Vehicle trajectory prediction is a critical and complex task in autonomous driving systems, where accurate prediction is essential to ensure both safety and comfort. Given that driving style impacts future trajectory prediction, integrating driving style information is crucial. In this paper, a trajectory prediction framework is proposed, in which a spatial-temporal information fusion network incorporating driving style is leveraged. Driving style is captured through a denoising Transformer autoencoder for dimensionality reduction and refined using fuzzy k-means++ clustering. Temporal information and spatial interactions of the vehicle are dynamically extracted using Transformer and graph neural network, and the future trajectory distribution is generated using a Transformer decoder enhanced by the KAN network with a sine function. Ablation and comparison experiments are carried out on the public NGSIM dataset. The results demonstrate that our model outperforms others in prediction accuracy, with improvements of up to 29.7% across evaluation metrics.

Our framework employs a hybrid model of Bidirectional Temporal Convolutional Network and Bidirectional Gated Recurrent Unit for sequential intention prediction. Additionally, a TransformerConv-based Graph Attention Network captures spatial interactions from historical frames and incorporates temporal features to generate spatial-temporal features, which are further processed by an intention-inspired context extraction attention mechanism to generate inputs for final trajectory prediction. On the NGSIM US-101 and I-80 datasets, our model achieves a 93.20% accuracy in predicting sequential driving intentions at a prediction horizon of 3 seconds. By incorporating these predicted intentions into the trajectory prediction network, it reduces the RMSE by 11.5% over a prediction horizon of 5 seconds compared to state-of-the-art methods, demonstrating its effectiveness in highway scenarios.

While the application fields of li-ion batteries becoming more and more extensive, the risk of thermal runaway caused by overcharging, over-discharging, internal short circuit and other faults, mainly due to the complex operating environment, can no longer be ignored. Predicting the risk of thermal runaway and ensuring the safety of drivers are the first problems to be solved. On the other hand, how to reduce the false alarm rate and realize more robust fault detection is still a common challenge for researchers. To address the above problems, this paper combines Wasserstein GAN and autoencoder to realize robust and effective fault detection. The autoencoder’s discriminative ability is enhanced by the adversarial network and the training stability of Wasserstein GAN is utilized to significantly reduce the false alarm rate. Experimental results on a real electric vehicle dataset experiencing thermal runaway show that the proposed method reduces the false alarm rate to 0.71% while ensuring accurate detection capability.

The rise of electric vehicles has created a demand for more advanced battery management systems. Pre-trained large models, such as language, time-series, vision, and multimodal models, offer promising yet underexplored opportunities for enhancing battery management. This review examines their integration through methods like prompt learning and fine-tuning, with applications in state-of-charge/state-of-health estimation, remaining useful life prediction, and anomaly detection. It also discusses key challenges, including data limitations, computational demands, and model interpretability, while outlining future directions. The paper provides insights into how these models can enable safer, more efficient, and longer-lasting electric vehicle batteries.

To address the limitations of traditional energy management strategies in hybrid energy storage systems for electric vehicles, including poor adaptability to dynamic conditions and safety risks in online reinforcement learning, this paper proposes an offline reinforcement learning framework integrating a time-aware variational autoencoder and Decision Transformer. Initially, high-quality expert trajectories are generated by a dynamic programming-based energy management strategy. Subsequently, a bidirectional long short-term memory network extracts temporal features from state sequences, while variational autoencoder synthesizes physics-constrained trajectories to mitigate distribution shift. Finally, Decision Transformer employs a self-attention mechanism to conduct multiscale temporal modeling of historical state-action sequences, establishing implicit policy mapping. Experimental results under the Dallas5 driving cycle demonstrate that the energy management strategy trained with a mixed dataset D1 outperforms the strategy trained with a pure expert dataset D2: battery capacity loss is reduced by 5.5%, and the final state of charge of the supercapacitor is stably maintained at 0.7320. This highlights the critical role of data diversity in enhancing generalization, offering a novel pathway for robust EMS design in real-world vehicular applications.

Deep reinforcement learning (DRL) has emerged as a promising approach for energy management in hybrid electric vehicles. However, the current focus of energy management in DRL primarily centers on energy-saving performance while neglecting safety constraints during the training process. To address this challenge, this paper proposes a rational-safe reinforcement learning energy management strategy for hybrid electric vehicles. First, a safety evaluation mechanism based on eXtreme Gradient Boosting is developed to assess the safety of actions generated by the agent. Subsequently, a physics-informed safety layer is introduced to modify irrational control signals through constrained optimization when the agent’s outputs are evaluated as unsafe. Experimental results demonstrate that the proposed method ensures the safety of output actions while improving fuel economy by 5.64%-6.28% compared to existing reinforcement learning approaches.

This paper investigates the state-of-charge (SOC) estimation problem for lithium-ion batteries in balancing systems. Currently, most research focuses on the SOC estimation of individual lithium-ion cell. However, in practical applications, lithium-ion batteries are often connected to balancing circuits to eliminate imbalances within the battery pack. When the balancing circuit is activated, the dynamic characteristics of the battery system change considerably, and existing estimation methods often fail to effectively capture this dynamic transition. To address this issue, this paper proposes a switched Gaussian process regression(GPR) method. First, we conduct a qualitative analysis of the lithium-ion battery balancing circuit switched system based on a switch resistor balancing circuit. Then, building on this analysis, we develop a switched GPR method that adapts to the on/off state of the balancing circuit. Finally, we construct an experimental platform and validate the proposed switched model through comparative experiments with traditional methods. The results demonstrate that the proposed switched model exhibits significant advantages in terms of SOC estimation accuracy and adaptability, effectively handling the complex dynamic changes in the system after the balancing circuit is activated.

The demand for efficient energy management strategies (EMSs) in electric vehicles (EVs) has become increasingly critical. However, existing EMSs based on predictive reinforcement learning (RL) often exhibit low sample efficiency and limited predictive accuracy due to reliance on simple velocity features and conventional sequential models. This paper proposes a novel prediction-enhanced RL framework that integrates an iTransformer-based velocity predictor with Soft Actor-Critic (SAC). Specifically, the predictor forecasts the vehicle velocity for the next three seconds, and the predicted velocity is then processed by an EV dynamics model to calculate future power demand. This predicted information is incorporated into the SAC state space to enhance decision-making. Based on the augmented state, SAC learns an adaptive EMS that reduces energy consumption and battery aging, extends driving range and the system lifespan, and maintains the supercapacitor state of charge within a desirable range. Experimental validation using real-world data shows that the proposed method achieves a 4.20% reduction in energy consumption costs and a 6.11% decrease in battery aging costs, leading to an overall 4.11% cost reduction compared to the SAC without predictive information.

