Ionics最新文献

Nickel manganese cobalt oxide (NMC) lithium-ion batteries are widely used in electric vehicles due to their high energy density and long lifespan. Accurate state of charge (SOC) estimation is crucial for improving battery performance and efficiency. This paper models the battery using a 2-RC equivalent circuit and evaluates three SOC estimation methods: particle filter (PF), Ensemble Kalman-Bucy filter (EnKBF), and deterministic ensemble Kalman-Bucy filter (DEnKBF). The results show that DEnKBF achieves a mean absolute error (MAE) of 1.6 × 10⁻³ and a root mean square error (RMSE) of 1.8 × 10⁻³, while EnKBF achieves a slightly lower MAE of 1.5 × 10⁻³ with the same RMSE. In contrast, PF demonstrates higher errors, with a MAE of 4.3 × 10⁻³ and an RMSE of 4.8 × 10⁻³, indicating lower accuracy. Furthermore, the performance of EnKBF and DEnKBF improves at higher temperatures, with DEnKBF achieving a MAE of 6.9 × 10⁻⁴ and an RMSE of 1.2 × 10^–3 at 50 °C, compared to 2.23 × 10⁻³ and 2.41 × 10⁻³, respectively, at − 5 °C. Similarly, EnKBF achieves a MAE of 9.3 × 10⁻⁴ and an RMSE of 1.06 × 10⁻³ at 50 °C, improving from 3.66 × 10⁻³ to 3.86 × 10⁻³ at − 5 °C. Computationally, DEnKBF and EnKBF exhibit efficient performance with execution times of approximately 0.0126 ms and 0.0136 ms per cycle, respectively, compared to the PF method, which requires 0.0482 ms per cycle. This work introduces the novelty of using the ensemble Kalman-Bucy filter (EnKBF) and deterministic ensemble Kalman-Bucy filter (DEnKBF) for SOC estimation, achieving superior accuracy and efficiency over the particle filter (PF). These methods offer a robust and practical solution for real-time battery management in electric vehicles.

A facile method to synthesize β-MnO₂ micro-particles under low temperature without templates or catalysts was reported in the study. The products were characterized by power X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared spectrum (FTIR), AC impedance, and cyclic voltammetry measurement (CV). Compared to the commercial Xiangtan electrolytic manganese dioxide (XT-EMD, China), the prepared β-MnO₂ had better crystallinity and nanorod structure, and higher rate performance. The prepared β-MnO₂ and the commercial XT-EMD were used as cathode active materials, respectively, and assembled into CR2032 batteries for discharge performance characterization. The AC impedance analysis of the battery shows that the diffusion coefficient of lithium ions on prepared β-MnO₂ cathode is 29.73% higher than that of XT-EMD cathode. The service life of the prepared β-MnO₂ as cathode was 5.14%, 8.01%, 6.48%, and 11.23% higher than those of the commercial XT-EMD with high-temperature treatment at 15, 8.25, 3, and 1 kΩ constant load discharge, respectively, showing the good rate performance of the prepared β-MnO₂ as cathode active material of Li–MnO₂ battery. At the same time, a small amount of multilayer graphene was doped in cathode, which was benefit for the third-stage discharge performance of the Li–MnO₂ primary battery. And the glass fiber separator was more suitable for the separator of the Li–MnO₂ primary battery and improved the discharge performance of the second stage of discharge.

Novel NiCo₂S₄ nanorod arrays are uniformly grown on carbon nanofibers (NiCo₂S₄@CNF) through a facile hydrothermal approach. The elaborate designed composite structure ensures that the NiCo₂S₄ nanorods arrays are uniformly dispersed on the surfaces of carbon nanofibers (CNF) and tightly bonded with each other. Conductive networks of CNF facilitate the electron transport at the interfaces to readily react with NiCo₂S₄, thereby enhancing sodium storage. In view of this, NiCo₂S₄@CNF exhibits a high reversible capacity (683.6 mAh g⁻¹ at 0.1 A g⁻¹) and excellent long-term cycling stability (with only a 0.07% capacity loss per cycle after 400 cycles). This work provides a simple and efficient strategy for synthesizing high-performance sodium-ion battery electrodes.

