Data-driven online SOH estimation of a grid-connected BESS: Accuracy improvements and lifetime prediction

A Machine learning method based on extended Kalman filter to predict critical heat flux (CHF) Occurring in narrow channels between fuel plates

This study developed an exploratory, model-constrained state-space model to characterize how real-time visual biofeedback (VB) and late-stance, phase-specific belt modulation (propulsion-facilitating, PF) were associated with improvements in affected-limb propulsion following stroke. Twelve individuals with chronic stroke participated in a single-session experiment conducted on the Adaptive Propulsion Enhancement eXperience (APEX) system, which utilizes an instrumented split-belt treadmill. The system continuously displays the affected-limb anterior ground reaction force (AGRF) while modulating belt speed during late stance. The experimental session consisted of three sequential phases: baseline, combined VB and PF training, and post-training assessment. Step-to-step AGRFs were modeled as the sum of an error-based learning (EBL) state with separate VB-related and PF-related model input channels, a use-dependent learning (UDL) state, and a direct same-step PF feed-through component. Model parameters and latent states were estimated using maximum likelihood with an extended Kalman filter and Rauch-Tung-Striebel smoother. The model reconstructed propulsion trajectories with high in-sample fidelity within the fitted session (R² = 0.976 ± 0.004), whereas temporal hold-out and post-training prediction analyses indicated limited extrapolative performance. Therefore, the decomposition should be interpreted as a model-constrained, within-session descriptive analysis rather than as evidence of robust predictive generalizability. In this model-constrained decomposition, propulsion gains were approximately distributed across EBL-related processes (~56%), UDL-related processes (~43%), and direct same-step PF feed-through (~1%). Within EBL, the VB-related and PF-related model channels contributed comparably (25% and 31%), and both adaptive states were consistent with very slow decay within the imposed identifiable range, rather than providing precise estimates of long-duration retention. These results suggest that, within the model-constrained decomposition and despite limited extrapolative performance on unseen data, propulsion enhancement in the fitted session was more closely aligned with adaptive components than with direct same-step PF feed-through. This exploratory, proof-of-concept framework provides interpretable patient-level parameters as hypothesis-generating descriptors of within-session propulsion learning and may serve as a foundation for future mechanism-informed, personalized post-stroke gait rehabilitation strategies that require prospective validation.

Abstract Simultaneous Localization and Mapping (SLAM) is essential for autonomous navigation and positioning of mobile robots. However, most existing LiDAR-based SLAM methods rely on specific environmental assumptions and observation models, which significantly degrade robustness under different degenerate scenarios. To address this issue, this research presents a multi-module degeneracy-resistant SLAM framework (MDR-SLAM) designed to enhance robustness in diverse degenerate scenarios. In the front-end, a clustering and segmentation model based on the variation of point cloud density is designed to filter dynamic interference and invalid point clouds. Additionally, a B-spline-based motion error correction method is proposed to correct IMU errors by fitting two continuous time trajectories within a sliding window. In the degeneracy-handling stage, a degeneracy detection and decoupling method based on features is proposed. By evaluating the feature distribution and the constraint ability of the LiDAR system, the degenerate state is identified and further decoupled into rotational and translational degeneracy factors, The motion state of IMU is then used to compensate for the degenerate motion state of the LiDAR in a targeted manner. In the back-end, degeneracy factors are introduced into the iterative extended Kalman filter, working with the observation model to suppress abnormal observation data. Finally, experiments in diverse degenerate scenarios, such as feature scarcity, dynamic interference, structural repetition, and platform instability, show the proposed method achieves the smallest pose errors (APE and RPE) and the highest-quality mapping results in most scenarios involving different types of degeneracy, validating its adaptability to diverse degenerate scenarios.

To improve the anti-lock braking system (ABS) performance, the control algorithm is the core, and the slip ratio and optimal slip ratio should be determined firstly. However, tyre wear may cause a deviation in the slip ratio calculation, while road conditions affect the optimal slip ratio. To enhance the ABS control performance, while considering tyre wear, a Harris hawk optimisation (HHO) sliding mode control method is proposed in this paper. First, the effective tyre radius is estimated using an adaptive extended Kalman filter (AEKF) and slip ratio correction to reflect variations in tyre wear, and the actual slip ratio is calculated using the estimated tyre radius. Then, the road adhesion coefficient is estimated with AEKF method, and the optimal slip ratio is determined by establishing its mapping relationship with the road adhesion coefficient. Finally, a sliding mode controller is designed with the HHO algorithm based on the obtained effective tyre radius and optimal slip ratio, and the proposed control strategy is verified on a dSPACE hardware-in-the-loop simulation platform under various tyre wear and road conditions. The results show that the proposed control strategy significantly shortens the braking distance. The maximum shortening ratio reaches 7.82%.

