Showing papers on "Recursive least squares filter published in 2022"

State-of-charge estimation for the lithium-ion battery based on adaptive extended Kalman filter using improved parameter identification

Abstract: The state of charge(SOC) of lithium-ion battery is an essential parameter of battery management system. Accurate estimation of SOC is conducive to give full play to the capacity and performance of the battery. For the problems of selection of forgetting factor and poor robustness and susceptibility to the noise of extended Kalman filtering algorithm, this paper proposes a SOC estimation method for the lithium-ion battery based on adaptive extended Kalman filter using improved parameter identification. Firstly, the Thevenin equivalent circuit model is established and the recursive least squares with forgetting factor(FFRLS) method is used to achieve the parameter identification. Secondly, an evaluation factor is defined, and fuzzy control is used to realize the mapping between the evaluation factor and the correction value of forgetting factor, so as to realize the adaptive adjustment of forgetting factor. Finally, the noise adaptive algorithm is introduced into the extended Kalman filtering algorithm(AEKF) to estimate the SOC based on the identification results, which is applied to the parameter identification at the next time and executed circularly, so as to realize the accurate estimation of SOC. The experimental results show that the proposed method has good robustness and estimation accuracy compared with other filtering algorithms under different working conditions, state of health(SOH) and temperature.

An Information Appraisal Procedure: Endows Reliable Online Parameter Identification to Lithium-Ion Battery Model

Abstract: Online parameter identification is vital for boosting the accuracy of the battery equivalent circuit model (ECM) under dynamic profiles. However, traditional recursive least squares (RLS) method easily decays with the noise corruption from sensors or insufficient exciting signal in reality, which further limits the performance of ECM in battery modeling and states estimation. This article thus proposes a reliable online parameter identification method for battery ECM, which utilizes a well-designed information appraisal procedure based on the Fisher-information-based Cramer–Rao lower bound (CRLB). Without increasing much computing complexity, a comprehensive appraisal indicator, derived recursively from CRLB, enables a new mechanism for online parameter updating. Simulation and experimental results prove the validity of the proposed method under different driving cycles, temperatures, and aging conditions. The results show that the identification accuracy of the proposed method has been significantly improved comparing with a typical RLS and a multiple adaptive forgetting factors RLS method.

Estimation of Sideslip Angle and Tire Cornering Stiffness Using Fuzzy Adaptive Robust Cubature Kalman Filter

Abstract: The accurate information of sideslip angle (SA) and tire cornering stiffness (TCS) is essential for advanced chassis control systems. However, SA and TCS cannot be directly measured by in-vehicle sensors. Thus, it is a hot topic to estimate SA and TCS with only in-vehicle sensors by an effective estimation method. In this article, we propose a novel fuzzy adaptive robust cubature Kalman filter (FARCKF) to accurately estimate SA and TCS. The model parameters of the FARCKF are dynamically updated using recursive least squares. A Takagi–Sugeno fuzzy system is developed to dynamically adjust the process noise parameter in the FARCKF. Finally, the performance of FARCKF is demonstrated via both simulation and experimental tests. The test results indicate that the estimation accuracy of SA and TCS is higher than that of the existing methods. Specifically, the estimation accuracy of SA is at least improved by more than 48%, while the estimators of TCS are closer to the reference values.

Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems

Abstract: This article deals with the problems of the parameter estimation for feedback nonlinear controlled autoregressive systems (i.e., feedback nonlinear equation‐error systems). The bilinear‐in‐parameter identification model is formulated to describe the feedback nonlinear system. An overall recursive least squares algorithm is developed to handle the difficulty of the bilinear‐in‐parameter. For the purpose of avoiding the heavy computational burden, an overall stochastic gradient algorithm is deduced and the forgetting factor is introduced to improve the convergence rate. Furthermore, the convergence analysis of the proposed algorithms are established by means of the stochastic process theory. The effectiveness of the proposed algorithms are illustrated by the simulation example.

A novel lumped thermal characteristic modeling strategy for the online adaptive temperature and parameter co-estimation of vehicle lithium-ion batteries

Abstract: Accurate modeling of thermal characteristics is critical to the safe use and reliable management of lithium-ion batteries. However, limitations in sensors and testing methods make online real-time acquisition of internal temperatures extremely difficult. This paper uses the similarity of dynamic system modeling to construct a lumped thermal characteristic model of the battery. By analyzing the heat conduction mechanism inside the battery, the optimized heat path model is combined with the classical Bernardi equation to realize the state description of the battery thermal characteristic system. In addition, the forgetting factor recursive least squares algorithm is used to realize the online identification of the parameters of the lumped thermal characteristics model. Meanwhile, the identification of the external thermal resistance is coupled with the estimation of the internal temperature, and a novel online adaptive co-estimation strategy based on the forgetting factor recursive least squares — joint Kalman filter is proposed, which solves the problem that the external thermal resistance cannot be accurately identified adaptively in a complex environment. The experimental results show that the maximum root-mean-square error of the model under different experiments is 0.53 °C, which verifies the high-accuracy of the lumped thermal characteristics modeling strategy.

