Accurately estimating the State of Health (SOH) of batteries in field applications is critical for timely maintenance and secondary utilization. Although considerable studies are conducted using data-driven techniques, these methods often face challenges in interpretability and integrating physical knowledge. To address this issue, this paper proposes an accurate SOH estimation method using a physics-informed neural network (PINN) with spatio-temporal feature extraction. The proposed model utilizes multi-sensor data as an input and employs a spatio-temporal attention mechanism to automatically extract effective features from both the time step dimension and the sensor dimension. Subsequently, PINN is utilized to regulate the convolutional neural network training process and oversee the degradation trajectory of SOH estimation. By integrating the attention mechanism and physical information, the model achieves higher accuracy and more interpretable predictions. The proposed method is validated on a field dataset with 20 on-road vehicles. Experimental results indicate that the proposed method achieves a root mean square error of 1.599%, which is a relative reduction of 42.32% compared to the baseline model.

Continuous sign language recognition seeks to identify unsegmented sign language from videos by means of a weakly supervised manner, providing only sentence-level labels. In sign language videos, the gestures are smooth and continuous, and the same word may also correspond to video clips of different scales. Therefore, this poses a challenge in accurately capturing complex temporal dependencies. For hearing-impaired service robots, continuous sign language recognition capability is particularly critical, as the robots need to understand the natural sign language expressions of hearing-impaired users in real time. Previous studies have shown that using methods with a time-invariant receptive field for temporal modeling can partially address this issue, but they are not well-suited to handle video clips of varying scales. In this study, we re-examined the temporal modeling schemes in recent CSLR works and proposed the Two-stage Temporal ConvTransformer (T2CT), which fully leverages the advantages of one-dimensional convolutional neural networks and Transformer encoders, adopting a two-stage structure to capture more comprehensive spatiotemporal features. In particular, each stage of the proposed T2CT consists of two parts: a Local Temporal Modeling Module to capture short-term temporal dependencies, and a Global Temporal Modeling Module for long-term temporal modeling. Experimental results on three challenging CSLR datasets demonstrate that the proposed T2CT achieves competitive performance.

Contemporary battery management systems pre-dominantly employ machine learning frameworks as the principal methodology for lithium-ion batteries degradation assessment. However, this approach suffers from the drawback of relying on a large amount of labeled data, which is not applicable when estimating the state of health (SOH) of real vehicle batteries. To address this challenge, this paper proposes a Physics-Informed Neural Network -based SOH estimation method for lithium-ion batteries. Our approach integrates the physical mechanisms of battery aging into a deep learning framework, taking into account the model’s interpretability, adaptability, and robustness under complex conditions. Experimental validation with multi-year real-vehicle datasets demonstrates that in comparison to existing methods, the PINN approach achieves mean absolute errors below 2.5% and approximately 30% higher accuracy, which realizes effective SOH estimation in practical vehicular applications.

Accurate and rapid state-of-health (SOH) estimation of retired lithium-ion batteries is critical for sustainable recycling and second-life applications. However, data-driven methods face challenges due to data scarcity and heterogeneity under random retirement conditions, such as varying states of charge (SOC). This study proposes a generative learning framework based on a vector quantized-variational autoencoder (VQ-VAE) to generate synthetic battery pulse voltage response data, enabling robust SOH estimation without exhaustive physical measurements. The VQ-VAE model leverages cross-attention mechanisms to capture dependencies between SOC conditions and voltage responses, generating high-fidelity data for unseen retirement scenarios. Experimental results demonstrate that the generated data achieve a mean absolute percentage error (MAPE) below 6% for SOH estimation across diverse battery types, including nickel manganese cobalt oxide (NMC), lithium iron phosphate (LFP), and lithium manganese oxide (LMO).

Smart Microgrid with distributed energy resources is prevalent due to its flexibility and adaptability. Benefiting from Internet of Things technology, massive real-time energy data can be gathered for intelligent energy management. However, the high stochasticity of renewable energy resources makes it difficult to design an optimal scheduling strategy for smart microgrid. To address this problem, a dynamic energy management strategy based on model-free reinforcement leaning is proposed. Considering the uncertainties of renewable resources and energy demands, the dynamic energy management problem is formulated as a Markov decision process with unknown transition probabilities. Then, an intelligent energy management strategy based on soft actor-critic is proposed. Moreover, gate recurrent unit is incorporated to enhance the extraction of time-series characteristics. Finally, simulation results demonstrate that the proposed method significantly reduces the energy bills by up to 24.28%, compared with other strategies.

