State Estimation Method Research Articles

A novel suppressing Kalman filter divergence method for the state of charge estimation of lithium-ion batteries under complex conditions

The estimation method for state of charge (SOC) of lithium-ion batteries based on the Extended Kalman Filter (EKF) is prone to losing the ability to track sudden changes under complex driving conditions. Thus, this method cannot guarantee estimation accuracy and robustness. To overcome this deficiency, fading factors are introduced into the error covariance to enhance the effect of newly generated measurement data on the current filtering estimation. A Suboptimal Multiple Fading EKF (SMFEKF) SOC estimation method is proposed herein. The fading factors are solved by forcing the new error sequence of the predicted and actual terminal voltage values to remain orthogonal. The new error sequence that remains orthogonal undergoes transitions from strong autocorrelation in the original stable state to weak autocorrelation. Through real-time adjustment of the Kalman gain matrix, the effective tracking ability of the system in the event of sudden changes gets enhanced. To verify the effectiveness of the proposed method, first, a battery dynamic condition test library covering multiple driving scenarios (such as cities, suburbs, and highways) is established through ADVISOR advanced vehicle simulator driving condition tests. Second, a second-order equivalent circuit model is established and the recursive least squares method is used for parameter identification. Finally, a detailed comparison is made between SMFEKF and EKF in terms of SOC estimation accuracy and robustness under driving conditions with different levels of complexity. It is found that four operating conditions are the main reasons for filtering divergence in EKF. The results show that the root mean square errors of SMFEKF are 63.72 %, 54.20 %, and 21.30 % those of EKF under the three complex conditions, respectively. The new method effectively reduces SOC estimation error and solves the problem of EKF filtering divergence under complex conditions.

Learning‐based monocular visual‐inertial odometry with SE2(3) $S{E}_{2}(3)$‐EKF

AbstractLearning‐based visual odometry (VO) becomes popular as it achieves a remarkable performance without manually crafted image processing and burdensome calibration. Meanwhile, the inertial navigation can provide a localization solution to assist VO when the VO produces poor state estimation under challenging visual conditions. Therefore, the combination of learning‐based technique and classical state estimation method can further improve the performance of pose estimation. In this paper, we propose a learning‐based visual‐inertial odometry (VIO) algorithm, which consists of an end‐to‐end VO network and an ‐Extended Kalman Filter (EKF). The VO network mainly combines a convolutional neural network with a recurrent neural network, taking advantage of two consecutive monocular images to produce relative pose estimation with associated uncertainties. The ‐EKF, which is proved to overcome the inconsistency issues of VIO, propagates inertial measurement unit kinematics‐based states, and fuses relative measurements and uncertainties from the VO network in its update step. The extensive experimental results on the KITTI data set and the EuRoC data set demonstrate the superior performance of the proposed method compared to other related methods.

A robust state estimation method for power systems using generalized correntropy loss function

The accurate estimation of power system states is crucial for effective monitoring and control. However, the performance of conventional state estimators, which assume Gaussian measurement noise and do not account for denial-of-service attacks, can deteriorate significantly in real power systems. To address these issues, this paper proposes a novel robust state estimation method based on the quadratic function (QF) and the generalized correntropy loss function (GCL). The proposed QF-GCL state estimation method can effectively deal with non-Gaussian measurement noise and denial-of-service attacks. To enhance the computational efficiency, an influence function based solving method is developed. To determine the optimal parameters for the proposed QF-GCL state estimation method, a new state estimation error covariance equation is further derived. Simulations are performed on the IEEE 30-bus, 118-bus and 300-bus systems, to demonstrate the accurate and robust performance of the proposed QF-GCL robust state estimation method.

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.

Intention inference‐based interacting multiple model estimator in photoelectric tracking

AbstractAiming to improve the estimation and prediction accuracy of a target's position, this paper proposes a state estimation method for photoelectric tracking systems, based on the evaluation of the tracked target's motion intention. Traditional photoelectric tracking systems utilize external physical quantities such as the position, velocity, and acceleration of the target as the estimated states. While this method can output good results for pre‐modelled target positions, it struggles to maintain the accuracy when facing manoeuvering targets or complex motion patterns targets. Here, the relevant parameters of the tracked target's motion intention are directly estimated innovatively, like estimating the circling point position rather than the circular flying target's position and velocity. This approach enables recognizing the target's motion intention and leads to precise estimation, which specifically consists of an interacting multiple model approach, multiple unscented Kalman estimators, and a robust estimator. The effectiveness and stability of this estimator are validated through software simulations and experiments on a dual‐reflection mirror platform.