State of charge estimation of Power lithium-ion battery based on an Affine Iterative Adaptive Extended Kalman Filter

Abstract: State of charge (SOC) is a very important parameter for power lithium-ion battery in battery operation, but it cannot be measured directly, so it needs to be accurately estimationed. Considering the time-varying characteristics of the model parameters of the power lithium-ion battery, an online identification algorithm called Adaptive Forgetting Factor Recursive Augmented Least Squares (AFFRALS) is proposed. It can obtain a more accurate model compared with the offline method (fixed model parameters) by considering that the noise of power lithium-ion battery system is commonly non-Gaussian white noise in practice, which is different from the existing equivalent circuit models. Then, an Affine Iterative Adaptive Extended Kalman Filter (AIAEKF) method is proposed to deal with the non-Gaussian white noise and accelerate the convergence rate of the estimated results when the initial SOC value is wrong. Experiments demonstrate the effective of the online identification method as well as the SOC estimation method. This method shows a faster convergence rate and better SOC estimation performance than the traditional Extend Kalman Filter (EKF) method. The RMSE of the SOC estimation results is less than 0.023 except for 0 °C, which rarely occurs in practice. • AFFRALS algorithm for is proposed for online parameters identification. • The new identification method can obtain a more accurate model under working conditions. • SOC estimation is reached by AIAEKF algorithm. • The new SOC estimation algorithm has a faster correction capability and no overshoot.

Data-Reuse Recursive Least-Squares Algorithms

Abstract: There are different strategies to improve the overall performance of the recursive least-squares (RLS) adaptive filter. In this letter, we focus on the data-reuse approach, aiming to improve the convergence rate/tracking of the algorithm by reusing the same set of data (i.e., the input and reference signals) several times. First, we present a computationally efficient data-reuse RLS algorithm, which is the result of a low complexity implementation of the data-reuse process. Moreover, we extend the idea to the fast RLS algorithm. Simulations performed in the context of echo cancellation support the performance gain.

State of charge estimation of lithium-ion battery based on improved forgetting factor recursive least squares-extended Kalman filter joint algorithm

Abstract: In order to solve the problem that forgetting factor recursive least squares (FFRLS) is prone to abnormal jitter and even divergence under complex working conditions, improved forgetting factor recursive least squares based on dynamic constraint and parameter backtracking is proposed. A joint algorithm of improved forgetting factor recursive least squares and extended Kalman filter (EKF) is used to estimate the state of charge (SOC) of lithium-ion battery. Firstly, parameters of Thevenin equivalent circuit model are identified on-line by the improved FFRLS considering dynamic constraint and parameter backtracking, and then the SOC of lithium-ion battery is estimated by extended Kalman filter. The results show that the improved forgetting factor recursive least squares has high accuracy of battery model parameters identification and the joint algorithm also has high accuracy and robustness of SOC estimation under dynamic stress test (DST) condition, the maximum absolute SOC estimation error is 2.49 % and the average absolute SOC estimation error is 1.39 %. • Thevenin equivalent circuit model of lithium-ion battery is established. • Improved forgetting factor recursive least squares based on dynamic constraint and parameter backtracking is proposed. • Improved forgetting factor recursive least squares extended Kalman filter joint algorithm is used to estimate SOC. • The joint algorithm has high accuracy and robustness of SOC estimation under dynamic stress test condition.

Optimization of adaptive filter control parameters for non-invasive fetal electrocardiogram extraction

Abstract: This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters of different hybrid systems used for non-invasive fetal electrocardiogram (fECG) extraction. The tested hybrid systems consist of two different blocks, first for maternal component estimation and second, so-called adaptive block, for maternal component suppression by means of an adaptive algorithm (AA). Herein, we tested and optimized four different AAs: Adaptive Linear Neuron (ADALINE), Standard Least Mean Squares (LMS), Sign-Error LMS, Standard Recursive Least Squares (RLS), and Fast Transversal Filter (FTF). The main criterion for optimal parameter selection was the F1 parameter. We conducted experiments using real signals from publicly available databases and those acquired by our own measurements. Our optimization method enabled us to find the corresponding optimal settings for individual adaptive block of all tested hybrid systems which improves achieved results. These improvements in turn could lead to a more accurate fetal heart rate monitoring and detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to find optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing and analysis, opening new diagnostic possibilities of non-invasive fetal electrocardiography.

