Search Results (394)

High-precision spiral trajectory tracking for aquaculture net-cage inspection is hindered by uncertain hydrodynamics, strong coupling, and time-varying disturbances acting on an underactuated autonomous underwater vehicle. This paper adapts and validates a model–data-driven learning-aided adaptive robust control strategy for the specific challenge of high-precision spiral trajectory tracking for aquaculture net-cage inspection. At the kinematic level, a serial iterative learning feedforward compensator is combined with a line-of-sight guidance law to form a feedforward-compensated guidance scheme that exploits task repeatability and reduces systematic tracking bias. At the dynamic level, an integrated adaptive robust controller employs projection-based, rate-limited recursive least-squares identification of hydrodynamic parameters, along with a composite feedback law that combines linear error feedback, a nonlinear robust term, and fast dynamic compensation to suppress lumped uncertainties arising from estimation error and external disturbances. A Lyapunov-based analysis establishes uniform ultimate boundedness of all closed-loop error signals. Simulations that emulate net-cage inspection show faster convergence, higher tracking accuracy, and stronger robustness than classical adaptive robust control and other baselines while maintaining bounded control effort. The results indicate a practical and effective route to improving the precision and reliability of autonomous net-cage inspection. Full article

Urban rivers, as crucial components of ecosystems, serve multiple functions, including flood control, drainage, and landscape services. However, with the acceleration of urbanization, factors such as industrial wastewater discharge, domestic sewage leakage, and surface runoff pollution have led to increasingly severe degradation of water quality in urban rivers. Unmanned aerial vehicle (UAV) remote sensing technology, with its sub-meter spatial resolution and operational flexibility, demonstrates significant advantages in the detailed monitoring of complex urban water systems. This study proposes an Ensemble Self-Attention Enhanced Mixture Density Network (ESA-MDN), which integrate an ensemble learning framework with a mixture density network and incorporates a self-attention mechanism for feature enhancement. This approach better captures the nonlinear relationships between water quality parameters and remote sensing features, achieving high-precision modeling of water quality parameter distributions. The resulting spatiotemporal distribution maps provide valuable support for pollution source identification and management decision making. The model successfully retrieved five water quality parameters, Chl-a, TSS, COD, TP, and DO, and validation metrics such as R², RMSE, MAE, MSE, MAPE, bias, and slope were utilized. Key metrics for the ESA-MDN test set were as follows: Chl-a (R² = 0.98, RMSE = 0.31), TSS (R² = 0.93, RMSE = 0.27), COD (R² = 0.93, RMSE = 0.39), TP (R² = 0.99, RMSE = 0.02), and DO (R² = 0.88, RMSE = 0.1). The results indicated that ESA-MDN can effectively extract water quality parameters from multispectral remote sensing data, with the generated spatiotemporal water quality distribution maps providing crucial support for pollution source identification and emergency response decision making. Full article

High-specific-energy lithium-ion batteries face accelerated degradation and safety risks. To ensure stable and safe operation of such batteries in electric vehicles throughout their service life, this study proposes a quantitative aging mechanism analysis method based on electrode equilibrium potential reconstruction under rest conditions. First, by integrating the single-particle electrochemical model with equilibrium potential reconstruction, a quantitative mapping framework between State of Charge (SOC) and electrode lithiation concentration is established. Subsequently, to address the strong nonlinearity between equilibrium potential and lithiation concentration, the State Transition Algorithm (STA) is introduced to solve the high-dimensional coupled parameter identification problem, enhancing aging parameter estimation accuracy. Finally, the effectiveness of the proposed method was validated using a commercial NCM622/graphite power cell as the research object, and the battery’s aging pathways were analyzed using differential voltage analysis (DVA) and incremental capacity analysis (ICA) methods. Experimental results indicate that the OCV curve fitting achieved a maximum Root Mean Square Error of 0.00932, while quantitatively revealing the degradation patterns of electrode lithiation degrees during aging under both fully charged (SOC = 100%) and fully discharged (SOC = 0%) states. Full article