To address the critical technical challenge of positioning failure in unmanned mining trucks induced by global navigation satellite system (GNSS) signal attenuation in complex open-pit coal mine environments, particularly the significant trajectory oscillation during cornering in IMU-LiDAR integrated navigation under the simulated GNSS outage condition (with a focus on the challenging scenario of GNSS outage), this study proposes a collaborative positioning method integrating inertial measurement unit (IMU) and LiDAR based on the extended Kalman filter (EKF). A nonlinear system fusion positioning model was established, where IMU high-frequency motion estimation serves as the prediction equation and LiDAR point cloud matching (adopting the normal distribution transformation, NDT, algorithm) acts as the observation equation. The EKF algorithm performs optimal estimation on both the position estimation results of IMU and the pose information of LiDAR obtained via NDT, effectively suppressing the cumulative errors of IMU and the matching jitter of LiDAR under feature degradation scenarios. To verify the algorithm's performance comprehensively, a standardized experimental vehicle platform was constructed, and tests were conducted under multi-route, multi-speed, and repeated trial conditions. To simulate a representative and controllable weak-GNSS condition, the GNSS signal was programmatically blocked to emulate a complete outage scenario. Results indicate that while individual positioning algorithms exhibit significant tracking jitter in curved sections, the proposed IMU-LiDAR fusion trajectory closely matches the preset path. Compared to standalone IMU/LiDAR algorithms, the fusion method reduces average offset by up to 24.51% and standard deviation by up to 34.15%; when compared with mainstream open-source integrated navigation algorithms such as FAST-LIO2 and LIO-SAM, it also demonstrates superior positioning accuracy and trajectory stability. These research findings provide reliable technical support for intelligent construction in open-pit coal mines.

Abstract This paper investigates temperature-dependent optical fiber signals in a Mach-Zehnder interferometer (MZI) by establishing a thermo-optic measurement model and analyzing the temporal relationship between fiber temperature and photoelectric voltage under thermal excitation. We employ the Extended Kalman Filter (EKF) to approximate the nonlinear state and measurement equations, achieving precise tracking of photoelectric signals and accurate fiber temperature estimation. This work provides an effective solution for improving the reliability of fiber-optic sensing and transmission systems.

Metapopulation models, which consider epidemic spread across interconnected regions, can provide more accurate epidemic predictions compared to isolated models for the corresponding regions. Still, their added complexity and data requirements raise questions about their tangible benefits over simpler, localized models. We develop and validate two networked compartmental metapopulation models for predicting influenza-like illness across Europe: a detailed network-based model, including international travel dynamics, and a simpler mean-field model, aggregating average regional data. The network is constructed using public mobility data and complemented with population densities at border regions. Incidence data of influenza-like illnesses from 28 countries are integrated using an Extended Kalman filter. We show that networked epidemic models effectively capture epidemic dynamics across regions and epidemic phases. The models enable accurate forecasts, missing data imputation, and actionable insights: network models outperform isolated models in forecasting epidemic progression, particularly during critical periods such as wave onsets and peaks, and maintain reliability in scenarios with missing data. The findings unveil and quantify the advantages of metapopulation models for epidemic forecasting in interconnected regions, and pave the way to the integration of mobility and epidemic surveillance to improve the monitoring and prediction of spreading diseases.

Contribution to the vibration-based prognosis of the remaining useful life of thrust ball bearings using a phenomenological power law degradation approach and an extended Kalman filter

Accurate online parameter identification and state-of-charge (SOC) estimation are essential for lithium-ion battery management systems. However, under constant or quasi-constant current operating conditions, the system excitation is inherently weak, leading to poor parameter identifiability when conventional model-based estimation methods are used. This issue is particularly critical in grid-connected battery energy storage systems, where current dynamics are limited. To address this problem, this paper proposes an online measured impedance-assisted SOC estimation framework that integrates online electrochemical impedance measurements with a fractional-order battery model and an extended Kalman filter. Online impedance data are utilized to update the model parameters in real time through a geometric-based fitting algorithm, thereby enhancing model adaptability under low excitation conditions. Experimental results obtained from lithium-ion cells with different aging states demonstrate that the proposed method enables stable and accurate online parameter identification and SOC estimation under the tested low-excitation conditions, where conventional time-domain approaches tend to degrade or diverge. Robustness under highly dynamic operating conditions remains to be further validated.