Impedance spectroscopy was used to study proton conductivity changing of pre-dried sulfonated perfluorinated and hydrocarbon (polynaphtoleimide) membranes over time during their hydration. For this purpose, commercial perfluorinated membranes Nafion 212, Nafion 211, Gore 18 and hydrocarbon co-polynaphthoyleneimide (co-PNIS) membranes with different hydrophobicity blocks (ODAS/MDAC and ODAS/MDOT) synthesized in this work were used. It was found that the proton conductivity steady state values of Gore 18 membrane were established faster in humidity atmosphere after its drying than for other membranes studied in this work. This membrane demonstrates also better characteristics with cyclic humidity changes from 35 to 75% (sorption) and from 75 to 35% (desorption), which actually simulates start operation of PEMFC after its storage at the normal conditions. It was found that Nafion type membranes possess more universal hydration characteristics PEMFC in whole investigated humidity values interval and reveal consistently both high proton transport values and proton conductivity recovery kinetics parameters from dry state. However, the proton conductivity highest level at RH > 75% is demonstrated by fluorine-free co-PNIS membranes, which is also associated with its higher hydration degree compared to perfluorinated analogues. Moreover, it has been observed that the hydrocarbon co-PNIS (ODAS/MDOT) membrane, with a large difference in the hydrophobicity degree between polymer blocks is characterized by both higher protonic conductivity and its recovery kinetics parameters compared to the hydrocarbon analogue co-PNIS (ODAS/MDAC). The effect of the hydrophobic block on proton transport in co-PNIS membrane is discussed.

Zinc-nickel secondary batteries are characterized by environmental protection, safety, low cost, and high specific energy, and the rich content and high energy density of zinc negative electrodes make it a promising electrochemical energy storage device. However, due to zinc dendrite, deformation, passivation, hydrogen precipitation corrosion, and other problems generated by zinc negative electrodes, the cycle life and stability are still the challenges for zinc-nickel batteries. Many solutions have been proposed to address the above problems, such as optimization of anode components, design of zinc structure and morphology, and use of layered double hydroxide (double oxide). This paper briefly introduces the research progress of anode materials for zinc-nickel secondary batteries, focuses on the deterioration mechanism of zinc anode, the challenges, and solutions, and discusses the development direction and research trend of zinc anode.

Few layered graphene has more platelet-like structures. This causes more stacking and aggregation of graphene sheets. Silver nanoparticles are intercalated between these sheets. Silver nanoparticles are able to tailor the physical and electrochemical properties of graphene. The graphene/silver nanoparticle composite is synthesized using a high-temperature solid-state synthesis technique with tartaric acid as the activating agent. The nanocomposite is characterized by XRD, UV–visible analysis, FTIR, SEM, and cyclic voltammetry. The formation of the graphene/silver nanocomposite is confirmed by the XRD spectrum. Peaks at 290 and 450 nm in the UV–visible spectrum of the graphene/silver nanocomposite indicate the plasmonic properties of both constituent materials. The intercalation of spherical particles in between the two-dimensional sheets is clearly observed from SEM images. The silver nanoparticles are well-intercalated within the graphene matrix and exhibit excellent electrochemical performance. The electrochemical measurements confirm the feasibility of the obtained nanocomposite to fabricate the electrodes for energy storage devices. The cyclic voltammetry (CV) and galvanostatic charge–discharge (GCD), electrochemical impedance spectroscopy (EIS), and the cyclic stability are obtained through the electrochemical test. The highest specific capacitance obtained for graphene/silver composite is 851.68 F/g.

