Dual Kalman Filter Research Articles

Islanded microgrid: hybrid energy resilience optimization

To maintain a dependable and sustainable power supply in a microgrid system, it is crucial to combine renewable energy sources with hybrid energy backup. To achieve maximum output power from solar source, a high gain DC-DC boost converter is managed using the dual Kalman filter based perturb and observe approach. A sliding mode current controller at a three phase inverter with an LC filter is intended to follow an undisturbed reference voltage. To effectively manage the power flow and optimize the utilization of available resources, a robust power management algorithm is required. The novel power management algorithm for a solo operated renewable distribution generation unit with hybrid energy backup in a microgrid is introduced. The algorithm aims to dynamically allocate power among various sources, storage systems, and loads, considering their characteristics and the overall system constraints. The algorithm utilizes sliding mode control techniques to regulate the current flow from the renewable generator and effectively manage the power allocation among different energy sources, storage systems, and loads.

Dual Kalman Filter Based on a Single Direction under Colored Measurement Noise for INS-Based Integrated Human Localization

For inertial-based integrated pedestrian navigation, the navigation environment might affect the positioning accuracy in different directions. Meanwhile, complex filtering algorithms can reduce computational efficiency. Therefore, one dual Kalman filter (KF) based on a single direction under a colored measurement noise (CMN) scheme is developed herein to improve the robustness and operational efficiency. The proposed method involves designing a data fusion model for the KF that integrates data from an inertial navigation system (INS) and ultrawideband (UWB). Subsequently, the INS/UWB integrated model-based KF under CMN (cKF) will be derived. Then, two sub-cKFs are proposed to fuse the data in the east and north directions, respectively. The empirical findings highlight the superior performance of the proposed approach over the KF for position estimation accuracy and runtime reduction, demonstrating its effectiveness.

Online meta-learning approach for sensor fault diagnosis using limited data

The accurate and timely diagnosis of sensor faults plays a critical role in ensuring the reliability and performance of structural health monitoring (SHM) systems. However, the challenge is detecting, locating, and estimating sensor faults in an online manner using limited training data. To resolve this problem, a novel approach for online sensor fault diagnosis is proposed for SHM. The proposed approach is based on meta-learning, which enables superior model generalization capabilities using limited data. The detection, localization, and estimation of typical sensor faults in an online manner can be achieved efficiently by the proposed approach. First, a one-dimensional convolutional neural network (1D CNN) is designed to detect and locate faulty sensors. The initial model parameters of the 1D CNN are optimized using a model-agnostic meta-learning training strategy. This strategy allows the acquisition of transferable prior knowledge, which can speed up the learning process on new sensor fault detection and localization tasks. The meta-learning strategy also enables efficient and accurate detection and localization of potential faulty sensors with limited data. After detecting and locating the faulty sensors, an online updating algorithm based on a dual Kalman filter is used to estimate the severity of sensor faults and structural states simultaneously. The proposed approach is demonstrated with simulated sensor faults that cover a numerical example and field measurements from the Canton Tower. The results show that the proposed approach is applicable for online sensor fault diagnosis in SHM.

Real-time prediction of bladder urine leakage using fuzzy inference system and dual Kalman filtering in cats

The use of electrical stimulation devices to manage bladder incontinence relies on the application of continuous inhibitory stimulation. However, continuous stimulation can result in tissue fatigue and increased delivered charge. Here, we employ a real-time algorithm to provide a short-time prediction of urine leakage using the high-resolution power spectrum of the bladder pressure during the presence of non-voiding contractions (NVC) in normal and overactive bladder (OAB) cats. The proposed method is threshold-free and does not require pre-training. The analysis revealed that there is a significant difference between voiding contraction (VC) and NVC pressures as well as band powers (0.5–5 Hz) during both normal and OAB conditions. Also, most of the first leakage points occurred after the maximum VC pressure, while all of them were observed subsequent to the maximum VC spectral power. Kalman-Fuzzy method predicted urine leakage on average 2.2 s and 1.6 s before its occurrence and an average of 2.0 s and 1.1 s after the contraction started with success rates of 94.2% and 100% in normal and OAB cats, respectively. This work presents a promising approach for developing a neuroprosthesis device, with on-demand stimulation to control bladder incontinence.