Supercapacitors are typically charged with constant current conditions. However, existing parameter identification methods can not effectively identify equivalent series resistance (ESR) under constant current conditons. To address this problem, this paper proposes a Switching Kalman Filter (SKF) parameter identification method for reconfigurable supercapacitors. By introducing a switching excitation strategy, transient current pulses are utilized to enhance the observable dimension of the system, overcoming the limitations of the traditional methods in recognizing parameters under constant current conditions and achieving high-precision identification. First, an RC model of the supercapacitor incorporating a reconfigurable topology is established. Then the model undergoes mathematical discretization, and a recursive algorithm framework for parameter identification is constructed. Through several sets of simulation experiments, it is verified that SKF is able to perform parameter identification under constant current conditions.

This paper proposes a vehicle following control strategy based on Transformer-enabled offline reinforcement learning, effectively addressing the adaptability problems of traditional control methods in traffic scenarios. We design a Transformer encoding architecture capable of capturing temporal dependencies in the vehicle following, enhancing state representation. By combining policy optimization with behavior cloning, Expert driving knowledge is utilized to optimize the policies without the risks of environmental interactions. Additionally, a velocity-based dynamic safety gap model is constructed and corresponding reward function is designed to balance safety, comfort and efficiency. Quantitative assessments reveal that our proposed method outperforms traditional methodologies and existing learning methods in terms of safety and efficiency metrics, while maintaining equivalence with behavior cloning techniques regarding comfort indices.

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.

Accurate prediction of lithium-ion battery capacity degradation and remaining useful life (RUL) is crucial for battery health management and the safe operation of equipment. However, the diversity of battery types and variations in usage environments pose challenges to data-driven predictive models. Traditional machine learning models often exhibit poor performance in terms of prediction and generalization capabilities. This paper introduces a time-series large model: ANVMD-Llama. The model employs Adaptive Noise Variational Mode Decomposition (ANVMD) to process battery aging data for RUL prediction. Initially, the adaptive noise variational mode decomposition optimizes the tokenization scheme of Lag-Llama, decomposing battery degradation data into multiscale modal components with distinct features to characterize degradation trends and fluctuation properties, aiding the model in understanding fluctuation patterns. Subsequently, ANVMD-Llama is pre-trained on a large dataset of diverse lithium-ion battery degradation data to learn capacity degradation patterns. The model is then fine-tuned using a small amount of data to update the top-level modules, achieving more accurate predictions. Finally, the experimental results demonstrate that the proposed model achieves accurate RUL prediction and exhibits strong transfer capability.

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).

Accurate and effective prediction of battery remaining useful life (RUL) is crucial for the retirement planning of electric buses and the secondary utilization of battery packs. This study utilizes four years of operational data from nine electric buses to achieve precise RUL prediction for power batteries. First, considering the real-world operating characteristics of electric buses, this paper introduces a new RUL definition based on remaining mileage (RML) and remaining days of life (RDL) to characterize the remaining lifespan of battery packs. Subsequently, SOH labeling is conducted using charging data and filtering algorithms, followed by determining the end-of-life point of battery packs from SOH degradation trajectories. Finally, multi-time-scale features—including battery features, historical features, seasonal features, and discharging features are extracted from raw data, and the predictive performance of multiple ensemble learning models is compared. The results indicate that the AdaBoost model achieves the best performance in predicting RML and RDL, with a mean absolute error of 98 days and 16,852 km, respectively.

Machine learning models have become a prominent technique for predicting battery state in electric vehicles (EVs). However, due to the significant variability in the operating environments and conditions of different EVs, traditional machine learning models often exhibit limited generalization capabilities. Additionally, the computational limitations of on-board chips in conventional battery management systems (BMSs) can lead to considerable computational overhead. Furthermore, acquiring large-scale battery data for model training can be economically prohibitive. To address these challenges, this article proposes an Internet of Batteries (IoB) approach for battery health management, leveraging large language model (LLM) to monitor battery health. First, this article introduces the concept of IoB in EVs. Subsequently, an experimental IoB system is established. Through comparisons with other machine learning methods, the study demonstrates that LLM exhibit strong generalization capabilities for predicting the battery data of EVs, even with small-scale data fine-tuning. The experimental results suggest that the combination of LLM and IoB may represent a promising advancement over traditional machine learning approaches.

A compiler converts smart contract source code into bytecode, ensuring behavior consistency between them. However, as compiler is also a program, it may contain bugs that disrupt this consistency, known as Compiler-Introduced Bugs (CIBs). Of the latest 4,857 verified smart contracts coded in Solidity, approximately 58% still use compilers that contain at least one CIB. These CIBs can be exploited by attackers to bypass security checks or inject malicious data, leading to significant security issues, which becomes even more serious for smart contracts in blockchain as they cannot be modified after being deployed. To this end, this paper proposes sBugChecker, to the best of our knowledge, the first systematic framework designed to automatically and effectively detect CIBs for smart contracts coded in Solidity. sBugChecker can be readily extended with the rule customization suite we propose based on domain specific language. Additionally, it employs two static analytical methods, i.e., pattern matching, and symbolic execution, to identify CIBs’ triggering conditions and confirm their impacts, broadening its detection scope and improving its detection efficiency. To evaluate sBugChecker’s performance, we construct a CIB mutated smart contract dataset, which is the first publicly-available one for this study. According to the evaluation based on this dataset, sBugChecker performs exceptionally well, with detection precision, recall, and F-measure on average achieving 96.6%, 95.5% and 96.0%, respectively. Moreover, sBugChecker has been applied to successfully discover real-world deployed smart contracts capable of triggering CIBs.

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.