Lithium-ion battery parameter identification and state of charge estimation based on multi-strategy dung beetle optimization algorithm and forgetting extended Kalman filter

Beyond SPC: a nonlinear state-space model for explainable prediction of linac output drift.

High-precision array element localization (AEL) is essential for bottom-mounted horizontal line arrays, as deployment uncertainties can degrade array-processing performance. In deep-ocean environments, long-range acoustic propagation and sound-speed profile variability introduce non-negligible travel-time errors in the linear propagation model (LPM), limiting localization accuracy. This study formulates the AEL problem as a simultaneous localization and mapping task within a Bayesian framework, incorporating a ray propagation model (RPM) to exploit both direct and surface-reflected acoustic paths while accounting for sound-speed profile variability. An extended Kalman filter is used to jointly estimate the array geometry, source trajectory, emission time, and sound-speed bias, while incorporating prior information and uncertainty. Simulation and experimental results show that, compared with the LPM-based approach, the RPM-based method achieves comparable horizontal localization accuracy while significantly improving vertical localization performance and source-timing estimation. Sensitivity and consistency analyses indicate robustness to sound-speed profile uncertainty. In bearing estimation, array calibration reduces the root mean square error from 4.36° to 0.29° and improved the beamforming gain by 2.1 dB, confirming its effectiveness in practical applications.

A Hybrid Phase compensation method for marine buoy motion combining dynamic mode decomposition and extended kalman filter

Accurate real-time estimation of the instantaneous vehicle state plays a crucial role in modern automotive research, both in the state diagnostics and anomaly detection and in the design and development of advanced control systems and onboard monitoring strategies. In particular, accurate knowledge of chassis motion and wheel dynamics in response to road disturbances is essential for advanced control strategies aimed at simultaneously enhancing ride quality and handling. However, the road profile represents an unmeasured and highly variable input, often requiring complex and costly sensors such as LiDAR for direct observation: this motivates the development of virtual sensing approaches capable of inferring road irregularities from standard onboard sensors. This work presents a novel state observer based on an Extended Kalman Filter (EKF) architecture for the online estimation of road-induced excitations and key vehicle dynamic quantities, including chassis out-of-plane motions, suspension displacements, and tyre-loaded radii. The observer relies on a computationally efficient 7-degree-of-freedom vehicle model, analytically derived through a streamlined multibody formulation, and validated against a high-fidelity multibody reference model under two sensor configurations, both limited to signals typically available in mass-produced vehicles. The results achieved, even when using high-noise measurements, are encouraging for further applications in real-world virtual sensing scenarios.

ABSTRACT The transition toward Industry 5.0 necessitates safe human‐machine interaction (HMI) in unstructured open spaces, where Internet of Things (IoT) infrastructures serve as the primary backbone for real‐time spatial perception. However, current HMI systems frequently decouple network‐layer operations from physical‐layer control, rendering robotic actuation highly vulnerable to IoT network degradation, including communication latency and stochastic packet loss. This paper proposes a Resilient Collaborative Control Framework (RCCF) that tightly couples network quality‐of‐service (QoS) metrics with physical actuation strategies. The methodology integrates an edge‐deployed adaptive Extended Kalman Filter (EKF) for multi‐sensor fusion under compromised IoT channels, alongside a network‐aware dynamic impedance controller that modulates robotic stiffness and damping in real time based on measured packet drop rates. Experimental evaluations on the publicly available SiT (Spatial Interaction Trajectories) dataset demonstrate that, under a simulated 15% packet loss scenario, the proposed RCCF achieves a tracking root mean square error (RMSE) of 12.4 ± 1.2 mm, an end‐to‐end latency of 31.8 ± 2.5 ms, and a collision avoidance rate of 98.5% ± 0.8%, yielding statistically significant improvements over static baseline controllers (one‐way ANOVA, p < 0.05). The framework effectively mitigates physical safety risks induced by communication degradation, providing a robust cyber‐physical control architecture for dynamic HMI in complex IoT environments.