Microgrids have emerged as a promising solution to improve the resilience and reliability of power systems. However, unexpected faults can still pose significant challenges to the stable operation of microgrids. This paper proposes a stochastic programming-based strategy for mitigating faults in a DC microgrid. First, a scheduling strategy is implemented for normal operations and switched to a mitigation strategy upon detecting faults, which ensures optimal performance and resilience. Second, a scenario-based stochastic programming enhanced by the DBSCAN algorithm for scenario reduction is designed, which addresses the optimization problem efficiently. Comprehensive simulations are conducted to evaluate the proposed scheduling and mitigation strategies, demonstrating their advantages under both normal and fault conditions.

In the field of energy storage, precise estimation of the state-of-charge (SOC) of lithium-ion batteries is crucial for maximizing their efficiency and extending their lifespan. Existing research has predominantly focused on SOC estimation for individual battery cells. However, in practical applications, a balancing circuit is typically integrated into the battery management system (BMS) to mitigate cell imbalance. When the balancing circuit is activated, the battery cell transitions into a new operational mode, rendering conventional SOC estimation techniques ineffective. Reconfigurable circuits, recognized for their adaptability across diverse application environments, present a novel approach for SOC estimation in lithium-ion batteries, leveraging their dynamic reconfiguration capabilities. This paper introduces an innovative method for SOC estimation of reconfigurable lithium-ion batteries, employing an extended Kalman filter (EKF) within the context of reconfigurable circuits. The proposed methodology begins with the design of a switching system for lithium-ion batteries, facilitating equivalent circuit modeling. Subsequently, a nonlinear observer, based on the extended Kalman filter, is developed to estimate the SOC. An experimental platform was also constructed to validate the feasibility and efficiency of the proposed method. Experimental results demonstrate that this approach significantly enhances the accuracy and robustness of SOC estimation compared to traditional methods.

Grid operation state estimation of power grid operating states and power system analysis largely relies on physical layer measurements of the power system. However, the introduction of information and communication technologies in smart grids has greatly improved operational efficiency while also increasing the system’s vulnerability to attack. This can compromise data integrity and reliability, leading to data missing and then affecting subsequent steps. Additionally, data collection and transmission can encounter various issues, resulting in partial data loss or errors. Therefore, it is crucial to recover and complete the missing data.This paper proposes a solution for missing data recovery in power systems based on generative adversarial network (GAN). The approach utilizes graph convolutional network (GCN) to extract features from data, taking into account the topological connectivity between nodes. To improve the quality of data imputation and enhance the authenticity of recovered data, incomplete data containing nodes with missing data and observable nodes is used as input for the generator, and a local feature extractor is added to the existing discriminator network. This structure allows the generator to estimate unknown information by observing known data, while the discriminator focuses more on the local areas containing the recovered data when determining the authenticity, thereby helping the generator to improve its data completion capability. Correspondingly, we design context loss constraints considering both local and global ranges to ensure accurate recovery of non-missing node data while completing the missing data portions.The experimental results demonstrate that employing GCN and incorporating local features can significantly enhance recovery performance on grid data. For voltage magnitude, the mean absolute error decreased by 10.4335%. Additionally, high-precision recovery results can still be achieved even with up to half data missing, which is validated on the IEEE-14 system.

With the integration of advanced communication and sensor technologies, traditional power grids are shifting to automated smart grids, while exacerbating the risk of cyber-attacks. This paper proposes a spatio-temporal graph attention network to improve invisible attack detection in smart grids. First, graph theory and grid knowledge are utilized for modeling in order to learn graph structure and spatial features. Secondly, hidden features are extracted based on the graph attention mechanism to predict the future behavior of nodes. Thirdly, graph deviation score is calculated from the end-to-end learned node deviations, which is the judgement for identifying attacks. Simulation results demonstrate that our method identify attacks more accurately than baseline methods.

The braking system is the key part of trains, and its full life-cycle of health status is essential to ensure the safety of trains. How to accurately assess real-time health status throughout the full life-cycle of the train braking system is a challenge. In this paper, a digital twin-driven hybrid estimate method for health status of the braking system is proposed. Firstly, an equivalent model of the braking system is built in the digital twin platform. Then, a hybrid method of fusing model and data is proposed to assess the health status. Finally, a cloud digital twin experimental platform for health status assessment of the braking system is built, and the health status is shown by visualization framework. The experiments verify the effectiveness and practicality of the proposed scheme.