Thermal runaway of lithium-ion batteries is one of the key challenges hindering the development of electric vehicles. Realizing timely fault detection in battery systems is of great significance for preventing thermal runaways and safeguarding people’s lives and properties. As it is difficult to obtain fault battery datasets in the real world, there is a need to develop novel fault detection methods that can operate with normal data. In this paper, we propose an optimized Gated Recurrent Unit autoencoder architecture that integrates the sparse representation technique to detect battery faults in electric vehicles. Firstly, the Gated Recurrent Unit is employed to efficiently learn the information in battery data from normal electric vehicles. Then, the autoencoder utilizes the sparse representation technique to improve its ability to recognize abnormal data by learning a set of basis vectors that can sparsely represent normal data. Finally, the reconstruction errors between the original and reconstructed vectors are calculated in a sliding window and compared to the threshold to detect the fault. The effectiveness of the proposed method is verified on a real operating dataset including two normal electric vehicles and two faulty electric vehicles. The results show that it can provide early alarm time and reduce the probability of false alarms.

Microgrids based on distributed control are susceptible to cyber attacks during operation, which can result in system anomalies, crashes, and even equipment damage. To ensure a secure collaborative environment, this paper proposes an attack detection mechanism based on extended cell similarity. First, a DC microgrid model and a network attack model were established. Secondly, a microgrid state interval prediction mechanism based on QRLSTM is proposed. The output of the load prediction model is used as the input of the mechanism model, and the expected state range of the terminal equipment voltage and current is obtained through simulation. Then, the cell similarity algorithm is improved to relate the similarity between the actual measured data and the expected period to the probability of cyber attacks occurring. Finally, the effectiveness and feasibility of the detection method were verified through experiments.

In 5G and beyond, discontinuous reception (DRX) is a promising energy-saving technology for massive machine-type communications (mMTC) devices with intermittent connections. However, existing DRX solutions face challenges in effectively accommodating mMTC services with critical delay requirements, particularly in a dynamic wireless environment. To address this problem, this paper proposes an intelligent DRX scheme for critical mMTC. Firstly, we modify the conventional discontinuous reception model by introducing the critical delay flag. Then we optimize the DRX by Markov Decision Process and designe the energy-delay factor to achieve the simultaneous optimization. Finally, we extract the environmental features by convolutional neural network and solve the problem by deep Q-network. A communication simulation platform is established to verify the effectiveness of the proposed method. The simulation results show that the proposed method can effectively improve the energy efficiency in mMTC situations compared with state-of-the-art.

Lithium-ion batteries are widely used in portable devices and mobile medical equipment due to their high energy density and long cycle life. However, long charging times for lithium-ion batteries can limit their usability. This paper proposes a multi-stage constant current charging protocol.

The supercapacitor tram charges its supercapacitors as passengers enter and exit each station, taking advantage of the short parking time, usually within 30 seconds. However, using a single DC-DC converter for energy conversion would be costly and unreliable. To address this issue, this paper proposes a method that involves the parallel connection of multiple DC-DC converters in coordination with supercapacitors on supercapacitor trams to achieve power balancing. The paper begins by developing a system model for the power supply system and explaining its motivation. Next, a design for a parallel connection of multiple DC-DC converters is presented to prevent excessive current in any one converter and achieve current balancing among them, thereby reducing the system load. Finally, a cooperative control method is proposed to achieve current balancing at each DC-DC converter. The proposed method’s effectiveness has been demonstrated through extensive experiments and simulations.

Guaranteed safety and performance under different cases have significant influence on the development of lateral control of autonomous vehicles. This paper proposes a robust nonlinear controller using barrier Lyapunov function within the constraints. In the case of unknown bounded uncertainty, the barrier Lyapunov function is appropriately arranged into the controller to restrict the state variables of the designed safe region in the process. Thus, the proposed method is composed of the nonlinear controller using barrier Lyapunov function and kalman filter. The kalman filter is designed as an observer to estimate the state variables. At the meantime, the method we proposed meets the constraints of output and the external disturbance. Moreover, the validity of the proposed method is validated in the co-simulation of the MATLAB/Simulink and CarSim.

With the rapid advancement of technologies such as electric vehicles, the demand for energy storage devices has surged, leading to the widespread adoption of supercapacitors due to their numerous advantages. In practical applications, supercapacitors are often arranged in series or parallel configurations to form capacitor banks, which cater to higher voltage or capacity requirements. However, inconsistencies in the manufacturing processes and materials can lead to variations in the electrical performance of individual supercapacitors, necessitating effective balance management. Existing balancing methods are generally categorized into passive and active approaches. While passive balancing circuits are simple and cost-effective, they tend to be inefficient. On the other hand, active balancing methods, although offering high control precision, are typically more complex and expensive. This paper introduces a collaborative balancing strategy based on Deep Deterministic Policy Gradient (DDPG) using a switch resistor circuit, which serves as an intermediate approach between passive and active methods by combining their respective advantages. By integrating deep reinforcement learning with the switch resistor circuit for supercapacitor balancing, the proposed method addresses the slow balancing speed of traditional circuits under significant voltage disparities, enhancing the robustness of the balancing process and achieving superior performance. A simulation environment is established in Simulink to evaluate the effectiveness of the proposed method under various initial voltage conditions. The results demonstrate that the supercapacitor bank achieves balance within a short time frame. Moreover, comparative experiments indicate that the collaborative strategy significantly reduces overshoot and improves the robustness of supercapacitor balancing compared to noncollaborative approaches.

The estimation of the state-of-charge (SOC) in lithium-ion batteries has garnered significant attention, with current research primarily concentrating on individual batteries. In practice, however, lithium-ion batteries often require connection to a balancing circuit to correct battery imbalance. In such cases, the system topology differs from that of an individual battery. Therefore, it is essential to account for this change. This article proposes a method for estimating the SOC of lithium-ion battery cells within reconfigurable circuits. We established a switching system model for lithium-ion batteries in reconfigurable circuits. We then design a nonlinear switching observer and examine its stability. Additionally, we conducted extensive experiments to evaluate the proposed observer’s performance and compared it with other observers.

With the rapid growth of electric vehicle (EV) adoption, ensuring the reliability and safety of their battery systems is of significant importance. Current anomaly detection methods, which rely primarily on temporal dependencies, often overlook spatial patterns, leading to inaccurate detection. This paper introduces a novel autoencoder that incorporates a multi-head attention mechanism for voltage anomaly detection in EV batteries. First, comprehensive datasets from six real EVs operating under diverse and dynamic conditions were collected. Second, to address the complexity and variability of the data, various pre-processing techniques were employed, and the optimal method was selected to ensure data quality and consistency. Third, an improved deep learning autoencoder model was developed, incorporating multiple multi-head attention mechanisms to capture intricate patterns and temporal dependencies within the battery data. The effectiveness of the proposed method is verified by the tests on the comprehensive dataset of real electric vehicles operating under dynamic conditions.