Abstract As the core power source of electric vehicles and energy storage systems, the state of charge (SOC) of lithium-ion batteries directly influences battery lifespan, energy utilization efficiency, driving range and vehicle stability. To achieve accurate SOC estimation, a new model that combines the Grey Wolf Optimizer (GWO) algorithm and the Extended Kalman Filter (EKF) is proposed. First, a second-order RC equivalent circuit model is established, and the forgetting factor recursive least squares (FFRLS) method is adopted for online parameter identification. Second, leveraging the advantages of the GWO algorithm, such as few parameters and strong global search capability, the process noise covariance matrix Q and measurement noise covariance matrix R of the EKF are automatically optimized to determine the optimal noise parameter combination. Finally, comparative experiments with the traditional EKF are conducted under three dynamic driving cycles (DST, FUDS, and US06) to validate the performance of the GWO-EKF algorithm. The results show that the mean absolute errors (MAE) of SOC estimation by GWO-EKF algorithm are 1.73%, 1.51%, and 1.26%, respectively. The maximum error (ME) is significantly reduced, and the error curve exhibits smoother fluctuations. This verifies the effectiveness of the proposed method in improving estimation accuracy and robustness, providing strong technical support for the future development of battery management systems (BMS).

In networked sensing systems, nonlinear state monitoring and soft sensing are widely used to reconstruct key variables that cannot be directly measured in real time. For such nonlinear estimation tasks, the Extended Kalman Filter (EKF) is a commonly used recursive method. However, the conventional EKF neglects higher-order truncation terms during first-order Taylor linearization. As the nonlinearity increases, these neglected terms may accumulate and degrade filtering accuracy, and even lead to divergence in some cases. In addition, the statistical influence of the remainder terms and the correlation between prediction and measurement errors are usually ignored. To address these issues, this paper proposes an Extended Kalman Filter with remainder terms considering correlations (REKF). The proposed method replaces the higher-order terms in the Taylor expansion with remainder terms and identifies them incrementally by using least squares, thereby improving the EKF update process. A higher-order filtering framework is then constructed to jointly estimate the system state and the remainder-related random variables while accounting for the induced error correlation. Numerical simulations on typical nonlinear models demonstrate that the proposed REKF achieves better estimation performance than the conventional EKF. In this work, the proposed REKF is mainly developed for nonlinear estimation problems in which the dominant challenge arises from strong nonlinearity in the state evolution, while the measurement update is treated in a locally linearized EKF form. The results show that incorporating higher-order remainder information can effectively improve nonlinear state estimation for state monitoring and soft sensing tasks.

This study aimed to estimate vertical ground reaction force (vGRF) and lower-limb joint moments during football cutting movements using a trunk-mounted inertial measurement unit (IMU) combined with a Random Forest model, and to validate the feasibility of this approach. IMU data collected during 45° cutting tasks were corrected using an Extended Kalman Filter (EKF). The model demonstrated good and consistent performance for vGRF (coefficient of determination, R2 = 0.766; correlation coefficient, r = 0.796) and sagittal plane moments of the ankle and knee (R2 = 0.661–0.689, r = 0.807–0.842). While Bland–Altman analysis indicated low bias and generally good agreement, precision at the individual-trial level and accuracy for non-sagittal plane moments somewhat reflected the inherent within-player trial-to-trial variability in movement execution, particularly in non-sagittal loading patterns. It should be noted that performance estimates under the current trial-based validation design may differ from those obtained using a subject-independent framework such as leave-one-subject-out cross-validation. This study demonstrates that a single trunk-mounted IMU can reliably estimate key lower-limb loading patterns, providing a practical foundation for wearable-based kinetic monitoring in applied football settings.

Abstract It is difficult for existing methods to solve the real-time accuracy problem of battery module-level state of charge (SoC) and the impact of single battery inconsistency at the same time under dynamic operating conditions. The integration of data-driven technology and traditional algorithms is insufficient, resulting in limited error compensation effect. In view of the accuracy of the SoC estimation of electric vehicle (EV) battery packs under dynamic driving conditions, this paper proposes a hybrid SoC estimation method for battery management system (BMS) based on cloud master-slave architecture. The hybrid framework combines direct measurement methods (Coulomb counting method, open circuit voltage method), state estimation algorithms (extended Kalman filtering, traceless Kalman filtering), and data-driven technologies (neural networks, NARMA L-2 models), and verifies its effectiveness through hardware-in-the-loop experiments. The research results show that under dynamic operating conditions, the hybrid Coulomb counting+neural network (CC+NN) method has the fastest error convergence speed and is better than other methods. In addition, the proposed cloud master-slave BMS architecture significantly improves system reliability through real-time cross-verification of the SoC data of the advanced algorithms of the on-board BMS (slave device) and the master device. The experiment is based on the FTP-75 driving cycle and verifies the high efficiency of this method in practical applications.The final analysis shows that the CC+NN combination exhibits optimal error suppression ability in complex scenarios, and provides a high-precision solution for electric vehicle battery management.