Road surface conditions significantly impact driving safety and maintenance costs. Especially in connected and automated vehicles (CAVs), the road surface type recognition is critical for environmental perception. Traditional road surface recognition methods face limitations in feature extraction, so an improved one-dimensional convolutional neural network (1D-CNN) algorithm was proposed based on the VGG16 architecture. A vibration signal acquisition system was developed to efficiently acquire high-quality vehicle vibration signals. The optimized 1D-CNN algorithm model contains only 101.6 k parameters, significantly reducing computational cost and training time while maintaining high accuracy. Data augmentation, Adam optimization algorithm and L2 regularization were integrated to enhance generalization capabilities and suppress overfitting. On public datasets and actual vehicles tests, recognition accuracy rate reached 99.3% and 99.4%, respectively, substantially outperforming conventional methods. The algorithm also exhibited strong adaptability to different data sources. The research findings have implications for the accurate and efficient identification of road surfaces. Full article

Fractional-order calculus (FOC) has gained significant attention in electric vehicle (EV) energy storage and management systems, as it provides enhanced modeling and analysis capabilities compared to traditional integer-order approaches. This review presents a comprehensive survey of recent advancements in the application of FOC to EV energy storage systems, including lithium-ion batteries (LIBs), supercapacitors (SCs), and fuel cells (FCs), as well as their integration within energy management systems (EMS). The review focuses on developments in electrochemical, equivalent circuit, and data-driven models formulated in the fractional-order domain, which improve the representation of nonlinear, memory-dependent, and multi-scale dynamics of energy storage devices. It also discusses the benefits and limitations of current FOC-based models, identifies open challenges such as computational feasibility and parameter identification, and outlines future research directions. Overall, the findings indicate that FOC offers a robust framework with significant potential to advance next-generation EV energy storage and management systems. Full article

For electric vehicles widely used recently, lithium-ion batteries serve as the primary energy storage units, affecting the vehicles’ performance, safety, and lifespan. Accurate state of charge (SOC) estimation is pivotal for the battery management system (BMS) to enhance the predictability of the vehicle’s range and avert thermal runaway due to improper charging methods. In this study, an adaptive SOC estimation methodology was developed using parameter identification with forgetting factor recursive least squares (FFRLS). These parameters are then incorporated into a dual adaptive extended Kalman filter (DAEKF) for SOC estimation under varying load conditions. DAEKF is used to dynamically adjust the covariance matrices for process and measurement noises, significantly enhancing the filter’s adaptability and precision. The integration of FFRLS and DAEKF enables a robust SOC estimation of electric vehicles, featuring rapid computation speeds, high accuracy, and excellent adaptability, positioning them as ideal candidates for enhancements in battery management system technology. Full article

This paper presents a novel electro-thermal modeling approach for a lithium-ion battery pack in an electric vehicle (EV), along with parameter identification using controller area network (CAN) data collected from chassis dynamometer and real-world driving tests. The proposed electro-thermal model consists of a first-order equivalent circuit model (ECM) and a lumped-parameter thermal network in considering a simplified cooling circuit layout and temperature distributions across four distinct zones within the battery pack. This model captures the nonuniform heat transfer between the pack modules and the coolant, as well as variations in coolant temperature and flow rates. Model parameters are identified directly from vehicle-level test data without relying on laboratory-level measurements. Validation results demonstrate that the model can predict terminal voltage with an RMSE of less than 6 V (normalized root mean square error of less than 2%), and battery module surface temperatures with root mean square errors of less than 2 °C for over 90% of the test cases. The proposed approach provides a cost-effective and accurate solution for predicting electro-thermal behavior of EV battery systems, making it a valuable tool for battery design and management to optimize performance and ensure the safety of EVs. Full article

This study proposes a system identification (SI) technique based on the constrained recursive least squares (CRLS) method to model the dynamics of the P-SUROII. By simplifying the dynamic model in consideration of the inherent characteristics of underwater vehicles and minimizing the number of parameters to be estimated, the proposed approach aims to improve estimation accuracy. In addition, a simplified thruster input model was applied to quantify the actual thruster output and improve the reliability of the input data. To satisfy the persistent excitation (PE) condition during the estimation process, experiments incorporating various motion modes were designed, and free-running and S-shaped maneuvering tests were additionally conducted to validate the model’s generalization capability and prediction performance. The coefficients estimated using the CRLS method, which is robust to noise and bias, were evaluated using quantitative similarity metrics such as root mean squared error (RMSE) and mean absolute error (MAE), confirming their validity. The proposed method effectively captures the actual dynamics of the underwater vehicle and is expected to serve as a key enabling technology for the future development of high-performance control systems and autonomous operation systems. Full article