The battery management system of electric vehicles requires an accurate and reliable predictor of the state of charge (SoC) of the battery. The true SoC of a lithium battery is influenced by many factors, such as battery temperature, the number of charge and discharge cycles, and battery aging. Therefore, it is difficult to achieve ideal results by detecting external battery characteristics such as battery voltage, current, temperature, and internal resistance to predict SoC. To address this challenge, this article uses the non-battery external variable of speed as the input to the prediction model. After integrating the speed over time, linear regression algorithm is used to predict SoC. Furthermore, considering the effect of battery aging on the accuracy of SoC prediction, this article uses sliding time windows to update the training set, thereby achieving accurate SoC prediction. The experimental results show that the digital twin model achieves a prediction accuracy of over 95%in practical applications. In addition, as speed characterizes the external characteristics of electric vehicles, predicting SoC through speed establishes the connection between electric vehicles and electric vehicle batteries.

In electric vehicles equipped with hybrid energy storage systems, it is crucial to achieve fast and precise regulation of the DC bus voltage to match the target reference. Existing techniques often struggle to avoid voltage overshoots while pursuing fast stabilization, which leads to significant fluctuations in the DC bus voltage. To overcome this challenge, this paper presents an advanced energy management strategy using predictive setpoint modulation. We develop an innovative set-point modulation technique that achieves efficient regulation of the DC bus voltage by utilizing a feed-forward compensator in combination with parallel operation of multiple DC-DC converters and supercapacitors (SCs). This configuration not only ensures voltage stability, but also optimizes the charge/discharge cycle management of the supercapacitors, which improves the energy utilization efficiency and overall system performance. Laboratory experimental results show that our strategy significantly reduces DC bus voltage fluctuations and maintains stable system operation under various load conditions, outperforming conventional technologies. In addition, our technology has the advantages of fast response and easy integration into existing systems, providing an efficient and reliable solution for energy management in electric vehicles.

The aging process of lithium-ion batteries is a complex nonlinear process involving multiple electrochemical reactions. During different charge-discharge cycles, the battery exhibits different degradation characteristics. Accurate State of Health (SoH) estimation is a fundamental requirement for battery prediction and health management. Existing SoH estimation methods often rely on manually selected features, which introduce subjective bias, limiting their generalizability and robustness. This paper proposes a feature extraction framework based on a Bayesian optimization algorithm. First, partial incremental capacity (IC) analysis is performed within a specific voltage range, and this framework searches for effective Cycle-Voltage-IC features in the multidimensional feature space based on the self-fluctuation of the features and their correlation with battery capacity. Bayesian optimization is used to search for feature indices and intervals to extract features. Then a stacking ensemble model is developed that combines Bayesian ridge regression and randomized consistent sampling regressor to enhance the robustness of SoH estimation. This method provides interpretable feature extraction and improves the accuracy of SoH estimation. Compared with the feature extraction method based on manual experience, the feature extraction framework and SoH estimation model proposed in this paper can better fit the capacity degradation process of the four battery models, and the absolute errors of their SoH estimation are reduced by 21.88%, 66.67%, 8.92%, and 32.59%, respectively.

Supercapacitors are widely used as energy storage devices due to their high power density, long lifespan, and excellent charge/discharge capabilities. However, when connected in series to meet higher voltage requirements, cell imbalances can occur over time, negatively impacting system performance. To address this, we propose a novel method called Passive Balancing Circuit Voltage Estimation (PBVE), which utilizes existing passive balancing circuits to indirectly estimate cell voltages. This method reduces the reliance on extensive sensor networks, thereby lowering system complexity and cost. The PBVE method was validated using Simulink simulations, and the results demonstrated improved efficiency and reliability in monitoring and managing supercapacitor systems under various operational conditions.

State of charge (SOC) estimation of lithium-ion batteries has been extensively studied, and most of the existing research focuses on SOC estimation of individual lithium-ion battery. In practical applications, however, lithium-ion batteries are connected to a balancing circuit to eliminate imbalances between batteries. When a balancing circuit is activated, the state space equation of its equivalent circuit will change. In this paper, we propose a switched extended Kalman filter method for SOC estimation of lithium-ion battery balance systems. The switching system model is established by combining the Li-ion battery equivalent circuit model and the switching resistance balance circuit. A switching extended Kalman filter is designed to estimate the SOC of a switching system.

Batteries are pivotal in electric vehicles (EVs), serving as the primary source of power. To ensure the safe and efficient operation of EVs, it is essential to accurately predict the state of health (SOH) of the battery, typically achieved through a battery management system (BMS). However, the complex coupling reactions and nonlinear degradation processes inherent in lithium-ion batteries (LIBs) present significant challenges in SOH prediction. Current data-driven models often require extensive datasets and prolonged training periods, while also exhibiting limited generalization capabilities. To address these challenges, this paper proposes a novel method based on a foundation model which named Lag-Llama for predicting SOH and other critical battery characteristics synchronously, such as temperature, internal resistance. Remarkably, under zero-sample conditions, our approach achieves a Continuous Ranked Probability Score (CRPS) of 0.0079, demonstrating robust zero-shot generalization capabilities. Furthermore, the model’s predictive performance is significantly enhanced following fine-tuning.

The rapid and accurate detection of faults for lithium-ion batteries plays a critical role in ensuring the safe operation of electric vehicle systems. This paper proposes a fault detection method for electric vehicle batteries by exploiting the temporal smoothness and spatial similarity of battery pack data. Firstly, a temporal convolutional network (TCN) is utilized to learn the latent spatial and temporal features of the data. Then, a self-attention mechanism is employed to capture the correlations and importance between different features. Furthermore, an autoencoder is employed to reconstruct input data based on the extracted spatial-temporal features. This encoder-structured approach is trained only using normal data and the anomalies are detected as conspicuous differences between the input data and the reconstructed data. The detection performance of the proposed method is validated by utilizing real-world electric vehicle operational datasets.

