Real-time State Estimation Research Articles

Real-Time Detection of Internal Short Circuits in Lithium-Ion Batteries using an Extend Kalman Filter

Concerns over fuel scarcity and environmental degradation largely drive the increasing popularity of electric vehicles (EVs). Lithium-ion batteries (LIBs), known for their high energy and power densities, are the favored power source for EVs. Over the past few decades, research has been concentrated on ensuring these batteries operate efficiently, safely, and reliably. A key issue impacting the safety of Li-ion batteries is thermal runaway (TR), which can lead to hazardous battery fires. Internal short circuits (ISCs) are often the primary cause of these TR incidents, making the early detection of spontaneous ISC formation a pivotal diagnostic task. This research introduces an innovative ISC detection technique for cylindrical Li-ion battery cells. This technique is based on the augmentation of the model state vector in an extended Kalman filter (EKF), combining both classical voltage measurements to surface temperature observations. This framework enables real-time estimation of the internal ISC state while maintaining computational efficiency. The proposed method is tested numerically considering a high-fidelity numerical plant cycled using charge-depleting tests that mimic a practical battery cell working cycle at various C rates and at different ambient temperatures to account for both load and environmental uncertainties. The results demonstrate the robustness and effectiveness of the method. In addition, the method has been proven to be computationally efficient, demonstrating the feasibility of its real-time implementation.

Visual-Inertial RGB-D SLAM with Encoder Integration of ORB Triangulation and Depth Measurement Uncertainties.

In recent years, the accuracy of visual SLAM (Simultaneous Localization and Mapping) technology has seen significant improvements, making it a prominent area of research. However, within the current RGB-D SLAM systems, the estimation of 3D positions of feature points primarily relies on direct measurements from RGB-D depth cameras, which inherently contain measurement errors. Moreover, the potential of triangulation-based estimation for ORB (Oriented FAST and Rotated BRIEF) feature points remains underutilized. To address the singularity of measurement data, this paper proposes the integration of the ORB features, triangulation uncertainty estimation and depth measurements uncertainty estimation, for 3D positions of feature points. This integration is achieved using a CI (Covariance Intersection) filter, referred to as the CI-TEDM (Triangulation Estimates and Depth Measurements) method. Vision-based SLAM systems face significant challenges, particularly in environments, such as long straight corridors, weakly textured scenes, or during rapid motion, where tracking failures are common. To enhance the stability of visual SLAM, this paper introduces an improved CI-TEDM method by incorporating wheel encoder data. The mathematical model of the encoder is proposed, and detailed derivations of the encoder pre-integration model and error model are provided. Building on these improvements, we propose a novel tightly coupled visual-inertial RGB-D SLAM with encoder integration of ORB triangulation and depth measurement uncertainties. Validation on open-source datasets and real-world environments demonstrates that the proposed improvements significantly enhance the robustness of real-time state estimation and localization accuracy for intelligent vehicles in challenging environments.

Reflux Power Optimization of a Dual-Active Hybrid Full-Bridge Converter Based on Active Disturbance Rejection Control

The dual-active hybrid full-bridge (H-FDAB) DC–DC converter has great potential in medium-voltage high-power photovoltaic power station applications by introducing a three-level bridge arm to increase the output voltage range. However, its mathematical model and optimum modulation schemes have not been fully explored. Under the traditional PI control, the H-FDAB DC–DC converter will produce significant reflux power, which will lead to a decrease in converter efficiency and output voltage fluctuation. On this basis, this paper proposes a reflux power optimization strategy for an H-FDAB DC-DC converter based on active disturbance rejection control (ADRC). Firstly, the structure and power characteristics of the H-FDAB DC–DC converter are analyzed, and the relationship among the reflux power, the transmission power, and the phase shift angle is derived. Secondly, to reduce the complexity of the control calculation, upon the foundation of dual phase-shifting modulation, the Karush–Kuhn–Tucker (KKT) condition is used to solve for the phase shift angle that corresponds to the minimum reflux power. Simultaneously, we develop an ADRC loop utilizing an extended state observer (ESO) for the real-time estimation of system states. We also consider the sudden changes in input voltage, load switching, and transmission power fluctuations caused by reflux power optimization strategies as system disturbances and compensate for them accordingly. Finally, the experiments conclusively validate the designed control strategy’s correctness and feasibility.

