Search Results (23)

Electrochemical energy storage technology, primarily lithium-ion batteries, has been widely applied in large-scale energy storage systems. However, differences in assembly structures, manufacturing processes, and operating environments introduce parameter inconsistencies among cells within a pack, producing complex, high-volume datasets with redundant and fragmented charge–discharge records that hinder efficient and accurate system monitoring. To address this challenge, we propose a hybrid ASW-UKF-TRF framework for the classification and compression of battery data collected from energy storage power stations. First, an adaptive sliding-window Unscented Kalman Filter (ASW-UKF) performs online data cleaning, imputation, and smoothing to ensure temporal consistency and recover missing/corrupted samples. Second, a temporally aware TRF segments the time series and applies an importance-weighted, multi-level compression that formally prioritizes diagnostically relevant features while compressing low-information segments. The novelty of this work lies in combining deployment-oriented engineering robustness with methodological innovation: the ASW-UKF provides context-aware, online consistency restoration, while the TRF compression formalizes diagnostic value in its retention objective. This hybrid design preserves transient fault signatures that are frequently removed by conventional smoothing or generic compressors, while also bounding computational overhead to enable online deployment. Experiments on real operational station data demonstrate classification accuracy above 95% and an overall data volume reduction in more than 60%, indicating that the proposed pipeline achieves substantial gains in monitoring reliability and storage efficiency compared to standard denoising-plus-generic-compression baselines. The result is a practical, scalable workflow that bridges algorithmic advances and engineering requirements for large-scale battery energy storage monitoring. Full article

This paper presents a novel data-driven framework for real-time State of Charge (SOC) estimation in lithium-ion battery systems using a data-driven Prognostics and Health Management (PHM) approach. The method leverages an optimized bidirectional Long Short-Term Memory (Bi-LSTM) network, trained with enhanced datasets filtered via exponentially weighted moving averages (EWMAs) and refined through SHAP-based feature attribution. Compared against a Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) across ten diverse drive cycles, the proposed model consistently achieved superior performance, with mean absolute errors (MAEs) as low as 0.40%, outperforming EKF (0.66%) and UKF (1.36%). The Bi-LSTM model also demonstrated higher R² values (up to 0.9999) and narrower 95% confidence intervals, confirming its precision and robustness. Real-time implementation on embedded platforms yielded inference times of 1.3–2.2 s, validating its deployability for edge applications. The framework’s model-free nature makes it adaptable to other nonlinear, time-dependent systems beyond battery SOC estimation. Full article

To address multiple disturbance threats such as system anomalies and cyberattacks faced by power systems, an intelligent optimized secure filter method is developed in this paper for state estimation of power systems with the aid of the improved sparrow search algorithm–optimized unscented Kalman filter (ISSA-UKF). Firstly, the problem of insufficient robustness in noise modeling and parameter selection of the conventional unscented Kalman filter (UKF) is analyzed. Secondly, an intelligent optimization method is adopted to adaptively update the UKF’s process and measurement noise covariances in real time, and an ISSA-UKF fusion framework is constructed to improve the estimation accuracy and system response capability. Thirdly, an adaptive weight function based on disturbance observation differences is provided to strengthen the stability of the algorithm in response to abnormal measurements at edge nodes and dynamic system changes. Finally, simulation analysis under a typical power system model shows that compared with the conventional UKF method, the developed ISSA-UKF algorithm demonstrates significant improvements in estimation accuracy, robustness, and dynamic response performance and can effectively cope with non-ideal disturbances that may occur in power systems. Full article

To address the challenge of accurate apple harvesting by orchard robots, which is hindered by dynamic changes in apple position due to wind interference and branch swaying, this study proposes an optimized prediction algorithm based on an integration of the extended Kalman filter (EKF) and an improved particle filter (IPF), built upon initial apple detection and recognition using YOLOv8. The algorithm first employs spatial partitioning according to the cyclical motion patterns of apples to constrain the prediction results. Subsequently, it optimizes the rationality of particle weights within the particle filter (PF) and reduces its computational resource consumption by implementing historical position weighting and an adaptive particle number strategy. Finally, an adaptive error correction mechanism dynamically adjusts the respective weights of the EKF and IPF components, continuously enhancing the algorithm’s prediction accuracy. Experimental results demonstrate that, compared to the classic unscented Kalman filter (UKF) and unscented particle filter (UPF), the proposed EK-IPF algorithm reduces the mean absolute error (MAE) by 22.25% and 10.89%, respectively, and the root mean square error (RMSE) by 23.70% and 13.25%, respectively, indicating a significant improvement in overall prediction accuracy. This research provides technical support for dynamic apple trajectory prediction in orchard environments. Full article

