Temperature is considered to be an important indicator that affects the capacity of a lithium ion batteries. Therefore, it is of great significance to study the relationship between the capacity and temperature of lithium ion batteries with different anodes. In this study, the single battery is used as the research object to simulate the temperature environment during the actual use of the power battery, and conduct a charge and discharge comparison test for lithium iron phosphate battery, lithium manganate battery and lithium cobalt oxide battery. In the test of capacity characteristics of lithium ion batteries of three different cathode materials at different temperatures, the optimal operating temperature range of the lithium ion battery is extracted from the discharge efficiencies obtained. According to the research results, the discharge capacity of a lithium ion battery can be approximated by a cubic polynomial of temperature. The optimal operating temperature of lithium ion battery is 20–50 °C within 1 s, as time increases, the direct current (DC) internal resistance of the battery increases and the slope becomes smaller. Between 1 s and 10 s, the DC internal resistance of the battery basically shows a linear relationship with time. In the charge and discharge process, when state of charge (SOC) 0% and SOC 100%, the internal resistance of the battery is the largest. The SOC has the greatest impact on the polarization internal resistance, and the smallest impact on the ohmic internal resistance.

The battery thermal management system is a key skill that has been widely used in power battery cooling and preheating. It can ensure that the power battery operates safely and stably at a suitable temperature. In this article, we summarize mainly summarizes the current situation for the research on the thermal management system of power battery, comprehensively compares and analyzes four kinds of cooling systems including air cooling, liquid cooling, phase-change materials and heat pipe, two types of heating systems including internal heating and external heating, and the corresponding characteristics of the coupled system in no less than two ways. It is found that liquid cooling system and its heating system, phase-change material cooling system and it is heating system, heat pipe cooling system, coupling cooling system and its heating system have great research prospects, it also provides a certain reference for future research directions.

Remaining useful life prediction is of great significance for battery safety and maintenance. The remaining useful life prediction method, based on a physical model, has wide applicability and high prediction accuracy, which is the research hotspot of the next generation battery life prediction method. In this study, the prediction methods of battery life were compared and analyzed, and the prediction methods based on the physical model were summarized. The prediction methods were classified according to their different characteristics including the electrochemical model, equivalent circuit model, and empirical model. By analyzing the emphasis of electrochemical process simplification, different electrochemical models were classified including the P2D model, SP model, and electrochemical fusion model. The equivalent circuit model was divided into the Rint model, Thevenin model, PNGV model, and RC model for the change of electronic components in the model. According to the different mathematical expressions of constructing the empirical model, it can be divided into the exponential model, polynomial model, exponential and polynomial mixed model, and capacity degradation model. Through the collocation of different filtering methods, the different efficiency of the models is described in detail. The research progress of various prediction methods as well as the changes and characteristics of traditional models were compared and analyzed, and the future development of battery life prediction methods was prospected.

The variation of substrate concentration in anode chamber directly affects the power generation efficiency and decontamination performance of microbial fuel cell (MFC). In this study, three concentrations of swine wastewater with 800 mg/L, 1600 mg/L and 2500 mg/L were selected as substrates, and the performance of MFC and response characteristics of anode microbial community were investigated. The results show that the concentration of a selected substrate is positively correlated with the output voltage of MFC and chemical oxygen demand (COD) removal rate. The microbial community diversity in the anode chamber and the performance of battery can be significantly affected when concentration changes in different ways, which helps to selectively cultivate the adaptable dominant bacteria to enhance the stability and decontamination performance of MFC. The community structure of anodic biofilm is mainly composed of Proteobacteria, Bacteroidetes, Firmicutes, Chloroflexi and Spirochaetae. These findings are meaningful to improve the treatment effects of swine wastewater and can help to find out the mechanism of varying concentration that influences the production of microorganisms in MFC.

Rice, a staple food crop worldwide, is pivotal in agricultural productivity and public health. Automatic classification of typical rice pests and diseases is crucial for optimizing rice yield and quality in practical production. However, infrequent occurrences of specific pests and diseases lead to uneven dataset samples and similar early-stage symptoms, posing challenges for effective identification methods. In this study, we employ four image enhancement techniques—flipping, modifying saturation, modifying contrast, and adding blur—to balance dataset samples throughout the classification process. Simultaneously, we enhance the basic RepVGG model by incorporating the ECA attention mechanism within the Block and after the Head, resulting in the proposal of a new classification model, RepVGG_ECA. The model successfully classifies six categories: five types of typical pests and diseases, along with healthy rice plants, achieving a classification accuracy of 97.06%, outperforming ResNet34, ResNeXt50, Shufflenet V2, and the basic RepVGG by 1.85%, 1.18%, 3.39%, and 1.09%, respectively. Furthermore, the ablation study demonstrates that optimal classification results are attained by integrating the ECA attention mechanism after the Head and within the Block of RepVGG. As a result, the classification method presented in this study provides a valuable reference for identifying typical rice pests and diseases.

Polytetrafluoroethylene (PTFE) is polymerized by tetrafluoroethylene, which has high corrosion resistance, self-lubrication and high temperature resistance. However, due to the large expansion coefficient, high temperature will gradually weaken the intermolecular bonding force of PTFE, which will lead to the enhancement of permeation absorption and the limitation of the application range of fluoroplastics. In order to improve the performance of PTFE, the modified polytetrafluoroethylene, filled by carbon materials and aramid fiber with different scales, is prepared through the compression and sintering. Moreover, the mechanical properties and wear resistance of the prepared composite materials are tested. In addition, the influence of different types of filler materials and contents on the properties of PTFE is studied. According to the experiment results, the addition of carbon fibers with different scales reduces the tensile and impact properties of the composite materials, but the elastic modulus and wear resistance are significantly improved. Among them, the wear rate of 7 μm carbon fiber modified PTFE has decreased by 70%, and the elastic modulus has increased by 70%. The addition of aramid fiber filler significantly reduces the tensile and impact properties of the composite, but its elastic modulus and wear resistance are significantly improved. Among them, the wear rate of the modified composite material with 3% alumina particles and 5% aramid pulp decreased by 68%, and the elastic modulus increased by 206%.