Electrochemical Model-Based State-Space Approach for Real-Time Parameter Estimation of Lithium-Ion Batteries

Lithium-ion batteries are widely used in various applications, including electric vehicles and renewable energy storage systems, due to their high energy density, long cycle life, and low self-discharge rate. Accurate estimation of battery state of charge (SOC) and state of health (SOH) is crucial for their safe and efficient operation.In this study, we propose a model-based state-space solver for real-time monitoring of lithium-ion batteries under various operating conditions. The state-space model simplifies a single-particle model and enables simulations considering the current state of the battery using a Kalman filter. The model includes a one-dimensional Single Particle Model with Electrolyte (SPMe) and uses eigenfunction expansion to solve the mass conservation equation for the electrolyte phase. The final form of the state-space model for the solid and electrolyte phase includes a voltage equation using lithium-ion concentration. The proposed solver was validated by comparing it with the SPMe solver, and the results agree well.To achieve on-line updating of the battery model parameters, we propose a multi-scale parameter adaptive method based on dual Kalman filters for estimating the SPMe model parameters. The fast time-varying parameters are separated from the slowly time-varying parameters, and then each group is estimated by a different Kalman filter with a different time constant. This method enables the estimation of both the SOC and all parameters, including the SPMe model parameters. Additionally, we provide a parameter adjustment method for dual extended Kalman filters when estimating multiple parameters.The proposed method reduces the influence of the initial state error on the algorithm and improves its robustness. Experimental results demonstrate that the accuracy of the algorithm is improved by separating the fast and slow parameters and estimating them with different Kalman filters. The proposed solver can be used with Kalman filters and the like in an onboard environment due to its fast calculation speed. Figure 1

Driving fatigue detection based on brain source activity and ARMA model.

Fatigue among drivers is a significant issue in society, and according to organizational reports, it substantially contributes to accidents. So accurate fatigue detection in drivers plays a crucial role in reducing the number of people fatalities or injured resulting from accidents. Several methods are proposed for fatigue driver recognition among which electroencephalography (EEG) is one. This paper proposed a method for fatigue recognition by EEG signals with extracted features from source and sensor spaces. The proposed method starts with preprocessing by applying filtering and artifact rejection. Then source localization methods are applied to EEG signals for active source extraction. A multivariate autoregressive (MVAR) model is fitted to selected sources, and a dual Kalman filter is applied to estimate the source activity and their relationships. Then multivariate autoregressive moving average (ARMA) is fitted between EEG and source activity signals. Features are extracted from model parameters, source relationship matrix, and wavelet transform of EEG and source activity signals. The novelty of this approach is the use of ARMA model between source activities (as input) and EEG signals (as output) and feature extraction from source relations. Relevant features are selected using a combination of RelifF and neighborhood component analysis (NCA) methods. Three classifiers, namely k-nearest neighbor (KNN), support vector machine (SVM), and naive Bayesian (NB) classifiers, are employed to classify drivers. To improve performance, the final label for fatigue detection is calculated by combining these classifiers using the voting method. The results demonstrate that the proposed method accurately recognizes and classifies fatigued drivers with the ensemble classifiers in comparison with other methods.