A state estimation method based on CNN-LSTM for ball screw

Ball screw is widely used in the engineering field, and accurate estimation of their state is crucial for the reliability of system operation. However, existing methods often overlook the time series characteristics and spatial correlation of vibration signals, unable to provide complete degradation information and divide the degradation process, resulting in limited prediction accuracy. Therefore, a state estimation method for ball screw based on Convolutional Neural Networks (CNN) and Long Short-Term Memory Neural Networks (LSTM) is proposed. An experiment of ball screw transmission equipment was conducted to collect vibration signals throughout the entire life cycle and verify the proposed method. Firstly, the frequency domain amplitude signal of the transformed ball screw is normalized to eliminate scale differences, which serves as the input for CNN feature extraction. Then, these deep features are input into the LSTM network to capture the fault evolution patterns that reveal the degradation of ball screw performance, and achieve accurate estimation of ball screw state. The final prediction accuracy was 97.87%, verifying the effectiveness of the proposed method.

A Network-Group Target State and Network Topology Estimation Method Based on Signals Containing Delay, Doppler and Address

Network-group targets are a set of objectives that adhere to a shared communication protocol, perform common tasks, and exhibit relatively coordinated movements. Typically, network-group targets emit radar and communication signals. However, they often employ a silent mode to evade our perception. Despite this, they continue to exchange data through their communication networks. By intercepting the communication signals of these targets, this paper proposes a method for estimating the state and network topology of network-group targets based on random finite set (RFS) theory. This method is based on the modeling of network-group targets using a labeled multi-Bernoulli (LMB) RFS. In state estimation, the usual method involves extracting the localization parameters from the signals in the first step and use these parameters for target tracking in the second step. This study focused on estimating the kinematic states of network-group targets using communication signals containing delay and Doppler information received by multiple mobile sensor platforms. The proposed method considers the coherency between frequency-hopping pulses in the signals, resulting in an improved estimation performance. In addition, considering that network-group targets require network access for information exchange, graph theory concepts were utilized to estimate the network topology of network-group targets by address measurement. The simulation results validated the effectiveness of the proposed method and demonstrated its superior performance.

Overview of State Estimation Methods and Research Progress of Power Lithium Batteries for Electric Vehicles

This paper presents a comprehensive research framework for state estimation of power lithium batteries by summarizing both model-based and non-model-based approaches. It provides an in-depth analysis of various methods, supported by experimental results. In the first part, the paper surveys model-based state estimation techniques, including the ECM (Equivalent Circuit Model), electrochemical models, and data-driven models. It systematically explains the principles behind these models and their respective applications. Additionally, the study delves into model-independent state estimation, emphasizing the utilization of machine learning algorithms in battery state estimation and the advancements in artificial intelligence technology. The potential of hybrid methods and intelligent algorithms in state estimation is also explored, highlighting possible future directions. The findings indicate that while model-based methods can attain high estimation accuracy in specific scenarios, they are constrained by model complexity and parameter uncertainty. The application of fusion method and intelligent algorithm further improves the performance of state estimation and provides strong support for real-time monitoring and prediction of power lithium batteries.

State of Charge and State of Health Coestimation for Lithium-Ion Capacitor Based on Multi-innovation Filters

The lithium-ion capacitor (LIC) is a new type of hybrid energy storage device, which combines the advantages of lithium-ion battery and electric double layer capacitor. To achieve efficient and reliable application of LIC in practical scenarios, accurate model and state estimation method are needed. In this work, the electrical behavior of LIC is studied, which is then described by the Thevenin model. A multi-innovation filter- (MIF-) based coestimation method is proposed, in which the multi-innovation linear Kalman filter (MI-LKF) is used for model parameter identification, the multi-innovation cubature Kalman filter (MI-CKF) is used for state of charge estimation, and the multi-innovation extended Kalman filter (MI-EKF) is used for state of health estimation. Compared to traditional methods, this method can significantly improve estimation accuracy by only expanding the innovation used to update the state from a single moment to multiple moments. The experimental results indicate that the estimation errors of SOC and SOH can be constrained within ±0.5%. In addition, the proposed method has good robustness and can achieve high-precision state estimation even in the face of noise interference, uncertain initial values of the algorithm, and uncertain starting operating points.

Smart distribution network voltage estimation using PMU technology considering zero injection constraints.

To properly control the network of the power system and ensure its protection, Phasor measurement units (PMUs) must be used to monitor the network's operation. PMUs can provide synchronized real-time measurements. These measurements can be used for state estimation, fault detection and diagnosis, and other grid control applications. Conventional state estimation methods use weighting factors to balance the different types of measurements, and zero injection measurements can lead to large weighting factors that can introduce computational errors. The offered methods are designed to ensure that these zero injection criteria can be strictly satisfied while calculating the voltage profile and observability of the various distribution networks without sacrificing computing efficiency. The proposed method's viability is assessed using standard IEEE distribution networks. MATLAB coding is used to simulate the case analyses. Overall, the study provides a valuable contribution to the field of power distribution system monitoring and control by simplifying the process of determining the optimal locations for PMUs in a distribution network and assessing the impact of ZI buses on the voltage profile of the system.

