Electric vehicles have been promoted worldwide because of their high energy efficiency and low pollution. However, frequent charging safety accidents have to a certain extent restricted the development of electric vehicles. Therefore, it is extremely important to accurately evaluate the safety state of EV charging. The paper presents an integrated safety assessment method for electric vehicle charging safety based on fuzzy neural network. The integrated fault model was established by analyzing the correlation between truck–pile–grid. Then the integrated evaluation index was analyzed and sorted out, and the comprehensive fuzzy evaluation method used to evaluate. Following this, the improved GA_BP neural network algorithm was used to calculate the weight. Compared with the evaluation effect before and after the improvement, the simulation results show that the GA_BP neural network has higher accuracy and smaller error than the ordinary BP neural network. Finally, the feasibility and effectiveness of the evaluation method was verified by a case study.

Flexible thin-film pressure sensors have garnered significant attention due to their applications in industrial inspection and human–computer interactions. However, due to their ultra-thin structure, these sensors often exhibit lower performance, including a narrow pressure response range and low sensitivity, which constrains their further application. The most commonly used microstructure fabrication methods are challenging to apply to ultra-thin functional layers and may compromise the structural stability of the sensors. In this study, we present a novel design of a film pressure sensor with a double-sided microstructure sensing layer by adopting the template method. By incorporating the double-sided microstructures, the proposed thin-film pressure sensor can simultaneously achieve a high sensitivity value of 5.5 kPa⁻¹ and a wide range of 140 kPa, while maintaining a short response time of 120 ms and a low detection limit. This flexible film pressure sensor demonstrates considerable potential for distributed pressure sensing and industrial pressure monitoring applications.

The rapid development of energy storage power stations plays a significant role in the widespread adoption of the energy internet. Anomaly detection in these stations, as a critical component of daily operation and maintenance, holds great importance for ensuring the normal operation of energy storage systems. Currently, station monitoring primarily relies on preset fixed threshold-based alerts combined with manual supervision. However, this approach is unable to detect abnormal states below the threshold and poses a risk of missing certain anomalies. This study employs an unsupervised deep learning model based on variational autoencoders (VAEs) to perform anomaly detection on real operational data. By training the model on normal operational data, the model learns the distribution of data in the latent space under normal conditions. Experimental results demonstrate that the VAE-based model is capable of effectively detecting abnormal data segments and outliers in electricity power real-world data. Compared to classical machine learning algorithms such as Isolation Forest and Support Vector Machine, the detection performance of the VAE-based model demonstrates superiority, indicating its practical value and research significance.

With the rapid development of electric vehicles (EV), the problem of charging safety is a multifaceted and complex issue. Taking the integration of electric vehicle charging as the research object, including power batteries, charging piles, and power distribution grids, charging data is collected based on data mining technology. At the same time, the factors affecting charging safety are analysed, and an integrated index set is established and optimised. The comprehensive fuzzy evaluation method is used to comprehensively analyse the monitoring data of the electric vehicle charging process, the weight is determined based on the grey correlation method and the expert scoring mechanism, and an integrated evaluation model of the electric vehicle charging process is established. Analyse five sets of charging data in Nanjing through calculation examples, and output the integrated health degree of the electric vehicle charging process so that the equipment can be maintained in a targeted manner, which effectively proves the practicability and reliability of the assessment model.

Traditional methane sensors based on metal oxide semiconductors typically require high operating temperatures (200–400 °C), which not only leads to high power consumption but also often results in detection limits that struggle to fall below 5 ppm. To achieve room-temperature operation with lower detection limit, this work proposed a surface acoustic wave (SAW) methane sensor using monolayer graphene oxide (GO) film. The GO material was prepared by a modified Hummers method combined with liquid-phase exfoliation and characterized by transmission electron microscopy (TEM) and atomic force microscopy (AFM). The sensor was constructed by drop-casting the GO dispersion onto the delay line region of a Y-cut quartz SAW device. Results showed that the GO sensor exhibited a low detection limit of 1 ppm and a sensitivity of 0.046 mV/ppm in the range of 100–1000 ppm, which was 2.2 times higher than that of multilayer graphite (0.021 mV/ppm). The response and recovery times for 500 ppm methane were 16.7 s and 66.4 s, respectively. Good repeatability and selectivity were also demonstrated. This work presents a simple and effective approach for room-temperature methane detection using monolayer GO on SAW devices.

In recent years, electric vehicles have been widely promoted by virtue of environmental protection and pollution-free advantages. In the future, they are expected to become a mainstream means of transportation. However, as new services and business models become more and more closely related to the distribution network, the traditional AC distribution district has limited ability to respond to the large-scale access of electric vehicles. Therefore, it is becoming increasingly important to improve the ability to respond to electric vehicle access, reduce the negative impact of large-scale electric vehicle access on the grid, and predict and calculate the uncertainty caused by electric vehicle load access. Therefore, this paper proposes the concept of flexible distribution district, constructs the security operational boundary model for flexible distribution district, and uses security margin percentage as the measurement mechanism to provide dispatchers with an intuitive system operating status. In addition, this paper also proposes an effective uncertainty quantification method for the problem that it is difficult to measure the changes of bus voltage and line current at network nodes when large-scale electric vehicles are connected to the network. Numerical results demonstrate both the extensibility and the practicality of the proposed security operational boundary. In addition, the proposed uncertainty quantification method can reflect the impact of load changes on the characteristics of the power grid, which is helpful to study the improvement significance of adjusting electric vehicle load changes to distribution network optimization.