An improved multi innovation adaptive robust dual kalman filter algorithm for estimating battery state

An improved multi innovation adaptive robust dual kalman filter algorithm for estimating battery state

High‐precision state of charge estimation of lithium‐ion batteries based on improved particle swarm optimization‐backpropagation neural network‐dual extended Kalman filtering

SummaryHigh‐precision state of charge (SOC) estimation is essential for battery management systems (BMSs). In this paper, a new SOC estimation method is proposed. The dual Kalman filter algorithm and backpropagation neural network (particle swarm optimization ‐ backpropagation neural network ‐ double extended Kalman filter [PSO‐BPNN‐DEKF]) are combined to estimate and correct the SOC of lithium‐ion batteries, in which the initial weight and threshold of the BPNN are optimized by particle swarm optimization algorithm. Based on the second‐order RC equivalent circuit model, parameter identification is carried out using the adaptive forgetting factor least squares (AFFRLS) method. Online parameter updates and SOC estimation are realized by DEKF algorithm. Then, the trained PSO‐BPNN is used to predict the SOC estimation error in real time, and the SOC estimation value is corrected by adding prediction errors. The SOC estimates before and after correction under Beijing Dynamic Stress Test (BBDST), dynamic stress test (DST), and hybrid pulse power characterization (HPPC) were compared. Under BBDST, DST, and HPPC tests, the maximum errors of the corrected SOC estimates are 0.0107, 0.0090, and 0.0147, respectively. The root mean square error (RMSE) of the corrected SOC estimates decreased by 94.02%, 83.18%, and 88.03%, respectively, compared with the extended Kalman filtering (EKF). The mean absolute error (MAE) of the corrected SOC estimates remained around 0.1% for all the BBDST dynamic operating conditions at different temperatures. The experimental results demonstrate the accuracy, effectiveness, and temperature adaptability of the proposed algorithm for SOC estimation under complex conditions of lithium‐ion batteries.

A Dual Kalman Filtering Algorithm for Estimating the SOC of Lithium-Ion Batteries with LiMn0.6Fe0.4PO4/LiNi0.5Co0.2Mn0.3O2 Cathode Based on Multi-Innovation and Schmidt Orthogonal Transformation

Accurately estimating the state of charge (SOC) is imperative for ensuring safe and dependable battery utilization. However, accurately calculating SOC for LiMn0.6Fe0.4PO4/LiNi0.5Co0.2Mn0.3O2 (LMFP/NCM) batteries can be challenging due to their two flat voltage platforms and significant temperature dependence. To improve estimation accuracy, a battery SOC estimation method based on a dual Kalman filter (DKF) was proposed. The adaptive unscented Kalman filter (AUKF) process starts with the introduction of Schmidt orthogonal transform, which is subsequently employed in the algorithm’s sampling point selection procedure to mitigate computational complexity. Moreover, the utilization of the multi-innovation theory serves to enhance the accuracy of algorithmic estimation. The extended Kalman filter is used to identify the parameters of the equivalent circuit model online while simultaneously carrying out battery SOC estimation. This approach mitigates the impact of variations in battery model parameters during charging and discharging processes. Under complex conditions, the algorithm’s average error is less than 0.53%, demonstrating its effectiveness in improving SOC estimation accuracy as evidenced by comparison between experiment and simulation results. It has reference significance for optimizing LMFP/NCM battery SOC estimation.

Real-time continuous EOG-based gaze angle estimation with baseline drift compensation under stationary head conditions

Objective:This work aims to propose a novel method to estimate the gaze from electrooculography (EOG) signals while compensating for the baseline drift, and which intrinsically detects fixations, saccades and blinks. In contrast to existing baseline drift mitigation techniques, the proposed framework is real-time-implementable, and does not require the average point of gaze to lie at the primary gaze position, nor does it disrupt the overall ocular pose-induced EOG signal DC characteristics. The EOG data used to validate the proposed method is also being made publicly available. Methods:The proposed method is based on the dual Kalman filter, which estimates the gaze angles (GAs) and the baseline concurrently, taking into consideration the EOG signal’s non-stationary and temporally-multimodal characteristics. In fact, it is a multiple-model technique based on a battery model of the eye wherein fixations, saccades and blinks are modelled separately. Results:When applied to short EOG data segments, a horizontal and vertical GA estimation error of 1.64 ± 0.82°and 1.97 ± 0.34°, respectively, was obtained, which compared well with the corresponding results obtained using the state-of-the-art linear regression models. Conversely, for longer data segments, the proposed framework yielded superior GA estimation performance when compared to the state-of-the-art techniques. Eye movement detection and labelling F-scores exceeding 90% were achieved. Conclusion:The proposed method yields reliable gaze estimation performance, and accurately detects fixations, saccades and blinks. Significance:This work proposes an integrated method to simultaneously estimate the GAs and address the baseline drift issue without the limitations of existing baseline drift mitigation techniques.