State Estimation of Lithium-ion Battery for Shipboard Applications: Key Challenges and Future Trends

With the aggravation of environmental problems caused by the long-term dependence of shipping traffic on heavy fossil fuels, it is an irreversible development trend for electrified ships to integrate large-capacity battery energy storage systems (ESSs). As the main component, the shipboard lithium-ion battery (LIB) plays an important role in the operation of ship power system to balance the source and load sides. By analyzing the effects of temperature, vibration, humidity and salt spray on battery characteristics in the shipping environment, this paper points out that the characteristics of shipboard LIB have certain differences on the state changes with the land-based batteries. Then, this paper systematically reviews the most commonly used LIB modeling and state estimation methods and their applicability to the shipping environment, including the empirical models, electrochemical models, equivalent circuit models (ECMs) and data-driven models. On this basis, the state estimation methods of state of charge (SOC), state of power (SOP), state of health (SOH), state of energy (SOE) and state of temperature (SOT) are reviewed. Finally, the challenges and prospects of shipboard LIB research are prospected, in the hope of providing inspiration for the development and design of efficient and safe electric ships.

Estimation Method for Nonuniform State of MOIP in Traction Transformer via Curve Reconstruction of Frequency Domain Spectrum

Estimation Method for Nonuniform State of MOIP in Traction Transformer via Curve Reconstruction of Frequency Domain Spectrum

Physics-Informed Graphical Learning and Bayesian Averaging for Robust Distribution State Estimation

This paper proposes a robust topology change-aware distribution system state estimation (DSSE) method based on a physics-informed graph neural network and Bayesian Probability Weighted Averaging (BPWA). A general state estimator is first built utilizing a graph attention network to learn the nonlinear mapping functions under different distribution network topologies. During this stage, the topology information is embedded in the neural network and the attention mechanism is employed to capture collaborative signals and discriminate the importance of neighboring buses. Then, the BPWA method allows assigning proper weights for the state estimation results under different topologies, which finally yields a single consensus solution via the sparse training samples under the new topology. The physics-informed mechanism enables the proposed method to embed the topology knowledge in the neural network while fully exploiting the value of historical data. Robustness to anomalous measurements is achieved through the embedding of physics knowledge. The application of the BPWA method further allows the proposed method to achieve faster adaptation to topology change and quantification of the estimation uncertainties by measurement errors. MATLAB and Python are used to carry out the comparative tests to evaluate the performance of the proposed method.

Estimation of geomagnetic components under unknown interferences for drilling tools

The geomagnetic components measurement directly influences the azimuth and magnetic Toolface of downhole drilling tools. Accurate estimation of the geomagnetic components is crucial for wellbore trajectory control. However, the unknown magnetic interferences caused by ferromagnetic drilling tools introduce significant errors in the estimation of the geomagnetic components. Firstly, this paper considers the constraint of a constant total magnetic field value and introduces the unknown axial interferences of the drilling tools as a new state. At the same time, parameter uncertainties and radial interferences within the drilling tools are regarded as unknown but bounded, leading to the establishment of a linear time-varying system model for the measurement system. Then, a state estimation method based on the Zonotopic Kalman filter (ZKF) is proposed for geomagnetic components and attitude measurements. The simulation results indicate that, compared to the ZKF method without considering nonlinear constraints, the method proposed in this paper effectively separates the axial interference of the geomagnetic components from the drilling tools during dynamic measurements. The root mean square errors of the estimated three-axis geomagnetic components are reduced to 0.0249 Gauss, 0.0082 Gauss, and 0.0148 Gauss, resulting in improvements in accuracy of 96.06%, 28.6%, and 14.1%, respectively. Furthermore, the root mean square errors of the gravity Toolface, magnetic Toolface, and azimuth estimation decreased to 1.8296°, 1.8294°, and 3.2898°, leading to enhancements in accuracy of 9.61%, 9.71%, and 96.37%, respectively. Finally, experimental validation confirms the effectiveness of the proposed method, providing a reference basis for dynamic measurement while drilling.

A variance-constrained method to encoding–decoding [formula omitted] state estimation for memristive neural networks with energy harvesting sensor

This paper considers the encoding–decoding-based H∞ state estimation issue under variance constraint for delayed memristive neural networks with energy harvesting sensor. In order to improve the transmission security, the encoding–decoding communication mechanism is introduced in the transmission channel, which can encode the transmitted data with limited bits. In addition, the energy level of the energy harvester is described by a random variable that obeys the specific probability distribution, in which the energy harvesting technology is used to provide the required energy and continuously maintain the operation of the sensor. The main objective is to present new encoding–decoding-based H∞ state estimation method such that, in the presence of time-delay and energy harvesting mechanism, the desirable H∞ performance requirement and the estimation error variance constraints are both ensured by providing some sufficient conditions. In the end, the feasibility of new encoding–decoding-based H∞ state estimation algorithm is demonstrated by a simulation experiment.