Zinc ion batteries (ZIBs) have attracted considerable attention owing to their inherent safety and environmental friendliness. As a cathode material for ZIBs, MnO₂ possesses the dissolution and volumetric expansion issues. Herein, to address these problems, an easy-obtained conductive carbon black (CCB) and a simple pyrolysis approach have been utilized to fabricate a novel δ-MnO₂/conductive carbon black (δ-MnO₂/CCB) composite. Notably, the introduction of CCB can improve the conductivity of MnO₂, and hence resulting in superior electrochemical performance. The reversible capacity of δ-MnO₂/CCB is 263.9 mAh g⁻¹ at 0.1 A g⁻¹. The δ-MnO₂/CCB composite also remains at a high capacity of 145.2 mAh g⁻¹ after 500 cycles at 0.2 A g⁻¹. Given its simplicity, the preparation method can serve as a valuable reference for synthesizing other MnO₂/C composite materials.

To achieve high density and high Li-ion conductivity in the garnet LLZO (Li₇La₃Zr₂O₁₂), an effective strategy is to dope it with high-valence elements to stabilize the cubic phase and to lower the sintering temperature. As well known, flash sintering (FS) is characterized by short sintering times and low furnace temperature. Herein, the doping modification of Ta-LLZO by flash sintering at 700 °C for the higher conductivity of LLZO was investigated in the present study. The results indicate that the addition of an appropriate amount of Ta⁵⁺ can enhance the ionic conductivity. The relative density of the sintered cubic Li_6.4La₃Zr_1.4Ta_0.6O₁₂ is 94.72%, with a corresponding total ion conductivity of 9.8 × 10^–4 S cm⁻¹, demonstrating good electrical performance. A solid-state lithium metal battery with Li_6.4La₃Zr_1.4Ta_0.6O₁₂ electrolyte was assembled, and its electrical performance was tested at 50 °C. The results showed that an initial discharge specific capacity of 126.5 mAh g⁻¹ at a 0.1C rate could be reached.

This study explores two energy storage materials: cobalt-doped P2-type Na_0.67MnO₂ (Na_0.67Mn_0.9Co_0.1O₂, NMCO) and hard carbon derived from purple basil (Ocimum basilicum L., HC-based PB) biomass. NMCO was synthesized via a solid-state method involving high-temperature quenching in liquid nitrogen (LN₂). Analytical techniques confirmed a pure P2-type layered structure with reduced lattice volume due to Co³⁺ substitution. FTIR identified Na–O, Mn–O, and Co–O bonds, while XPS revealed reduced Mn³⁺ content, enhancing structural stability by mitigating the Jahn–Teller effect. Electrochemical tests of NMCO showed charge/discharge capacities of 184 mAhg⁻¹ and 185 mAhg⁻¹ with a coulombic efficiency of 99.5%. HC-based PB, exhibiting disordered graphitic structures, demonstrated higher charge and discharge capacities of 231 and 349 mAhg⁻¹, respectively, despite a relatively low efficiency of 66%. Long-term cycling demonstrated capacity fading for both materials after 100 cycles. Ex situ XRD confirmed NMCO’s structural integrity, while HC’s amorphous structure contributed to its stability. These findings provide valuable insights into these materials’ electrochemical performance and durability for energy storage applications.

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is crucial to optimize energy storage performance and safety. A data-driven prediction method based on a multilayer optimization fusion deep network was proposed in this investigation. Firstly, interested feature information referred to lithium-ion battery’s life was identified by Pearson correlation coefficient analysis. Initial battery capacity data was then decomposed into multiple modal components through variational modal decomposition (VMD), and the particle swarm optimization (PSO) algorithm was adopted to optimize these modal sequences where the minimum average envelope entropy was applied as the fitness function. The extracted features and decomposed, optimized modal sequences were fused and introduced to a multi-channel parallel CNN-BiLSTM deep network for RUL prediction. Additionally, an attention mechanism (AM) was integrated into BiLSTM layer to improve its capacity to capture important information more effectively and efficiently. Finally, the NASA and CALCE datasets were employed to validate the proposed model by experimental study. In comparison with the CNN-BiLSTM-AM and PSO-VMD-CNN-BiLSTM models, the proposed model demonstrated superior precision. Furthermore, by comparison with general models such as CNN, LSTM, and GRU, the proposed model supplied robust generalization capabilities, which showed high potential to extended application in engineering practice.