Co-estimation of state of charge and internal temperature of pouch lithium battery based on multi-parameter time-varying electrothermal coupling model

Accurate estimation of battery state of charge (SOC) and internal temperature can improve battery performance and safety. In this process, the accuracy of the battery's electrical model and thermal model and the applicability and robustness of the estimation algorithm are key. In this paper, a multi-parameter time-varying electrothermal coupling model for pouch batteries is established. The model takes into account the coupling relationship between the battery SOC and temperature changes, as well as the heat exchange between multiple surfaces of the battery and the external environment. And then through the improved hybrid pulse power characterization (HPPC) test to realize the identification of model parameters, and the changing external heat transfer conditions could be identified online. At last, an adaptive strong tracking square root cubature Kalman filter (AST-SRCKF) algorithm was designed to estimate SOC and internal temperature simultaneously. The results were verified by three dynamic conditions. The conclusion is that the AST-SRCKF has high robustness and can be quickly corrected when the initial values of SOC and temperature are unknown. And the estimation accuracy is higher than extended Kalman filter (EKF) and dual Kalman filter (DEKF). Furthermore, the simulation efficiency is improved by more than 6 % compared with EKF, and by more than 11 % compared with DEKF, which is more conducive to online real vehicle applications.

Object Recognition Using Mechanical Impact, Viscoelasticity, and Surface Friction During Interaction.

Current robotic haptic object recognition relies on statistical measures derived from movement dependent interaction signals such as force, vibration or position. Mechanical properties, which can be estimated from these signals, are intrinsic object properties that may yield a more robust object representation. Therefore, this paper proposes an object recognition framework using multiple representative mechanical properties: stiffness, viscosity and friction coefficient as well as the coefficient of restitution, which has been rarely used to recognise objects. These properties are estimated in real-time using a dual Kalman filter (without tangential force measurements) and then are used for object classification and clustering. The proposed framework was tested on a robot identifying 20 objects through haptic exploration. The results demonstrate the technique's effectiveness and efficiency, and that all four mechanical properties are required for the best recognition rate of 98.18 ± 0.424%. For object clustering, the use of these mechanical properties also results in superior performance when compared to methods based on statistical parameters.

Adaptive Double Kalman Filter Method for Smoothing Wind Power in Multi-Type Energy Storage System

At present, in the situation that wind power penetration is increasing year by year, the use of a hybrid energy storage system (HESS) to smooth out wind power fluctuations becomes an effective method. However, the existing control strategy has the problem of inadequate utilization of fluctuating power. In this paper, we propose a control strategy for smoothing wind power fluctuations based on double Kalman filters with adaptive adjustment of the state of charge (SOC). Firstly, considering the wind power’s active power grid constraint, the parameters of the dual Kalman filter are adaptively adjusted according to the original output power to obtain the target grid power synchronously, and the total smoothing command of the energy storage system is determined with the goal of improving the SOC of the lithium battery. On this basis, the SOC of the supercapacitor is considered to be improved, and the adaptive low-pass filter is used for the secondary distribution of the energy storage power command to achieve fine-grained management of the output power of HESS. The final simulation results show that the obtained smoothed wind power satisfies the 1 min and 10 min fluctuation criteria, and the minimum capacity required for lithium batteries is reduced by 0.07 MW·h under the same initial conditions as in the proposed method in this paper; it can use the fluctuating power when the SOC crosses the limit, and has a regulating effect on the SOC of HESS to improve the wind power smoothing ability and realize the stable grid-connection requirement of wind power.