Joint detection and state estimation based on GPS spoofing attack in smart grids

Phasor Measurement Units (PMUs) are widely used in power system state estimation due to its high sampling rate and real-time estimation of power state in power systems. However, it is also vulnerable to cyberattacks (GPS spoofing attacks) since it uses unencrypted GPS civil signals for synchronization. The GPS spoofing attacks (GSAs) will intentionally manipulate the time reference of PMUs, which is equivalent to falsifying the phase angle of the PMU measurements. In this paper, we consider the secure power state estimation and attacks detection with the unknown GSAs in the power systems. Particularly, we derived the PMU-based GSAs model in the power state estimation problem, which is formulated as a mixed maximum likelihood problem. In the formulated problem, the involved unknown parameters are coupled and the objective function is non-convex, which are of significant challenges in finding optimal solutions. To tackle these challenges, a joint maximum a posteriori and maximum likelihood (JMAP-ML) algorithm is proposed to securely estimate the power state and detect the GSAs in the mixed measurements of PMUs. Different testing scenarios in IEEE 14 bus system are simulated to show the proposed algorithm’ performance on GSAs detection and state estimation. Numerical examples demonstrate the improved accuracy of our algorithm compared with classical algorithms when GSAs are present. And we conclude that when a sufficient number of PMUs are deployed in the system, the impact of GSAs will be largely compensated in the estimation stage.

A causation-based computationally efficient strategy for deploying Lagrangian drifters to improve real-time state estimation

Deploying Lagrangian drifters that facilitate the state estimation of the underlying flow field within a future time interval is practically important. However, the uncertainty in estimating the flow field prevents using standard deterministic approaches for designing strategies and applying trajectory-wise skill scores to evaluate performance. In this paper an information measurement is developed to quantitatively assess the information gain in the estimated flow field by deploying an additional set of drifters. This information measurement is derived by exploiting causal inference. It is characterized by the inferred probability density function of the flow field, which naturally considers the uncertainty. Although the information measurement is an ideal theoretical metric, using it as the direct cost makes the optimization problem computationally expensive. To this end, an effective surrogate cost function is developed. It is highly efficient to compute while capturing the essential features of the information measurement when solving the optimization problem. Based upon these properties, a practical strategy for deploying drifter observations to improve future state estimation is designed. Due to the forecast uncertainty, the approach exploits the expected value of spatial maps of the surrogate cost associated with different forecast realizations to seek the optimal solution. Numerical experiments justify the effectiveness of the surrogate cost. The proposed strategy significantly outperforms the method by randomly deploying the drifters. It is also shown that, under certain conditions, the drifters determined by the expected surrogate cost remain skillful for the state estimation of a single forecast realization of the flow field as in reality.

Efficiency and longevity trade-off analysis and real-time dynamic health state estimation of solid oxide fuel cell system

Solid oxide fuel cell (SOFC) is a promising energy conversion technology due to its advantages of high efficiency, low emissions, and fuel adaptability. Addressing the balance between efficiency and longevity is crucial for their application on a large scale. Accurate estimation and prediction of the health state of SOFC systems is essential for the study of optimization between system efficiency and degradation. In this work, the influence law of degradation, the relationship between efficiency and longevity, and their joint optimization, alongside the state of health (SOH) estimation of the SOFC system have been deeply investigated, based on a rigorously validated model of a degraded SOFC system. First, the influential variables that predominantly influence system degradation are identified and analyzed via principal component analysis, including stack average temperature, stack temperature gradient, and fuel utilization. Then, the exploration of the trade-off relationship between system efficiency and longevity under all operating conditions is conducted, and the influence laws of influential variables on system degradation are revealed. In light of the lack of accuracy during load changes and the high time cost for existing SOH estimation methodologies, a novel improved SOH estimation strategy, considering both stack voltage and current-related states, is proposed for real-time estimation of system SOH under varying fuel compositions and dynamic power scenarios. The performance of the proposed method is demonstrated by comparing with existing methodologies. This comprehensive study lays the groundwork for the effective and sustainable health management of SOFC systems, ensuring their efficiency and longevity are maintained at an optimal balance.