Temperature heavily affects the lifespan and performance of batteries. High temperatures accelerate capacity degradation and can cause thermal runaway, highlighting the importance of battery cooling strategies for electric vehicles. This paper proposes the battery cooling strategy for electric vehicles with LiFePO4batteries using the twin delayed deep deterministic policy gradient (TD3) algorithm. Firstly, the electric-thermal-aging and active battery thermal management system models are introduced, and the thermal management problem is formulated as a continuous Markov decision process. Then, a reward function is designed based on prolonging battery life, reducing refrigeration cost, and maintaining battery temperature. Finally, a TD3 algorithm based on the double-delayed update mechanism is designed to obtain the optimal battery cooling strategy in the continuous state-action space. Simulation results demonstrate that the proposed strategy outperforms the traditional methods and closes to the optimal benchmark, offline dynamic programming, with battery capacity loss of less than 3.32% and an SoC consumption of less than 0.55%, significantly reducing the operational cost and mitigating the battery aging.

It is a significant challenge to reconcile the competing interests of individual microgrid units when energy is shared in a multi-energy system. Moreover, the majority of these systems solely focus on power sharing, without adequately addressing the associated carbon emissions. This paper puts forward a sharing approach that makes use of multi-agent collaboration to optimise energy sharing in multi-energy systems and achieve low-carbon operation. Firstly, a system model is constructed to describe the interrelationship between each microgrid and the energy-sharing platform. Subsequently, a shared pricing mechanism will be devised to incentivise each microgrid to prioritise its participation in the local sharing market. The shared pricing mechanism is employed to construct the microgrid utility maximisation problem and transform it into a Markov game process. A multi-agent cooperative approach is put forth as a means of optimising the energy-sharing strategy. Ultimately, the simulation results demonstrate that this energy-sharing strategy can satisfy the operational constraints, maintain equilibrium between supply and demand, reduce energy costs, and promote the economic and low-carbon operation of the microgrid.

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.

In truck platoons with a bidirectional-leader topology, variations in channel conditions result in unreliability and high latency in vehicle-to-vehicle (V2V) communications. This paper proposes an adaptive relay selection strategy based on Q-learning (QL). The strategy ensures that all vehicles in the platoon receive safety messages from the lead vehicle quickly and reliably. Firstly, relay selection is modeled as a Markov decision process (MDP). The lead vehicle and the relays act as intelligent agents. Agents make decisions adaptively based on real-time state observations in a dynamic communication environment. Secondly, a reward function is designed based on platoon topology and channel state information statistics (CSI). The purpose is to drive the proposed strategy to learn the optimal strategy for message transmission under different environments. Lastly, the simulation results demonstrate the effectiveness and robustness of the proposed algorithm. In various channel attenuation environments, the strategy has been demonstrated to enhance the packet delivery ratio (PDR) for the platoon tail and significantly increase the platoon’s throughput.

Lithium-ion batteries are increasingly critical in portable and electrical technologies, making accurate estimations of their State of Health essential for extending battery life and ensuring safety. This study proposes a novel State of Health estimation model that integrates transfer learning with a Retentive Network architecture, addressing the limitations of traditional methods in data processing and feature extraction. By pre-training a model adaptable to various battery types, the approach leverages the Retentive Network’s powerful temporal data processing capabilities, enabling end-to-end application to raw data and reducing the need for complex preprocessing. The model utilizes a dual-stream parallel network architecture to extract valuable information from both charging and discharging data, significantly enhancing the accuracy of State of Health estimation. Furthermore, the model can quickly adapt to new or sparsely sampled battery types through pre-training and fine-tuning strategies, minimizing reliance on large labeled datasets. Compared with existing transfer learning approaches, the proposed model demonstrates superior performance in terms of accuracy and robustness, particularly in handling long-term degradation patterns. Extensive experiments on multiple public datasets show that the model consistently achieves a Mean Absolute Error below 0.90% and a Root Mean Square Error below 0.95%, confirming its effectiveness and precision. This research provides novel insights and methodologies for optimizing battery performance and improving management practices.

In the frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems, channel state information (CSI) needs to be fed back to the base station (BS) by the resource-limited user equipment (UE). Although deep learning (DL)-based methods have been widely applied to improve performance of CSI feedback in FDD massive MIMO systems, there is still potential for improvement in feedback accuracy and computational complexity. In this paper, we propose a lightweight channel state information feedback scheme, aiming to improve feedback performance and reduce complexity of model. This scheme utilizes lightweight depthwise separable convolution to process channel state information for reducing information loss during compression, and employs a multi-scale feature and residual learning techniques to recover and reconstruct CSI. Simulation results demonstrate that proposed feedback scheme exhibits better feedback accuracy compared to other lightweight DL-based networks.

In massive multiple-input multiple-output (MIMO) systems, the accuracy of the downlink channel state information (CSI) plays an important role in improving system performance. In frequency division duplexing (FDD) mode, CSI has to be sent back by user equipment (UE) due to lack of channel reciprocity, which is challengeable. Deep learning (DL) based methods have been proposed to improve the recovery and reconstruction performance of CSI feedback. In DL-based methods, although transformer-based models have achieved the state-of-the-art performance, these models have high computational complexity. In this paper, we propose a shift window transformer framework to improve CSI feedback performance, which reduces the amount of calculation by calculating self-attention in sub-windows. Experimental results show that the proposed model has good trade-off between feedback performance and computation complexity, and outperforms other methods at compression ratios of 1/16 and 1/64 under indoor scenarios.

Accurate trajectory prediction is crucial in autonomous driving to ensure safe and efficient navigation, yet effectively modeling complex interactions among multiple agents remains a significant challenge. Many existing methods still suffer from over-reliance on HD maps, high computational cost, and a lack of interpretability in interaction reasoning. In response, the proposed model innovatively incorporates counterfactual reasoning into social interaction modeling to tackle the challenges of interaction-aware multi-agent trajectory prediction, prioritizing both accuracy and efficiency. In light of the spatiotemporal interaction mechanism and the inherent human cognition governing agents’ motion, the approach simultaneously generates multi-modal trajectories for all agents in a scenario, providing a novel perspective for modeling social interactions through causal reasoning. The results demonstrate that our map-free, lightweight trajectory prediction model rivals the performance of state-of-the-art methods and shows notable improvements over various baselines on publicly available real-world datasets.