Evaluation of recursive Bayesian filters for modal contribution estimation in high-tech compliant mechanisms

This study evaluates three recursive Bayesian input and state estimation algorithms, as introduced in the field of Structural Health Monitoring, for estimating modal contributions for high-tech compliant mechanisms. The aim of estimating modal contributions is the use for active vibration control. High-tech compliant motion stages allow for different sensor configurations, making new and interesting performance evaluations of these filters possible. The algorithms used, namely, the Augmented Kalman Filter (AKF), Dual Kalman Filter (DKF) and Gilijns de Moor Filter (GDF) are implemented on a compliant motion stage for guidance flexure deformation estimation. Our results show the GDF performs overall best, with good estimation performance and real-world tuning capability.

Modeling and Estimation of Self-Phoretic Magnetic Janus Microrobot With Uncontrollable Inputs

This study theoretically investigates the modeling of spherical catalytic self-phoretic magnetic Janus microrobot (MJR) evolving in uniform viscous flow. A 2-D state-space representation of the MJR is developed, exhibiting a state-dependent-coefficient (SDC) form. To evaluate the consistency of the modeling formalism, a dual Kalman filter (DKF) methodology is employed with respect to experimental results when unknown parameters or uncontrollable inputs are considered. In fact, the self-phoretic thrust mechanism and the magnetodynamics of the MJR are not well-known. SDC-DKF is implemented, and we find that there is good agreement between the dynamics computed from our theoretical predictions and the experimental observations in a wide range of model parameter variations.

Input-state-parameter-noise identification and virtual sensing in dynamical systems: A Bayesian expectation-maximization (BEM) perspective

Structural identification and damage detection can be generalized as the simultaneous estimation of input forces, physical parameters, and dynamical states. Although Kalman-type filters are efficient tools to address this problem, the calibration of noise covariance matrices is cumbersome. For instance, calibration of input noise covariance matrix in augmented or dual Kalman filters is a critical task since a slight variation in its value can adversely affect estimations. The present study develops a Bayesian Expectation-Maximization (BEM) methodology for the uncertainty quantification and propagation in coupled input-state-parameter-noise identification problems. It also proposes the incorporation of input dummy observations for stabilizing low-frequency components of the latent states and mitigating potential drifts. In this respect, the covariance matrix of the dummy observations is also calibrated based on the measured data. Additionally, an explicit formulation is provided to study the theoretical observability of the Bayesian estimators, which helps characterize the minimum sensor requirements. Ultimately, the BEM is tested and verified through numerical and experimental examples, wherein sensor configurations, multiple input forces, and abrupt stiffness changes are investigated. It is confirmed that the BEM provides accurate estimations of states, input, and parameters while characterizing the degree of belief in these estimations based on the posterior uncertainties driven by applying a Bayesian perspective.

Dual Extended Kalman Filter for State of Charge Estimation of Lithium–Sulfur Batteries

Lithium-Sulfur is a promising technology for the next generation of batteries and research efforts for early-stage prototype implementation increased in recent years. For the development of a suitable Battery Management System, a state estimator is required; however, lithium-sulfur behavior presents a large non-observable region that may difficult the convergence of the state estimation algorithm leading to large errors or even instability. A dual Extended Kalman Filter is proposed to circumvent the non-observability region. This objective is achieved by combining a parameter estimation algorithm with a cell model that includes non-linear behavior such as self-discharge and cell degradation. The resulting dual Kalman filter is applied to lithium–sulfur batteries to estimate their State-of-Charge incorporating the effects of degradation, temperature, and self-discharge deviations.