Cluster-based Bayesian approach for noisy and sparse data: application to flow-state estimation

This study presents a cluster-based Bayesian methodology for state estimation under realistic conditions including noisy data from sparse sensors. The proposed approach is interpretable and, building upon previous work on transition networks, explicitly accounts for experimental noise within the data-driven framework by means of data clustering. Experimental measurements are exploited, beyond model training, to quantify the degree of uncertainty (noise) for each trained state. Such noise levels are eventually associated with probability distributions that, when combined with Bayes’ theorem, allow us to perform real-time state estimation. The proposed methodology is tested on two cases of challenging flows generated by an accelerating elliptical plate and also a delta wing experiencing gusts. Results specifically indicate that the proposed approach is robust against the number of clusters, enabling state estimation with a significant order reduction, notably decreasing the computational cost while preserving estimation accuracy. Based on the present findings, the proposed data-driven approach can be employed for realistic state estimation in nonlinear systems where noise, sensor sparsity and nonlinearities represent a challenging scenario.

2DLIW-SLAM:2D LiDAR-inertial-wheel odometry with real-time loop closure

Due to budgetary constraints, indoor navigation typically employs two-dimensional (2D) LiDAR rather than 3D LiDAR. However, the utilization of 2D LiDAR in simultaneous localization and mapping (SLAM) frequently encounters challenges related to motion degeneracy, particularly in geometrically similar environments. To address this problem, this paper proposes a robust, accurate, and multi-sensor-fused 2D LiDAR SLAM system specifically designed for indoor mobile robots. To commence, the original LiDAR data undergoes meticulous processing through point and line extraction. Leveraging the distinctive characteristics of indoor environments, line–line constraints are established to complement other sensor data effectively, thereby augmenting the overall robustness and precision of the system. Concurrently, a tightly-coupled front-end is created, integrating data from the 2D LiDAR, inertial measurement unit, and wheel odometry, thus enabling real-time state estimation. Building upon this solid foundation, a novel global feature point matching-based loop closure detection algorithm is proposed. This algorithm proves highly effective in mitigating front-end accumulated errors and ultimately constructs a globally consistent map. The experimental results indicate that our system fully meets real-time requirements. When compared to cartographer, our system not only exhibits lower trajectory errors but also demonstrates stronger robustness, particularly in degeneracy problem. We open source our methods here: https://github.com/LittleDang/2DLIW-SLAM.

Real-time and non-contact estimation of state of charge for lithium-ion battery using laser ultrasonics

Lithium-ion batteries (LIBs) are widely used in portable electronics, electric vehicles, and stationary storage systems due to their high power and high energy. Monitoring the status of LIBs, particularly the state of charge (SOC), is crucial to ensuring battery performance and safety. Non-destructive ultrasonic approaches are recently emerging as a promising method for estimating the battery state and improving safety. However, current battery state estimation methods require contact and the use of a couplant, limiting their practicality and real-time applications. This study presents a non-contact laser ultrasonic system for monitoring the battery state during charge/discharge cycles. The experimental setup utilizes a line laser source to generate ultrasonic waves and an interferometer to detect the wave signal in a pitch-catch mode. Various ultrasonic features, including time-of-flight (TOF) and signal amplitude (SA), are analyzed in the time and frequency domains, respectively, at different degradation rates. The results show significant changes in the ultrasonic features in response to battery SOC and cycling degradation. The proposed non-contact ultrasonic method has the capability to determine the internal status of LIBs, enabling real-time monitoring of battery safety.

Real-time harmonic state estimation of power systems using optimal placement of measurement devices through RT-LAB implementation

Real-time harmonic state estimation of power systems using optimal placement of measurement devices through RT-LAB implementation

Real-time freeway traffic state estimation for inhomogeneous traffic flow

This paper addresses model-based approach considering online model parameters estimation to estimate the real-time freeway traffic state for inhomogeneous traffic flow. Its effectiveness is demonstrated through macroscopic simulation and the influence of detector configuration on the estimation performance is investigated. The results indicate that when a freeway is inhomogeneity, additional detectors need to be placed at the boundary of traffic flow inhomogeneity to achieve better estimation performance. Finally, it is confirmed that considering traffic flow inhomogeneity can achieve better estimation performance using real expressway data from Shanghai.