In the current era, a Li-ion battery pack, typically comprised of multiple cells, can offer higher voltage and output power. This plays a crucial role in various applications, including electric vehicles and energy storage. Accurate estimating the battery pack's state of charge (SOC) is crucial to offer users a clearer understanding of the battery status and to alleviate range anxiety. In the industry, it's common practice to precisely estimate the SOC for each cell, enabling an accurate assessment of the battery pack's overall SOC. However, most current methods for estimating the SOC in battery packs are centralized. In such cases, a problem with estimating the SOC of a single cell can greatly impact the overall SOC estimation of the entire battery pack. Likewise, if centralized equipment encounters issues, the SOC estimation for the entire battery pack is likely to be interrupted. This paper presents a distributed method for estimating battery pack SOC, utilizing a digital twin-based simulation platform. In the following, the each node that measures the SOC of cell is regarded as an agent that can communicate. Through communication among agents, each agent can converge to a reliable battery pack SOC estimation. In the event of a sudden issue arising in the SOC estimation of a cell, the proposed method can still uphold a dependable estimate of the battery pack's SOC, thereby bolstering the overall robustness of the SOC estimation system for the entire battery pack.

To ensure optimal functionality of the super-capacitor management system in practical applications, the accurate and robust state of charge (SOC) estimation is crucial, particularly to account for aging effects and varying operating conditions. This paper proposes a switched system-based approach for the co-estimation of SOC and parameters of supercapacitors coupled with balancing resistor circuits. A switched model incorporating an equivalent circuit model is developed to accommodate the activation of equalization within a series-connected supercapacitor pack. The method combines a modified recursive least squares (RLS) algorithm with a switching sliding mode observer (SMO) for real-time parameter adaptation and SOC estimation. The experimental verification under a multi-balancing charging scenario demonstrates sig-nificant enhancements in accuracy and robustness compared to traditional methods employing fixed model configurations and parameters.

With the rapid development of Autonomous Rail Rapid Transit technology, lithium-ion battery as its main power source, the research of its charging technology has become particularly important. At present, the charging scheme of lithium-ion battery mainly includes two ways: single high-power charging and multiple low-power charging modules in parallel. However, the single high-power charging scheme has the problems of high cost and low efficiency, and the traditional parallel charging method lacks effective module management strategy, resulting in unbalanced load between modules during charging, affecting charging efficiency a nd safety. A iming at the shortcomings of current research, this paper proposes a parallel charging scheme of multiple DC-DC modules based on cooperative control. By designing a closed-loop control system of current inner loop and voltage outer loop, the precise control and cooperative work of parallel modules are realized. The experimental results show that the scheme not only improves the charging efficiency, but also ensures the stability and safety of the charging process, which provides an effective and reliable solution for the lithium-ion battery charging of the intelligent rail train.

This paper explores the estimation of the State of Charge (SoC) of lithium-ion batteries. Currently, the majority of research efforts focus on the SoC estimation of individual lithium-ion batteries. However, in practical scenarios, lithium-ion batteries are commonly connected with balancing circuits to address battery imbalances. Upon activation of the equalization circuit, the battery's system dynamics transition to a new mode. Therefore, it is difficult f o r c l assical S o C estimation algorithms to accurately estimate the real SoC value. In this paper, we employ a switched system methodology to estimate the battery's SoC. We describe the switched system of the Thevenin equivalent circuit model of a lithium-ion battery using a switched resistance balance circuit. Then we use the method of nonlinear switching observer to analyze the convergence and divergence. Finally, we set up an experimental platform and verify the performance of the observer through several sets of experiments.

With rubber wheels instead of steel wheels and no need to be guided by steel rails, Autonomous rail Rapid Transit(ART), is gradually coming into people's lives. However, ART still occupies existing lanes and the relatively small station spacing of ART means that ART needs to be started and stopped frequently, all of which can lead to fluctuations in DC bus voltage during ART operation, making it difficult for loads (such as motors, air conditioners, and sensors) to operate properly. Therefore, this paper proposes the use of predictive set-point modulation to suppress DC bus voltage fluctuations. The predictive set-point modulation method is able to predict the direction of DC bus voltage changes prospectively, and then adjust the voltage preset value to balance the fluctuation of the output voltage, optimizing the closed-loop system's transient dynamic performance. Moreover, since lithium ion battery has high power and high energy consumption, using only one DC-DC circuit can reduce system reliability and cost. Therefore, we propose to use parallel DC-DC modules to balance the excessive power of the battery and verify the feasibility of the proposed method through simulation experiments. The experiments show that the proposed method can effectively suppress the DC bus voltage fluctuation and improve the system reliability.

Predicting accurate capacity degradation and re-maining useful life (RUL) of lithium-lon battery is critical to health management and safe operation. However, variations in operating conditions and the variety of battery types present challenges to data-driven predictive models. Most data-driven methods rely on traditional machine learning models, which often have constrained predictive and generalization abilities. In this paper, a foundation model: Lag-Llama is used to predict capacity and RUL of battery with auto-correlation analysis. Firstly, the tokenization scheme of Lag-Llama is improved by auto-correlation analysis, which calculate the most probable periods in history capacity sequence. It is helpful for model to comprehend the capacity fluctuation pattern. Then, Lag-Llama is pre-trained to learn battery capacity degradation, and thus calculate the RUL. Additionally, the model is fine-tuned with a small amount of data to update the top-level module for application to the target cell. Finally, experimental results show that the proposed model exhibits accurate RUL prediction and strong transfer capability, within the average mean square error and absolute error less than 0.035 and 9 respectively.

The State-of-Charge (SOC) estimation for super-capacitors has been thoroughly examined in the literature, while the majority of the research to far is concentrating on the SOC estimation of single supercapacitor units. Nevertheless, the system dynamics of the battery may shift to a different system when utilizing the recently suggested reconfigurable circuit, suggesting that the straightforward use of current SOC estimate techniques is not possible. In order to assess the battery's state of charge (SOC), we use a switching systems technique in this paper. We establish the supercapacitor's RC model with a reconfigurable circuit and carefully investigate the continuity of the state and observability of the switched system. Afterwards, we propose a switching observer and compare the performance of various observers, analyzing its convergence qualities. We compare the proposed observer with other observers through a hardware platform, and the experimental results prove the superiority of the proposed observer in SOC estimation.