A new Kalman filter approach for structural parameter tracking: Application to the monitoring of damaging structures tested on shaking-tables

In this paper, a new data assimilation framework for correcting finite element models from datasets acquired on-the-fly in low-frequency dynamics is presented. An Unscented Kalman filter algorithm is coupled with a modified Constitutive Relation Error (mCRE) observer, leading to a Modified Dual Kalman Filter algorithm (MDKF). Built as a Hermitian data-to-model distance written in the frequency domain enriched with a CRE residual accounting for model bias, the mCRE functional has shown interesting assets for model updating purposes, in particular enhanced convexity and robustness to measurement noise. The proposed data assimilation strategy integrates the latter through a metric change in the measurement update equation. It thus differs from classical nonlinear Kalman filtering for parameter estimation as the comparison between measurements and model predictions is achieved through the mCRE functional itself. Besides, the calibration of MDKF internal parameters is facilitated by a set of general guidelines that ensure the performance of the algorithm. The methodology is applied to two earthquake engineering examples. The performances of MDKF are first assessed using synthetic measurements from a plane frame subjected to random ground acceleration. Actual measurements from the SMART2013 benchmark are then assimilated in a real-time context to monitor the eigenfrequency drop of a reinforced-concrete structure submitted to a sequence of gradually damaging shaking-table tests. The nice correlation with (i) data-driven identification results, and (ii) sequential mCRE-based model updating results, illustrates the relevance of this new approach and suggests promising use of MDKF for on-the-fly adaptive control prospects and applications involving data-to-model interaction.

An Online SOC-SOTD Joint Estimation Algorithm for Pouch Li-Ion Batteries Based on Spatio-Temporal Coupling Correction Method

An SOC (state of charge)-SOTD (state of temperature distribution) joint estimation algorithm is established for a pouch lithium-ion battery. This method integrates the first-order RC model, distributed heat generation model, thermal resistance network model, and spatio-temporal coupling correction based on spatial restoration algorithm and dual Kalman filter (DKF). Unlike the traditional 1-D SOT estimation model, the proposed algorithm can accurately estimate the temperature distribution in the battery online by restoring the battery surface temperature distribution, calculating the battery internal temperature distribution, and jointly correcting the SOC and average internal temperature. Then, the proposed method is used for the SOC-SOTD estimation of a 25-Ah pouch battery at the ambient temperatures of 10, 20, and 30 °C under the worldwide light-duty test cycle and new european driving cycle driving cycles, and the estimated values are verified by experiment and 3-D simulation. According to the verification results, calculation errors of SOC are no more than 2.12%, and the maximum average calculation errors are 0.16 °C for the surface temperature and 0.24 °C for the internal temperature. However, the DKF is needed for the SOC-SOTD joint estimation because the single KF can only correct SOC or average internal temperature but cannot handle the inconsistency in temperature distribution.

Low-Cost Parameter Estimation Approach for Modular Converters and Reconfigurable Battery Systems Using Dual Kalman Filter

Modular converters or reconfigurable battery energy storage systems are a promising approach to eliminate the dependence on the weakest element in previously hard-wired battery packs and to combine heterogeneous batteries (so-called mixed-battery systems). But their need for expensive sensors and complex monitoring as well as control subsystems hinders their progress. Estimating the parameters of each module can substantially reduce the number of required sensors and/or communication components. However, the existing estimation methods for cascaded modular circuits neglect important parameters such as the internal resistance of the battery, resulting in large systematic errors and bias. This article proposes an online estimator based on a dual Kalman filter (DKF) that exploits the slow dynamics of the battery compared to the load. The DKF algorithm estimates the open-circuit voltage (OCV) and internal resistance of each module by measuring only the output voltage and current of the system. Compared with the state-of-the-art, the proposed method is simpler and cheaper with only two sensors compared to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \geq N + 2$</tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> is the number of modules). Furthermore, the proposed algorithm achieves a fast convergence through an optimal learning rate. Simulations and experimental results confirm the ability of the proposed approach, achieving <1.5% and <5% estimation error for OCV and the internal resistance, respectively.