Search

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.

Many industrial processes belong to complex thermal system coupled by multiple heat sources, in which the energy distribution and transmission for each heat source is closely related to its adjacent neighbors, and the thermal dynamics have obvious nonlinear time-series characteristics, these factors limit the modeling and predict accuracy for this type of systems in actual practice. In this paper, a convolution and memory network-based spatiotemporal modeling approach is proposed to model the nonlinear spatial features and temporal dynamics of these heating systems. First, a deformation convolutional neural network (D-CNN) strategy is developed to extract the spatial features, it designs a deformable kernel matrix for convolution layer to obtain the spatial multi-scale context information of the DPS, and the feature order is reduced by pooling operation. Then, a cascade long-short time memory network (LSTM) model is constructed to model the temporal dynamics of the time-series data. In this model, the sliding window method is used to obtain the sequential dataset for several sampling time, which is taken as the input of each LSTM unit to reconstruct the nonlinear temporal dynamics in this type of systems. The effect of the proposed model for spatiotemporal dynamics is verified by the experiment of serial-connected lithium batteries (S-LIBs), and the result shows that the proposed model has smaller modeling errors for spatiotemporal dynamics than the other two methods. • A convolution and memory network-based modeling approach is proposed to model the spatiotemporal dynamics in thermal processes. • A deformation convolutional neural network strategy is developed to extract the spatial features using a deformable kernel matrix. • A cascade long-short time memory network model is constructed for the temporal dynamics of the time-series data. • The effect of the modeling approach is verified by the experiment of serial-connected lithium batteries (S-LIBs). [ABSTRACT FROM AUTHOR]

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]