Supercapacitors, leveraging their distinctive characteristics and advantages, have evolved into efficient energy storage solutions. State of Charge (SOC) is a crucial parameter for supercapacitors, and the estimation of SOC for individual supercapacitor cells has been extensively researched. In practical applications, it is common to assemble hundreds or even thousands of cells to form a supercapacitor pack, particularly in fields like electric vehicles a nd electric b uses. Therefore, estimating the SOC for the supercapacitor pack becomes imperative. In response to the demands for supercapacitor pack SOC$(SOC_{pack})$estimation and wireless management, this paper proposes a distributed method. After modeling the supercapacitor cells, the definition of$SOC_{pack}$is introduced. The SOC of a cell in the definition is estimated based on Kalman filter. The proposed distributed method relies on wireless communication and computational updates between cells. Through iterative processes, it ultimately converges to the estimated$SOC_{pack}$. Finally, we conducted simulation experiments to analyze the performance of the proposed method under various communication conditions, thereby validating its effectiveness and robustness.

The integration of advanced communication and information technologies in smart grids has led to enhanced efficiency and reliability but also introduced security vulnera-bilities, prompting the need for robust cyber attack detection methods. Traditional approaches struggle to capture evolving attack patterns and handle high-dimensional data, highlighting the necessity for more sophisticated approaches. A neighbor-hood reconstruction-based smart grid attack detection scheme based on subgraphs is proposed. By leveraging Graph Neural Networks (GNNs), the challenge of capturing complex inter-dependencies among grid nodes is addressed. This approach employs unsupervised learning principles, training the model solely on normal data and utilizing the reconstruction error of node features to detect attacks. Additionally, by subgraph sampling and feature suppression, the model's ability to utilize neighborhood information is enhanced, thereby further improving detection effectiveness. Simulation results on IEEE 30-bus and IEEE 118-bus power system demonstrate the feasibility of the method, achieving a detection accuracy of 96.67% and 97.46%, respectively.

Accurate modeling of open circuit voltage hysteresis for LiFePO4batteries is crucial for establishing an advanced battery model. However, existing hysteresis modeling methods often yield suboptimal results due to inadequate parameterization. This paper proposes an optimal modeling method for open circuit voltage hysteresis based on the Prandtl-Ishlinskii model and an associated parameterization method. First, an asymmetric operator with cubic envelope functions is designed to enhance the classical Prandtl-Ishlinskii model, which originally features a symmetric and linear operator. This modification enables the proposed model to accurately capture intricate hysteresis. Second, a hierarchical parameterization method is proposed to identify optimal parameters. Specifically, an improved grey wolf optimizer is employed to determine the operator-related parameters. Then, the remaining parameters are calculated using the least squares algorithm, enhancing computational efficiency. Finally, the proposed model is validated on the experimental hysteresis data from three distinct scenarios. The modeling error of the proposed model decreased by 66.57 % and 32.51 % compared with two other benchmark models.

Faults in braking actuators can compromise the safety and stability of urban rail train operations. Existing fault-tolerant control methods for trains struggle to guarantee both transient and steady-state braking performance quantitatively. In this paper, we propose a cooperative fault-tolerant braking control with prescribed performance for urban rail trains. A coupled multi-agent braking model is first developed, where each vehicle is treated as an independent and controllable agent subject to various uncertainties, input saturation and different levels of actuator faults. Further, incorporating a prescribed tracking performance function, a distributive adaptive terminal sliding mode controller is developed to ensure safe and reliable train braking control. The control input saturation nonlinearity is addressed by employing a smooth hyperbolic tangent function for approximation. Adaptation laws are introduced to mitigate the effects of parameter uncertainties and external disturbances. The efficacy of the proposed control scheme is validated through comprehensive numerical simulations.

Model predictive control is a real-time energy management method for hybrid energy storage systems, whose performance is closely related to the prediction horizon. However, a longer prediction horizon also means a higher computation burden and more predictive uncertainties. This paper proposed a predictive energy management strategy with an optimized prediction horizon for the hybrid energy storage system of electric vehicles. Firstly, the receding horizon optimization problem is formulated to minimize the battery degradation cost and traction electricity cost for the electric vehicle operation. Then, the optimal control sequence is solved to obtain the power allocation between the battery and the supercapacitor. Furthermore, the effect of different horizons on the optimization results is analyzed under diverse operating conditions, determining the optimal horizon to balance the system costs and computation burden. Compared with the short horizon, the optimal horizon can achieve 5.2%$\sim$8.5% performance improvement with the acceptable computation time approaching 1 s.

Battery degradation is a main hinder to extend the persistent lifespan of the portable heterogeneous computing device. Excessive energy consumption and prominent current fluctuations can lead to a sharp decline of battery endurance. To address this issue, a battery-aware workflow scheduling algorithm is proposed to maximize the battery lifetime and release the computing potential of the device fully. Firstly, a dynamic optimal budget strategy is developed to select the highest cost-effectiveness processors to meet the deadline of each task, accelerating the budget optimization by incorporating deep neural network. Second, an integer-programming greedy strategy is utilized to determine the start time of each task, minimizing the fluctuation of the battery supply current to mitigate the battery degradation. Finally, a long-term operation experiment and Monte Carlo experiments are performed on the battery simulator, SLIDE. The experimental results under real operating conditions for more than 1800 hours validate that the proposed scheduling algorithm can effectively extend the battery life by 7.31%-8.23%. The results on various parallel workflows illustrate that the proposed algorithm has comparable performance with speed improvement over the integer programming method.

With the rapid development of electric vehicle industry, the battery management system of electric vehicle is the focus of research. Battery management is not only related to the safe driving of electric vehicles, but also the basis of intelligent driving of electric vehicles. The state-of-charge (SoC) estimation of battery is very important in battery management system. The battery is in a state of power consumption when the electric vehicle is running, but when the electric vehicle is braked, the kinetic energy will also be converted into electric energy to charge the battery. The acceleration and braking of electric vehicles are frequently switched. Therefore, the working conditions of electric vehicle batteries are complex, and the influence of battery hysteresis on the accuracy of SoC estimation cannot be ignored. In this paper, a lithium ion battery model considering hysteresis effect based on machine learning is proposed. The experiment was designed to collect the data of small cycle charge and discharge of the battery. The data were used to train the long short-term memory (LSTM) neural network model, and a battery model with hysteresis effect was obtained. It is verified that the model performs well in the test set, and the error of hysteresis voltage can be reduced to 0.002V. This model can be used for SoC estimation considering hysteresis effect.