Robust robot manipulation hinges on effective state estimation. The VRT 6 robot leverages an inertia measurement unit with triaxial gyroscopes, magnetometers, and accelerometers, as well as a position sensor, but these sensors are plagued by noise that demands rigorous filtering. To tackle this, an adaptively scaled unscented Kalman filter was employed. The filter’s scaling parameter was meticulously optimized using density- and moment-based techniques, as both system properties and estimated state impact this crucial parameter. A Maximum Likelihood Estimation (ML) substantiates the enhanced quality of the estimated velocity and acceleration, on par with the position estimate. Minimizing measurement prediction error (MMPE) also shows better results with less RMSE when compared to fixed-kappa values, and the quality of position estimates is higher with the increase in the domain of the scaling parameter. By carefully selecting the adaptive scaling parameters’ range to minimize sigma point weights and ensure the positive definiteness of the covariance matrix, this enhanced UKF method achieved markedly superior state estimates compared to standard UKF implementations. Full article

Under large azimuth misalignment conditions, the initial alignment of strapdown inertial navigation systems (SINS) is challenged by the nonlinear characteristics of the error model. Traditional particle filter (PF) algorithms suffer from the inappropriate selection of importance density functions and severe particle degeneration, which limit their applicability in high-precision navigation. To address these limitations, this paper proposes an adaptive mixed-order spherical simplex-radial cubature particle filter (MLG-AMSSRCPF) algorithm based on matrix Lie group representation. In this approach, attitude errors are represented on the matrix Lie group SO(3), while velocity errors and inertial sensor biases are retained in Euclidean space. Efficient bidirectional conversion between Euclidean and manifold spaces is achieved through exponential and logarithmic maps, enabling accurate attitude estimation without the need for Jacobian matrices. A hybrid-order cubature transformation is introduced to reduce model linearization errors, thereby enhancing the estimation accuracy. To improve the algorithm’s adaptability in dynamic noise environments, an adaptive noise covariance update mechanism is integrated. Meanwhile, the particle similarity is evaluated using Euclidean distance, allowing the dynamic adjustment of particle numbers to balance the filtering accuracy and computational load. Furthermore, a multivariate Huber loss function is employed to adaptively adjust particle weights, effectively suppressing the influence of outliers and significantly improving the robustness of the filter. Simulation and the experimental results validate the superior performance of the proposed algorithm under moving-base alignment conditions. Compared with the conventional cubature particle filter (CPF), the heading accuracy of the MLG-AMSSRCPF algorithm was improved by 31.29% under measurement outlier interference and by 39.79% under system noise mutation scenarios. In comparison with the unscented Kalman filter (UKF), it yields improvements of 58.51% and 58.82%, respectively. These results demonstrate that the proposed method substantially enhances the filtering accuracy, robustness, and computational efficiency of SINS, confirming its practical value for initial alignment in high-noise, complex dynamic, and nonlinear navigation systems. Full article

Achieving accurate positioning of maglev trains is one of the key technologies for the safe operation of maglev trains and train schedules. Aiming at magnetic levitation train positioning, there are problems such as being easily interfered with by external noise, the single positioning method, and traditional weighting affected by historical data, which lead to the deviation of positioning fusion results. Therefore, this paper adopts self-corrected weighting and Sage–Husa noise estimation algorithms to improve them and proposes a research method of multi-sensor information fusion and positioning of an AUKF magnetic levitation train based on self-correcting weighting. Multi-sensor information fusion technology is applied to the positioning of maglev trains, which does not rely on a single sensor. It combines the Sage–Husa algorithm with the unscented Kalman filter (UKF) to form the AUKF algorithm using the data collected by the cross-sensor lines, INS, Doppler radar, and GNSS, which adaptively updates the statistical feature estimation of the measurement noise and eliminates the single-function and low-integration shortcomings of the various modules to achieve the precise positioning of maglev trains. The experimental results point out that the self-correction-based AUKF filter trajectories are closer to the real values, and their ME and RMSE errors are smaller, indicating that the self-correction-weighted AUKF algorithm proposed in this paper has significant advantages in terms of stability, accuracy, and simplicity. Full article