Estimating accurate torque and speed is critical to control the operation of permanent magnet synchronous motors (PMSM). But the temperature factors are usually neglected in existing studies, which degrades estimation accuracy. In this paper, a thermal-aware digital twin model is proposed for PMSM to estimate motor torque and speed with the motor temperature and d-q axis current and voltage. Firstly, the motor parameters related to torque and speed are extracted by the Spearman correlation coefficients. Moreover, the stator winding temperature is selected as the input feature. Secondly, a digital model based on BP neural networks (BPNN) is established to estimate torque and speed. Thirdly, the parameters of the BPNN model are optimized by the whale optimization algorithm to accelerate the convergence speed and avoid local optima. Finally, experimental results show that the mean square error (MSE) of the BPNN model considering the temperature factors is reduced by 8.3%, which verifies that there is an effect of temperature on the torque and speed estimation. The MSE of the proposed method is reduced by 11.7% on average, which confirmed the higher accuracy of the proposed method compared with the classical BPNN model.

The safety of railway crossings are of great important for rail and road transportation, because serious accidents occur in this area. Therefore, it is necessary to carry out foreign objects detection on railway crossings in order to improve the safety. Traditionally, video surveillance is one such solution, but it suffer from weather and illumination conditions. Under the hard environment conditions, the image of railway crossings is failed to capture by the camera. We propose a foreign objects detection system based on millimeter wave radar which has a higher detection accuracy, without the limitation of weather and light. Unlike vision-based approach, it can operate in darkness, high or low light intensity environment. With a millimeter wave radar, we first obtain the reflected signal from objects or ground and perform signal processing algorithm to extract the targets and suppress the clutter from received signal. We evaluate the detection capabilities of the millimeter wave radar in level crossings of railway.

Traditional lighting systems suffer from the problem of low energy efficiency and low illumination quality due to its disappointing management. To address this issue, in this paper, a novel traffic-flow adaptive scheme of smart lighting systems is proposed on the basis of the cyber-physical cloud system. The cyber-physical cloud system consists of the digital twin and cyber-physical system. The operation of the lighting system is simulated in the counterpart twin system with the digital twin technology. The cyber-physical system realizes data collection, information interaction, analysis, and processing, as well as complex computation and remote control. The traffic adaptive scheme works according to the brightness sequence to improves the energy efficiency of the lighting system and provide higher illumination quality for drivers. Extensive simulation results verify the proposed control scheme could improve the energy efficiency of lighting systems.

The high-speed solenoid valve is a crucial module in train braking system, which is an essential factor to ensure the safe operation of trains. How to estimate the health status of the high-speed solenoid valve accurately to improve the reliability of train braking system is a challenging issue. Most related work relies on accurate physical models or large amounts of historical data. To address this challenge, this paper proposes a hybrid data-fusion estimate method for the health status of train braking system. Firstly, the physical model of the high-speed solenoid valve is established, and physical indicators which represent the working performance are extracted. Then, the dynamic driving current is processed by ensemble empirical mode decomposition (EEMD) to calculate the information entropy. Physical indicators and information entropy indicators are combined into a feature vector, which can be reduced the dimension by the t-distributed stochastic neighbor embedding (T-SNE) algorithm. Finally, the feature vector is input into the probabilistic neural network (PNN) to estimate the health status of train braking system. The proposed method is implemented in the high-speed solenoid valve degradation dataset, which collected by the train brake system experiment platform. The result shows that it is better than other methods in the accuracy and calculation efficiency.

With the rapid development of inspection techniques, more emphases should be placed on the improvement of the reliability, safety and intelligence of the robot system. In this paper, a framework for the patrol robot that automatically finishes complex task and motion planning in the indoor substation is proposed. To realize real-time response to the environmental changes, the proposed framework keeps an ongoing interaction with the environment as a Reactive System (RS). The RS employs the Transition System (TS) and Nondeterministic Biichi Automaton (NBA) to create a discrete controller that bounds the acts of the patrol robot in the safe and reasonable specifications. What's more, the environment signals are treated as the trigger condition of task switching. If a new environment information is detected, our approach can automatically give a feasible plan. Then, the sensor-based mechanism of continuous controllers is guided by the discrete controller, which results in a hybrid system satisfying the high-level specification. The experiment within the LTLMoP toolkit verifies the proposed framework.

Recent advancement in the field of electrical technology and cyber-physical energy system (CPES) has brought the key towards challenging issues regarding transparency of information management and efficient allocation of energy. This paper is dedicated to a CPES that deals with an electric light rail that involves large number of distributed and globally interconnected supercapacitor energy storage modules, with the aim of efficient fusion of information, control protocol and energy. The autonomous modules estimate local state of energy and share the information via the communication network. A consensus control strategy is proposed to reach the balanced state of energy between these distributed supercapacitor modules with the advantages in terms of increasing efficiency and reducing time consumption of energy transfer. The proposed method enforces the CPES constraints specific to the particular supercapacitor modules in the electric light rails. Experimental results are provided to verify the effectiveness of the proposed state of energy balancing method.