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Lithium batteries are crucial for energy storage in electronics, transportation, and industrial sectors. However, lithium-ion battery separators such as Celgard require significant improvements, particularly in ionic conductivity (?). Combining Metal-Organic Frameworks (MOFs) with polymers is expected to create a separator membrane that enhances conductivity and mechanical properties in lithium-ion batteries. UiO-66 MOFs were synthesized using the solvothermal method at 120? and then composited with polyethylene oxide (PEO) and polyvinyl alcohol (PVA) polymer membranes using the solution casting method. The UiO-66 MOFs/PEO/PVA polymer composites were made by varying the UiO-66 content from 2% to 8% (w/w) while keeping a constant LiPF6 concentration of 9% (w/w). These composites were characterized using X-ray Diffraction (XRD) and Fourier-Transform Infrared spectroscopy (FTIR). Subsequently, the EIS test and tensile tests assessed the performance of the composite membranes. The resulting membrane with 6% (w/w) UiO-66 MOFs exhibited a conductivity (?) of 5.60 × 10–3 S cm–1 and a tensile strength of 32.5 MPa.

This paper proposed an interval prediction strategy for lithium-ion battery remaining useful life (RUL) based on fuzzy information granulation and linguistic description to solve the limitations on current numerical prediction strategies. Firstly, the fuzzy information granulation is introduced to process the time series of battery capacity degradation and then the original numerical level data is treated as granular level, which is the basic of achieving interval prediction. Secondly, in order to solve the problem of fluctuation information loss caused by fuzzy granulation when processing battery degradation data, this paper creatively introduced a linguistic description method to attach semantic label for each granule to represent the fluctuation characteristics. Then, combined with the least square support vector machine, the granules with linguistic labels are used for modeling, by which the fluctuation characteristics of degradation sequence are considered while implementing the RUL interval prediction. Finally, four groups of NASA battery aging data were used for the experiment, and the interval prediction evaluation criterion P was introduced to evaluate the RUL interval prediction performance. Compared with the model without linguistic description, the P % of the model with linguistic description is improved by 32% on average. • An interval prediction strategy for lithium-ion battery RUL is proposed. • Transforming the research object from numerical level into granular level. • Labeling the fluctuation information of capacity by linguistic description. • Effectiveness of the proposed interval prediction strategy is verified. [ABSTRACT FROM AUTHOR]

The effect of separator pore size on lithium dendrite growth is assessed through the use of the phase field method (PFM). Dendrites are found to undergo concurrent electrodeposition and electrodissolution that define their local growth or shrinkage. Moreover, dendrites are observed to detach due to localized electrodissolution and generate metallic debris that is detrimental to battery performance. A critical current density exists below which dendrites are fully suppressed. An analytical model based on the performed PFM simulations allows to formulate the critical current density as a function of separator morphology and pore radius. Four distinct regimes of dendrite growth are identified: (i) the suppression regime , where dendrite growth is thermodynamically unfavorable; (ii) the permeable regime , where dendrite growth is prohibited beyond the first layer of the separator; (iii) the penetration regime , in which dendrites are stable within the channels of the separator; and (iv) the short circuit regime , where dendrites penetrate the entire width of the separator causing a short circuit. The identification of these regimes serve as a guideline to design improved separators. [ABSTRACT FROM AUTHOR]