agent (SCA) was 15% of the mass of the SiO particles, the initial specific capacity of SiO@SiO₂-15 composites reached 2160.62 mAh·g⁻¹, with the highest first coulombic efficiency (70.06%). Additionally, the composites exhibited the highest reversible capacity (1345.54 mAh·g⁻¹) and a capacity retention of 62.28% after 100 cycles.

To ensure the safe operation and optimal performance of lithium battery systems, accurately determining the state of health (SOH) of the batteries is crucial. Research over the past few decades has shown that techniques based on electrochemical impedance spectroscopy (EIS) offer some advantages over traditional methods relying on voltage, current, and temperature. In this paper, we propose a novel approach for assessing the SOH of lithium-ion batteries using a CNN-BiLSTM-Attention model. By combining the effectiveness of bidirectional long short-term memory (BiLSTM) neural networks, known for their efficiency in long sequence prediction, with convolutional neural networks (CNN) capable of automatically extracting EIS features, we create a unique CNN-BiLSTM model. Additionally, an attention mechanism is incorporated to enhance the model’s accuracy and processing speed. This approach enables faster and more effective feature extraction while minimizing information loss from historical data. Experimental results demonstrate that the proposed model achieves higher estimation accuracy compared to other popular data-driven methods. When compared to the benchmark BiLSTM and CNN-BiLSTM models, the AC-BiLSTM model reduces the root mean squared error (RMSE) by 93.9% and 71.4%, respectively. These findings highlight the significant practical value of the proposed approach.