To enhance the path-tracking accuracy of unmanned articulated road roller (UARR) operating on low-adhesion, slippery surfaces, this paper proposes a hierarchical cascaded control (HCC) architecture integrated with real-time ground adhesion coefficient estimation. Addressing the complex nonlinear dynamics between the two rigid bodies of the vehicle and its interaction with the ground, an upper-layer nonlinear model predictive controller (NMPC) is designed. This layer, based on a 4-degree-of-freedom (4-DOF) dynamic model, calculates the required steering torque using position and heading errors. The lower layer employs a second-order sliding mode controller (SOSMC) to precisely track the steering torque and output the corresponding steering wheel angle. To accommodate the anisotropic and time-varying nature of slippery surfaces, a strong-tracking unscented Kalman filter (ST-UKF) observer is introduced for ground adhesion coefficient estimation. By dynamically adjusting the covariance matrix, the observer reduces reliance on historical data while increasing the weight of new data, significantly improving real-time estimation accuracy. The estimated adhesion coefficient is fed back to the upper-layer NMPC, enhancing the control system’s adaptability and robustness under slippery conditions. The HCC is validated through simulation and real-vehicle experiments and compared with LQR and PID controllers. The results demonstrate that HCC achieves the fastest response time and smallest steady-state error on both dry and slippery gravel soil surfaces. Under slippery conditions, while control performance decreases compared to dry surfaces, incorporating ground adhesion coefficient observation reduces steady-state error by 20.62%. Full article

To achieve a rapid and stable dynamic response of the drive anti-slip system for distributed electric vehicles on low-friction surfaces, this paper proposes an adaptive acceleration slip regulation control strategy based on wheel slip rate. An attachment coefficient fusion estimation algorithm based on an improved singular value decomposition unscented Kalman filter is designed. This algorithm combines Sage–Husa with the unscented Kalman filter for adaptive improvement, allowing for the quick and accurate determination of the road friction coefficient and, subsequently, the optimal slip rate. Additionally, a slip rate control strategy based on dynamic adaptive compensation sliding mode control is designed, which introduces a dynamic weight integral function into the control rate to adaptively adjust the integral effect based on errors, with its stability proven. To verify the performance of the road estimator and slip rate controller, a model is built with vehicle simulation software, and simulations are conducted. The results show that under icy and snowy road conditions, the designed estimator can reduce estimation errors and respond rapidly to sudden changes. Compared to traditional equivalent controllers, the designed controller can effectively reduce chattering, decrease overshoot, and shorten response time. Especially during road transitions, the designed controller demonstrates better dynamic performance and stability. Full article

This paper considers the estimation of SOC and SOH for lithium batteries using multi-innovation Levenberg–Marquardt and adaptive weighting unscented Kalman filter algorithms. For parameter identification, the second-order derivative of the objective function to optimize the traditional gradient descent algorithm is used. For SOC estimation, an adaptive weighting unscented Kalman filter algorithm is proposed to deal with the nonlinear update problem of the mean and covariance, which can substantially improve the estimation accuracy of the internal state of the lithium battery. Compared with fixed weights in the traditional unscented Kalman filtering algorithm, this algorithm adaptively adjusts the weights according to the state and measured values to improve the state estimation update accuracy. Finally, according to simulations, the errors of this algorithm are all lower than 1.63 %, which confirms the effectiveness of this algorithm. Full article

An accurate indoor positioning system (IPS) for the public has become an essential function with the fast development of smart city-related applications. The performance of the current IPS is limited by the complex indoor environments, the poor performance of smartphone built-in sensors, and time-varying measurement errors of different location sources. This paper introduces a hybrid indoor positioning system (H-IPS) that combines acoustic ranging, Wi-Fi fingerprinting, and low-cost sensors. This system is designed specifically for large-scale indoor environments with non-line-of-sight (NLOS) conditions. To improve the accuracy in estimating pedestrian motion trajectory, a data and model dual-driven (DMDD) model is proposed to integrate the inertial navigation system (INS) mechanization and the deep learning-based speed estimator. Additionally, a double-weighted K-nearest neighbor matching algorithm enhanced the accuracy of Wi-Fi fingerprinting and scene recognition. The detected scene results were then utilized for NLOS detection and estimation of acoustic ranging results. Finally, an adaptive unscented Kalman filter (AUKF) was developed to provide universal positioning performance, which further improved by the Wi-Fi accuracy indicator and acoustic drift estimator. The experimental results demonstrate that the presented H-IPS achieves precise positioning under NLOS scenes, with meter-level accuracy attainable within the coverage range of acoustic signals. Full article