A machine learning approach for real-time cortical state estimation

Objective. Cortical function is under constant modulation by internally-driven, latent variables that regulate excitability, collectively known as ‘cortical state’. Despite a vast literature in this area, the estimation of cortical state remains relatively ad hoc, and not amenable to real-time implementation. Here, we implement robust, data-driven, and fast algorithms that address several technical challenges for online cortical state estimation. Approach. We use unsupervised Gaussian mixture models to identify discrete, emergent clusters in spontaneous local field potential signals in cortex. We then extend our approach to a temporally-informed hidden semi-Markov model (HSMM) with Gaussian observations to better model and infer cortical state transitions. Finally, we implement our HSMM cortical state inference algorithms in a real-time system, evaluating their performance in emulation experiments. Main results. Unsupervised clustering approaches reveal emergent state-like structure in spontaneous electrophysiological data that recapitulate arousal-related cortical states as indexed by behavioral indicators. HSMMs enable cortical state inferences in a real-time context by modeling the temporal dynamics of cortical state switching. Using HSMMs provides robustness to state estimates arising from noisy, sequential electrophysiological data. Significance. To our knowledge, this work represents the first implementation of a real-time software tool for continuously decoding cortical states with high temporal resolution (40 ms). The software tools that we provide can facilitate our understanding of how cortical states dynamically modulate cortical function on a moment-by-moment basis and provide a basis for state-aware brain machine interfaces across health and disease.

Bias Analysis of PMU-Based State Estimation and Its Linear Bayesian Improvement

With the rapidly changing states of modern power systems, real-time state estimation based on phasor measurement units (PMUs) plays an increasingly important role in energy management systems (EMSs). However, due to the complicated distribution of PMU measurement errors, conventional state estimation methods such as the weighted least squares (WLS) estimator suffer from the estimation bias. Consequently, they do not represent the least variance unbiased estimators. To solve the estimation bias, a state estimation method combining the PMU linear measurement model and the linear Bayesian estimation theory is proposed. Specifically, considering the prior information on estimated parameters and the complicated probability distribution of PMU measurement errors, the linear Bayesian theory is applied to PMU-based state estimation to improve the performance of state estimation. To support this method, the correlation between real and imaginary errors is analyzed, and the prior information of estimated parameters is determined by the volatility of system states. When the real states of practical power systems are unavailable, performance indicators for state estimation are proposed based on the estimation residuals. The efficacies of the proposed estimation and evaluation methods are verified using IEEE 14 bus system and a large-scale power grid in China.

Improvement of real-time state estimation performance in HVDC systems using an adaptive nonlinear observer

This paper proposes a novel adaptive nonlinear observer design for a voltage source converter based on high-voltage direct current (VSC-HVDC) transmission systems. We consider a system consisting of a power grid, and a converter station connected to an unknown load through a long HVDC cable. The primary contribution of this work is the development of a global high-gain observer that facilitates the estimation of all system states. Specifically, it encompasses the estimation of power grid parameters, such as the angular frequency and the voltage at the point of common coupling (PCC), as well as the states of the HVDC cable and the current absorbed by the load. The performance of the proposed observer is assessed through theoretical analysis and simulations. Additionally, we implemented our observer on a digital signal processor (DSP) eZdsp in a processor-in-the-loop (PIL) quasi-real-time setting. Experimental results, coupled with numerical simulations, showcase the outstanding performance of our proposed observer.

Pseudo-metric modelling of distribution network state estimation based on CNN-BiLSTM network and customized HGGA algorithm

To address the insufficient configuration of real-time measurement devices in distribution networks, a state estimation method for distribution networks is proposed. First, both historical data and real-time data are used as the input of the convolutional neural networks-bi-directional long short-term memory (CNN-BiLSTM) network to obtain the pseudo measurements of the branch power and load node injected power with high accuracy at the current moment. Second, the pseudo measurement set of the input state estimation is determined using the customized hybrid greedy genetic algorithm (HGGA) algorithm. Finally, the real-time measurement, pseudo measurement, virtual measurement, and weight set are input into the state estimation program to complete the real-time state estimation of the distribution network based on the weighted least squares method. Additionally, the proposed method is applied to the data fusion of different measurement systems. Theoretical analysis and example verification confirm that the proposed method can effectively improve the accuracy of pseudo measurement modelling and state estimation results.