State Estimation of Drive-by-Wire Chassis Vehicle Based on Dual Unscented Particle Filter Algorithm

Accurate vehicle dynamic information plays an important role in vehicle driving safety. However, due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles, the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation. This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm, which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle. In the dual unscented particle filter algorithm, two unscented particle filter transfer information to each other, observe the vehicle state information and the tire force parameter information of the four wheels respectively, which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving. The performance with the dual unscented particle filter algorithm, which is analyzed in terms of the time-average square error, is superior of the unscented Kalman filter algorithm. The effectiveness of the algorithm is further verified by driving simulator test. In this paper, a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.

A distributed robust state estimation method based on alternating direction method of multipliers for integrated electricity‐heat system

AbstractIntegrated electricity‐heat system (IEHS) has been paid more and more attention in recent years for its advantage in improving energy efficiency, reducing carbon emissions and increasing renewable energy penetration. To ensure the safety, reliability and economic operation of IEHS, several centralised state estimation (SE) methods for IEHS have been proposed. However, power systems and heat systems often belong to different management entities, and there are industrial barriers such as information privacy, operational differences, and target differences between them, which leads to less applicability of centralised SE methods. In addition, the robustness of existing distributed SE methods for IEHS is not satisfactory. To this end, a distributed robust state estimation (DRSE) model for IEHS based on the alternating direction method of multipliers (ADMM) is proposed. Firstly, by introducing auxiliary state variables and measurements, a robust linear SE model based on weighted least absolute values (WLAV) is proposed. Then, second‐order cone constraints composed of auxiliary state variables are added to the SE model, leading a SOCP‐based robust SE model. Finally, the ADMM algorithm is used to solve the proposed SOCP‐based robust SE model. Simulations demonstrate that the proposed method has higher estimation accuracy in both general and strongly correlated adverse data tests and also can ensure data privacy, good robustness and high estimation accuracy. This indicates that the method proposed has good robustness and solves the problem of weak robustness of existing distributed static state estimation methods.

Simultaneous input and state estimation with multi-step delay for linear stochastic systems based on infinity filtering and smoothing

This paper presents a novel method for simultaneous unknown input and state estimation (UISE) with multi-step delay for linear stochastic systems with rank-deficient direct feed-through matrix. The augmented Kalman filter and smoothing are established with an input model, where the input signal follows a specific probability distribution with finite covariance. Then, in light of the established properties of matrix limiting, the infinity filtering is deduced as the covariance matrix of the input signal tends to infinity. Moreover, the infinity smoothing algorithm is also derived to obtain the multi-step delayed unknown input and state estimation. Furthermore, the infinity filtering is strictly proved to coincide with the existing five-step recursive filter (FSRF). It is shown that some previous UISE algorithms, such as the unbiased minimum-variance (UMV) filter and recursive three-step filter (RTSF), are special cases of the proposed infinity filtering algorithm. This work reveals that the existing UMV, RTSF, and FSRF algorithms are essentially the particular limit of augmented Kalman filter and smoothing, which gives a new insight into the area of UISE.

Tight interval state estimate for discrete‐time descriptor linear systems

SummaryIn this work, two state estimation methods are proposed for a class of discrete‐time descriptor linear systems subject to bounded uncertainties. First, we propose set‐valued estimator algorithm using symmetric boxes to compute rigorous bounds of the system states. The observer gains are calculated using norm to attenuate the effects of the uncertainties and to improve the accuracy of the proposed estimator. Second, to obtain tighter state enclosures, zonotopic set computation are developed instead of interval analysis to design a new set‐valued state estimation algorithm. The performances of the proposed state estimation approaches are highlighted on different illustrative examples.

Distribution State Estimation and Its Impact of Load Modeling

To maintain system security and other essential parameters like reliability and quality, continuous monitoring of the system is very important. Considering the distribution network, state estimation (SE) methods can be adopted. The purpose of the method is to identify and estimate unknown variables based on the online measurements of test data. The primary objectives considered in this paper are: To choose the exact SE method and the artificial neural network (back propagation algorithm), which can be used for determination in the islanding mode of distribution network states, the composite load model is considered for the estimation of states and further enhancement. By adopting the system, state variables in terms of error are measured in the 12-bus distribution network with precise measurements and compared with practical values. The SE proposed includes results with the load flow backward-forward sweep method to satisfy the system state variables. Numerical results indicate that the model performs better for error measurement data with states and in the case of state forecasting.