An adaptive weighted strong tracking unscented Kalman filter is proposed in this paper for long-range relative navigation alongside non-cooperative maneuvering targets. First, an equation for obtaining the relative motion of two bodies is derived, it can be well adapted for a problem that has medium or long-distance. Secondly, a variance statistics function is introduced in the method to calculate residual weight in real time. The residual weight can be used to adjust the contribution of different measurement information to the fading factor. In this way, the sensitivity of the system to small pulse maneuvers is improved. Finally, the mean and covariance of the posterior state are calculated by the unscented transformation. A replacement equation for the fading factor is derived to improve the first-order approximation accuracy for a strong tracking system. Impulsive maneuvers with three different magnitudes are employed in a series of tests. Results from different methods showed that the proposed method could effectively detect pulse maneuvers with low latency. The proposed method is also numerically stable. Full article

Proper monitoring of quality-related but hard-to-measure effluent variables in wastewater plants is imperative. Soft sensors, such as dynamic neural network, are widely used to predict and monitor these variables and then to optimize plant operations. However, the traditional training methods of dynamic neural network may lead to poor local optima and low learning rates, resulting in inaccurate estimations of parameters and deviation of predictions. This study introduces a general Kalman-Elman method to monitor the effluent qualities, such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total nitrogen (TN). The method couples an Elman neural network with the square-root unscented Kalman filter (SR-UKF) to build a soft-sensor model. In the proposed methodology, adaptive noise estimation and weight constraining are introduced to estimate the unknown noise and constrain the parameter values. The main merits of the proposed approach include the following: First, improving the mapping accuracy of the model and overcoming the underprediction phenomena in data-driven process monitoring; second, implementing the parameter constraint and avoid large weight values; and finally, providing a new way to update the parameters online. The proposed method is verified from a dataset of the University of California database (UCI database). The obtained results show that the proposed soft-sensor model achieved better prediction performance with root mean square error (RMSE) being at least 50% better than the Elman network based on back propagation through the time algorithm (Elman-BPTT), Elman network based on momentum gradient descent algorithm (Elman-GDM), and Elman network based on Levenberg-Marquardt algorithm (Elman-LM). This method can give satisfying prediction of quality-related effluent variables with the largest correlation coefficient (R) for approximately 0.85 in output suspended solids (SS-S) and 0.95 in BOD and COD. Full article

In this paper, an innovative optimal information fusion methodology based on adaptive and robust unscented Kalman filter (UKF) for multi-sensor nonlinear stochastic systems is proposed. Based on the linear minimum variance criterion, this multi-sensor information fusion method has a two-layer architecture: at the first layer, a new adaptive UKF scheme for the time-varying noise covariance is developed and serves as a local filter to improve the adaptability together with the estimated measurement noise covariance by applying the redundant measurement noise covariance estimation, which is isolated from the state estimation; the second layer is the fusion structure to calculate the optimal matrix weights and gives the final optimal state estimations. Based on the hypothesis testing theory with the Mahalanobis distance, the new adaptive UKF scheme utilizes both the innovation and the residual sequences to adapt the process noise covariance timely. The results of the target tracking simulations indicate that the proposed method is effective under the condition of time-varying process-error and measurement noise covariance. Full article

The PM${}_{2.5}$ concentration model is the key to predict PM${}_{2.5}$ concentration. During the prediction of atmospheric PM${}_{2.5}$ concentration based on prediction model, the prediction model of PM${}_{2.5}$ concentration cannot be usually accurately described. For the PM${}_{2.5}$ concentration model in the same period, the dynamic characteristics of the model will change under the influence of many factors. Similarly, for different time periods, the corresponding models of PM${}_{2.5}$ concentration may be different, and the single model cannot play the corresponding ability to predict PM${}_{2.5}$ concentration. The single model leads to the decline of prediction accuracy. To improve the accuracy of PM${}_{2.5}$ concentration prediction in this solution, a multiple model adaptive unscented Kalman filter (MMAUKF) method is proposed in this paper. Firstly, the PM${}_{2.5}$ concentration data in three time periods of the day are taken as the research object, the nonlinear state space model frame of a support vector regression (SVR) method is established. Secondly, the frame of the SVR model in three time periods is combined with an adaptive unscented Kalman filter (AUKF) to predict PM${}_{2.5}$ concentration in the next hour, respectively. Then, the predicted value of three time periods is fused into the final predicted PM${}_{2.5}$ concentration by Bayesian weighting method. Finally, the proposed method is compared with the single support vector regression-adaptive unscented Kalman filter (SVR-AUKF), autoregressive model-Kalman (AR-Kalman), autoregressive model (AR) and back propagation neural network (BP). The prediction results show that the accuracy of PM${}_{2.5}$ concentration prediction is improved in whole time period. Full article