Adaptive Kalman Filter for Real-Time Visual Object Tracking Based on Autocovariance Least Square Estimation

Real-time visual object tracking (VOT) may suffer from performance degradation and even divergence owing to inaccurate noise statistics typically engendered by non-stationary video sequences or alterations in the tracked object. This paper presents a novel adaptive Kalman filter (AKF) algorithm, termed AKF-ALS, based on the autocovariance least square estimation (ALS) methodology to improve the accuracy and robustness of VOT. The AKF-ALS algorithm involves object detection via an adaptive thresholding-based background subtraction technique and object tracking through real-time state estimation via the Kalman filter (KF) and noise covariance estimation using the ALS method. The proposed algorithm offers a robust and efficient solution to adapting the system model mismatches or invalid offline calibration, significantly improving the state estimation accuracy in VOT. The computation complexity of the AKF-ALS algorithm is derived and a numerical analysis is conducted to show its real-time efficiency. Experimental validations on tracking the centroid of a moving ball subjected to projectile motion, free-fall bouncing motion, and back-and-forth linear motion, reveal that the AKF-ALS algorithm outperforms a standard KF with fixed noise statistics.

Dynamic Estimation of Cardiovascular State From Arterial Blood Pressure Recordings.

Real-time estimation of patient cardiovascular states, including cardiac output and systemic vascular resistance, is necessary for personalized hemodynamic monitoring and management. Highly invasive measurements enable reliable estimation of these states but increase patient risk. Prior methods using minimally invasive measurements reduce patient risk but have produced unreliable estimates limited due to trade-offs in accuracy and time resolution. Our objective was to develop an approach to estimate cardiac output and systemic vascular resistance with both a high time resolution and high accuracy from minimally invasive measurements. Using the two-element Windkessel model, we formulated a state-space method to estimate a dynamic time constant - the product of systemic vascular resistance and compliance - from arterial blood pressure measurements. From this time constant, we derived proportional estimates of systemic vascular resistance and cardiac output. We then validated our method with a swine cardiovascular dataset. Our estimates produced using arterial blood pressure measurements not only closely align with those using highly invasive measurements, but also closely align when derived from three separate locations on the arterial tree. Moreover, our estimates predictably change in response to standard cardiovascular drugs. Overall, our approach produces reliable, real-time estimates of cardiovascular states crucial for monitoring and control of the cardiovascular system.

A Novel Approach for Real-Time Estimation of State of Charge in Li-ion Battery Through Hybrid Methodology

A Novel Approach for Real-Time Estimation of State of Charge in Li-ion Battery Through Hybrid Methodology

Real-time prediction of material removal rate for advanced process control of chemical mechanical polishing

Polishing torque holds significance in monitoring the chemical mechanical polishing (CMP) process due to its close correlation with material removal. This study introduces a new model-based technique for estimating the material removal rate (MRR) using in-process data from CMP machines. The proposed method employs either the sequential least squares method or Kalman filter for real-time state estimation. Real-time estimation of MRR enables material removal control without relying on conventional endpoint detection (EDP) technologies. The accuracy of the proposed approach is validated through oxide CMP experiments, demonstrating precise estimation of the center-slowed MRR profile towards the end of the pad life.

Vibration Research on Centrifugal Loop Dryer Machines Used in Plastic Recycling Processes

This study investigates the vibrations of centrifugal loop dryer machines used in plastic recycling processes. These machines are characterized by large uncertainties, high vibration, and unmodeled dynamics, making the design and maintenance of real-time state estimators for their operational conditions difficult. The present study includes an analysis of the centrifugal loop dryer machines’ vibration characteristics and their influence on operation results based on vibration analysis, frequency response analysis, and expert advice. Two identical loop dryers installed and operated in parallel in a single recycling line were investigated. Measurements were performed using a two-sample measurement design and based on a one-sample statistical method for estimating uncertainty in repeated measurements of data processing. Additionally, a problem connected with incorrect machine operation during high vibration, resulting in insufficient drying of loaded material, was investigated. This was defined as a situation in which some melted plastic is still too wet after mechanical drying, caused by the incorrect installation of damper elements of the holding elements. Finaly, it is recommended that a correction of the machine installation and a control measurement are carried out to determine whether the vibration in the base of the machine still exists. A simplified theoretical vibration analysis of the rotating machine was also carried out in the present paper.