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.

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.

In a complex industrial environment, it is difficult to obtain hot rolled strip steel surface defect images. Moreover, there is a lack of effective identification methods. In response to this, this paper implements accurate classification of strip steel surface defects based on generative adversarial network and attention mechanism. Firstly, a novel WGAN model is proposed to generate new surface defect images from random noises. By expanding the number of samples from 1360 to 3773, the generated images can be further used for training classification algorithm. Secondly, a Multi-SE-ResNet34 model integrating attention mechanism is proposed to identify defects. The accuracy rate on the test set is 99.20%, which is 6.71%, 4.56%, 1.88%, 0.54% and 1.34% higher than AlexNet, VGG16, ShuffleNet v2 1×, ResNet34, and ResNet50, respectively. Finally, a visual comparison of the features extracted by different models using Grad-CAM reveals that the proposed model is more calibrated for feature extraction. Therefore, it can be concluded that the proposed methods provide a significant reference for data augmentation and classification of strip steel surface defects.

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.

Electric vehicles have a promising development prospect. As its core component, lithium-ion power battery plays a crucial role in different application scenarios. Aiming at the availability and safety of square ternary lithium batteries at different ambient temperatures and different current rates, charge-discharge cycle experiments are carried out to study the voltage, temperature and capacity changes of lithium batteries. The voltage plateau characteristics of lithium batteries under different working conditions are explored. The results show that when discharging at current rates of 0.1C, 0.25C, 0.5C, 0.75C, and 1C under the ambient temperature of −5 °C, 10 °C, 25 °C, and 40 °C, the terminal voltage of the battery changes smoothly during the voltage plateau period, the rise of the surface temperature has not reached the peak value, and the discharge capacity accounts for about 50%. The battery has better working performance. While at the ambient temperature of −20 °C, the discharge capacity accounts for the highest proportion in the stage from the open-circuit voltage to the initial voltage of the plateau period. The research results can provide a reference for the modeling and control strategy design of lithium-ion power batteries in the energy storage system of electric vehicles.