Caching has been considered as a promising solution to achieve low latency in distributed erasure coded storage systems. The previous research work categorizes all feasible caching decisions into a set of cache partitions, and then obtains the optimal solution by applying the market clearing price on each cache partition. While enjoying the ultimate performance of low data access latency, the optimal scheme suffers from high computation overheads when applied to large-scale storage systems. This paper presents SampleX, which constructs the sparsification of cache partitions through sampling to approximate the optimal caching scheme with substantially reduced computation complexity. Theoretical analysis guarantees the performance of SampleX. Furthermore, SampleX is implemented in a streaming fashion, capturing the characteristics of recent traffic for online cache content replacement. Trace-driven experimental results show that online SampleX is up to 95× faster than the state-of-the-art online scheme while only incurring a performance loss of 0.81%.

Reasonable power distribution between battery and supercapacitor in electric vehicles is a crucial problem to improve energy consumption and economy. An online energy management strategy based on model predictive control (MPC) is proposed in this paper. Firstly, a radial basis function neural network optimized by particle swarm algorithm is presented to generate the short-term future velocity, i.e., the reference trajectory of the MPC. Then, a cost function considering the battery degradation cost and the electricity cost is constructed and optimized within each prediction horizon while maintaining the state of charge of the supercapacitor. Simulation results on the UDDS driving cycle show that the total cost of the proposed strategy is reduced by 6.3% and 3.9% compared with the near-optimal rule-based strategy and the none optimized velocity predictor-MPC, respectively, indicating that the velocity prediction accuracy has a significant impact on the performance of real-time energy management.

The prediction of driving range is very important for electric bus, but there is usually a difficulty: battery aging affects the accuracy of driving range prediction. In order to solve this problem, this paper proposes a driving range prediction method for electric bus, which is robust to the battery aging effect. Firstly, we extract the features that affect the driving range from the real-world dataset, quantify the correlation between them and the driving range by grey correlation analysis. Then through the feature enhancement technology, the time window processing is used to mitigate the influence of battery aging, and the time information hidden in the historical period sequence is deeply excavated. On this basis, we establish the driving range prediction model based on k-nearest neighbors regression, where the key parameters are optimized with the particle swarm optimization algorithm. Numerous experimental results show that compared with the classical methods, the method proposed in this paper has higher prediction accuracy especially when the batteries undergo significant aging effects.

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 driving performance of electric vehicles seriously degrades due to the deterioration of lithium-ion batteries at low temperatures. Preheating lithium-ion batteries can effectively improve the driving range of electric vehicles at subzero temperatures. In this paper, an optimal pulse heating strategy is proposed for low-temperature heating of lithiumion battery. Firstly, this paper establishes a coupling model to describe the electro-thermal-aging behavior of battery. Secondly, the heating time and capacity loss jointly form a multi-objective optimization problem with the current constraint. The optimization problem is solved by using the particle swarm optimization(PSO) algorithm and the effect of weighting coefficient on heating performance is discussed to obtain the optimal pulse current. The results show that the proposed strategy can effectively reduce heating time without causing serious capacity reduction.

Supercapacitors have recieved increasing attentions in emerging portable power applications. The charging process of supercapacitors significantly affects the performance of both supercapacitors and chargers. Considering the charging time of supercapacitors is typically limited in practical applications, in this paper, we propose an optimal charging method for supercapacits with the limited charging time. Firstly, we analyze existing cell balancing and charging circuits, and adopt the switched resistor circuit. Then, we design a user-interactive optimal charging method for supercapacitors where the charging time can be specified by the users. The energy efficiency maximization of the proposed charging method is proved rigorously. A simulation charging platform has been established to verify the effectiveness of the proposed charging method. The simulation results show that the proposed charging method can effectively improve the energy efficiency under charging time constraints when compared with existing methods.

The filter-based real-time energy management method has been proved practical and widely utilized in hybrid energy storage systems. However, the determination for the cutoff frequency of the energy-split filter is challenging. In this paper, an optimal filter-based energy management strategy is proposed for a battery/ultracapacitor electric vehicle to minimize the total energy consumption. A cost function of energy consumption for the cutoff frequency is established first. Considering the working condition of ultracapacitors, dynamic programming is adopted to obtain the optimal cutoff frequency series, i.e., the optimal energy distribution between batteries and ultracapacitors. Such an off-line optimization process is carried out under different driving cycles, e.g., urban and highway road conditions. Optimization results are used to determine the optimal cutoff frequency of a real-time filter-based energy management strategy. Simulation results indicate that the proposed strategy can minimize the total energy consumption of the hybrid energy storage system with ultracapacitors state of charge limitations being guaranteed. Compared with the existing real-time energy management strategies, the energy consumption is reduced 23.85% under aggressive acceleration conditions and 7.08% under urban conditions by the proposed strategy.

Cell balancing is crucial for charging supercapacitor cells to prevent cells from over-charging. Most existing cell-balancing charging methods typically adopt an output feedback control, i.e., the terminal voltages of cells are directly utilized in the controller design. One limitation of these methods is the voltage drop effect when the charging is terminated, which degrades the system capacity and results in cell imbalance. To address this challenge, in this article, we propose an observer-driven charging method for supercapacitors. The switched resistor circuit is applied and is further modeled using the switched systems theory, where the RC model of cells is considered. The communication interactions among cells is modeled using the graph theory. A switching Luenberger observer is designed to estimate the voltage of the equivalent capacitor of each cell, and a consensus-based switching control law is designed to charge and balance supercapacitors. The closed-loop system model is derived using the block diagram. A laboratory testbed has been built to verify the effectiveness of the proposed charging method. Experimental results show that the proposed method can effectively alleviate the voltage drop effect when compared with existing charging methods.

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.

With the wide application of Lithium-ion (Li-ion) batteries in electric vehicles and unmanned aerial vehicles (UAVs), it is becoming more important and urgent to estimate the battery state to extend the operation range of electric vehicles or UAVs. Existing state of charge (SOC) estimation methods are highly model-based, which are difficult to be implemented in different scenarios. In this paper, we propose a generalized extended state observer (GESO) based SOC estimation method, where the accurate model is unnecessary, which can be effectively tracked by the observer. Thus, a first order RC model is utilized in GESO to capture the characteristics of Li-ion batteries. By appropriately designing a disturbance compensation gain, the GESO is applied for the nonintegral-chain system that is subject to uncertainties and nonlinear parameters of Li-ion batteries. Experiment results show that the proposed method has a good performance and robustness on SOC estimation of the battery. The SOC estimation results are found to be consistent with the reference SOC with less chattering than sliding mode observer, where the error is within 2% under both the known and unknown initial SOC value cases.