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 steel strip is an essential raw material in the machinery industry. Besides, the surface defects of the steel strip directly determine its performance. To achieve rapid and effective detection of the defects, a CP-YOLOv3-dense (classification priority YOLOv3 DenseNet) neural network was proposed in the present study. The model used YOLOv3 as basic network, implemented priority classification on the images, and then replaced the two residual network modules with two dense network modules. Therefore, the network can receive the multi-layer convolution features output by the dense connection block before making predictions, consequently enhancing the reuse and fusion of features. Finally, the six kinds of surface defects were detected by the improved network. According to the results, the detection precision of the CP-YOLOv3-dense network is 85.7%, the recall rate is 82.3%, the mean average precision is 82.73%, and the detection time of each image is 9.68 ms.

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.

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.