Due to their advantages, such as abundant raw material reserves, excellent thermal stability, and superior low-temperature performance, sodium-ion batteries (SIBs) exhibit significant potential for future applications in energy storage and electric vehicles. Therefore, in this study, a commercial pouch-type SIB with sodium iron sulfate cathode material was investigated. Firstly, a second-order RC equivalent circuit model was established through parameter identification using multi-rate hybrid pulse power characterization (M-HPPC) tests at various temperatures. Then, both the specific heat capacity and entropy coefficient of the sodium-ion battery were measured through experiments. Building upon this, an electro-thermal coupling model was developed by incorporating a lumped-parameter thermal model that accounts for the heat generation of the tabs. Finally, the prediction performance of this model was validated through discharge tests under different temperature conditions. The results demonstrate that the proposed electro-thermal coupling model can achieve the simultaneous prediction of both temperature and voltage, providing valuable references for the future development of thermal management systems for SIBs. Full article

The parameter identification of Autonomous Underwater Vehicles (AUVs) serves as a fundamental basis for achieving high-precision motion control, state monitoring, and system development. Currently, AUV parameter identification typically relies on the complete motion information obtained from onboard sensors. However, in practical applications, it is often challenging to accurately measure key state variables such as velocity and angular velocity, resulting in incomplete measurement data that compromises identification accuracy and model reliability. This issue is particularly pronounced in vertical motion tasks involving low-speed, large pitch angles, and highly maneuverable conditions, where the strong coupling and nonlinear characteristics of underwater vehicles become more significant. Traditional hydrodynamic models based on full-state measurements often suffer from limited descriptive capability and difficulties in parameter estimation under such conditions. To address these challenges, this study investigates a parameter identification method for AUVs operating under vertical, large-amplitude maneuvers with constrained measurement information. A control autoregressive (CAR) model-based identification approach is derived, which requires only pitch angle, vertical velocity, and vertical position data, thereby reducing the dependence on complete state observations. To overcome the limitations of the conventional Recursive Least Squares (RLS) algorithm—namely, its slow convergence and low accuracy under rapidly changing conditions—a Multi-Innovation Least Squares (MILS) algorithm is proposed to enable the efficient estimation of nonlinear hydrodynamic characteristics in complex dynamic environments. The simulation and experimental results validate the effectiveness of the proposed method, demonstrating high identification accuracy and robustness in scenarios involving large pitch angles and rapid maneuvering. The results confirm that the combined use of the CAR model and MILS algorithm significantly enhances model adaptability and accuracy, providing a solid data foundation and theoretical support for the design of AUV control systems in complex operational environments. Full article

The growth of urban and industrial areas is accompanied by an increase in vehicle traffic, resulting in rising concentrations of various air pollutants. This is a global issue that causes environmental damage and risks to human health. The dataset presented in this research contains records with measurements of the air pollutants ozone (O₃) and carbon monoxide (CO), as well as meteorological parameters such as temperature (T), relative humidity (RH), and barometric pressure (BP). This dataset was collected using a set of low-cost sensors over a four-month study period (March to June) in 2024. The monitoring of air pollutants and meteorological parameters was conducted in a city with high industrial activity, heavy traffic, and close proximity to a petrochemical refinery plant. The data were subjected to a series of statistical analyses for visualization using plots that allow for the identification of their behavior. Finally, the dataset can be utilized for air quality studies, public health research, and the development of prediction models based on mathematical approaches or artificial intelligence algorithms. Full article

Sensor fusion in intelligent suspension systems constitutes a fundamental technology for optimizing vehicle dynamic stability, ride comfort, and occupant safety. By integrating data from multiple sensor modalities, this study proposes a hierarchical multi-sensor fusion framework for active suspension control, aiming to enhance control precision. Initially, a binocular vision system is employed for target detection, enabling the identification of lane curvature initiation points and speed bumps, with real-time distance measurements. Subsequently, the integration of Global Positioning System (GPS) and inertial measurement unit (IMU) data facilitates the extraction of road elevation profiles ahead of the vehicle. A BP-PID control strategy is implemented to formulate mode-switching rules for the active suspension under three distinct road conditions: flat road, curved road, and obstacle road. Additionally, an ant colony optimization algorithm is utilized to fine-tune four suspension parameters. Utilizing the hardware-in-the-loop (HIL) simulation platform, the observed reductions in vertical, pitch, and roll accelerations were 5.37%, 9.63%, and 11.58%, respectively, thereby substantiating the efficacy and robustness of this approach. Full article

Reducing gear noise in electric vehicle (EV) drivetrains is crucial due to the absence of internal combustion engine noise, making even minor acoustic disturbances noticeable. Manufacturing parameters significantly influence gear-generated noise, yet traditional analytical methods often fail to predict these complex relationships accurately. This research addresses this gap by introducing a data-driven approach using machine learning (ML) to predict gear noise levels from manufacturing and sensor-derived data. The presented methodology encompasses systematic data collection from various production stages—including soft and hard machining, heat treatment, honing, rolling tests, and end-of-line (EOL) acoustic measurements. Predictive models employing Random Forest, Gradient Boosting (XGBoost), and Neural Network algorithms were developed and compared to traditional statistical approaches. The analysis identified critical manufacturing parameters, such as surface waviness, profile errors, and tooth geometry deviations, significantly influencing noise generation. Advanced ML models, specifically Random Forest, XGBoost, and deep neural networks, demonstrated superior prediction accuracy, providing early-stage identification of gear units likely to exceed acceptable noise thresholds. Integrating these data-driven models into manufacturing processes enables early detection of potential noise issues, reduces quality assurance costs, and supports sustainable manufacturing by minimizing prototype production and resource consumption. This research enhances the understanding of gear noise formation and offers practical solutions for real-time quality assurance. Full article

With the rapid development of vehicular network technology, data sharing and collaborative training among vehicles have become key to enhancing the efficiency of intelligent transportation systems. However, the heterogeneity of data and potential Byzantine attacks cause the model to update in different directions during the iterative process, causing the boundary between benign and malicious gradients to shift continuously. To address these issues, this paper proposes a privacy-preserving Byzantine-tolerant federated learning scheme. Specifically, we design a gradient detection method based on median absolute deviation (MAD), which calculates MAD in each round to set a gradient anomaly detection threshold, thereby achieving precise identification and dynamic filtering of malicious gradients. Additionally, to protect vehicle privacy, we obfuscate uploaded parameters to prevent leakage during transmission. Finally, during the aggregation phase, malicious gradients are eliminated, and only benign gradients are selected to participate in the global model update, which improves the model accuracy. Experimental results on three datasets demonstrate that the proposed scheme effectively mitigates the impact of non-independent and identically distributed (non-IID) heterogeneity and Byzantine behaviors while maintaining low computational cost. Full article

Multibeam echosounder systems (MBESs) are widely used in unmanned surface vehicle swarms (USVs) to perform various marine bathymetry surveys because of their excellent performance. To address the challenges of systematic error superposition and edge beam error propagation in multibeam bathymetry surveying, this study proposes a novel error adjustment method integrating crossover error density clustering and beam incident angle (BIA) compensation. Firstly, a bathymetry error detection model was developed based on adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN). By optimizing the neighborhood radius and minimum sample threshold through analyzing sliding-window curvature, the method achieved the automatic identification of outliers, reducing crossover discrepancies from ±150 m to ±50 m in the deep sea at a depth of approximately 5000 m. Secondly, an asymmetric quadratic surface correction model was established by incorporating the BIA as a key parameter. A dynamic weight matrix ω = 1/(1 + 0.5θ²) was introduced to suppress edge beam errors, combined with Tikhonov regularization to resolve ill-posed matrix issues. Experimental validation in the Western Pacific demonstrated that the RMSE of crossover points decreased by about 30.4% and the MAE was reduced by 57.3%. The proposed method effectively corrects residual systematic errors while maintaining topographic authenticity, providing a reference for improving the quality of multibeam bathymetric data obtained via USVs and enhancing measurement efficiency. Full article