Adaptive Hammerstein Filtering via Recursive Non-Convex Projection

Abstract: Hammerstein model with a static nonlinearity followed by a linear filter is commonly used in numerous applications. This paper focuses on adaptive filtering techniques for parameter identification of Hammerstein systems and output prediction of nonlinear systems. By formulating the underlying filtering problem as a recursive bilinear least-squares optimization with the non-convex feasible region constraint, we develop a recursive non-convex projected least-squares (RncPLS) algorithm based on alternating direction method of multipliers (ADMM). The RncPLS algorithm alternates between implementing ridge regression and projecting on the non-convex feasible set, which successively refines the system parameters. The convergence and accuracy properties of the proposed RncPLS algorithm are theoretically investigated. Moreover, extensive simulation results in the context of system identification, nonlinear predication, and acoustic echo cancellation, are also included to demonstrate the performance characteristics of the proposed algorithm.

Recursive Least Squares and Adaptive Kalman Filter-Based State and Parameter Estimation for Series Arc Fault Detection on DC Microgrids

Abstract: In this article, we present a recursive least squares (RLS) and adaptive Kalman filter (AKF)-based state and parameter estimation (SE and PE) for series arc fault (SAF) detection and identification on dc microgrids. It is evident from the state-of-the-art research on dc SAFs that due to the lack of zero crossings and low current of the fault, the detection/identification of a SAF is difficult. Furthermore, due to the unplanned placement of sensors and the effect of SAF’s noise signatures on the adjacent sensors, we present a RLS-based SE for voltages and injection currents. The injection currents and nodal voltages from the states are then used by the AKF for a quick SAF detection, by estimating line admittances on the microgrid. The simulation results, control hardware in loop (CHIL), and experimental results are presented to manifest the SE–PE technique’s potential.

Online Modeling of the CNC Engraving System With Dead-Zone Input Nonlinearity

Abstract: This article is concerned with modeling of a class of permanent-magnet-synchronous-motor-based drive systems with dead-zone input nonlinearity. The experimental testing method based on recursive least squares (RLS) is combined with the mechanism analysis method based on physical laws. With the structure of the analytical model first determined by the mechanism analysis method, an online RLS-based data-driven method is then exploited to obtain the unknown parameters of the analytical model. Since the unknown parameters are decoupled in the proposed method, all parameter estimates are available. An auxiliary model and a variable forgetting factor are constructed to ensure the high accuracy and the fast convergence of the proposed method, respectively. Besides, the asymptotic convergence property of the proposed method is analyzed, and the upper bound of the parameter estimation error is also presented. The effectiveness of the proposed method is verified by simulations and experiments on a self-designed computer numerical control engraving system.

Multi-Layered Recursive Least Squares for Time-Varying System Identification

Abstract: Traditional recursive least squares (RLS) adaptive filtering is widely used to estimate the impulse responses (IR) of an unknown system. Nevertheless, the RLS estimator shows poor performance when tracking rapidly time-varying systems. In this paper, we propose a multi-layered RLS (m-RLS) estimator to address this concern. The m-RLS estimator is composed of multiple RLS estimators, each of which is employed to estimate and eliminate the misadjustment of the previous layer. It is shown that the mean squared error (MSE) of the m-RLS estimate can be minimized by selecting the optimum number of layers. We provide a method to determine the optimum number of layers. A low-complexity implementation of m-RLS is discussed and it is indicated that the complexity order of the proposed estimator can be reduced to ${\mathcal O}(M)$ , where $M$ is the IR length. Through simulations, we show that m-RLS outperforms the classic RLS and the RLS methods with a variable forgetting factor.

An Adaptive State of Charge Estimation Method of Lithium-ion Battery Based on Residual Constraint Fading Factor Unscented Kalman Filter

Abstract: It is crucial to conduct highly accurate estimation of the state of charge (SOC) of lithium-ion batteries during the real-time monitoring and safety control. Based on residual constraint fading factor unscented Kalman filter, the paper proposes an SOC estimation method to improve the accuracy of online estimating SOC. A priori values of terminal voltage were fitted using cubic Hermite interpolation. In combination with the Thevenin equivalent circuit model, the method of adaptive forgetting factor recursive least squares is used to identify the model parameters. To address the problem of the UKF method strongly influenced by system noise and observation noise, the paper designs an improved method of residual constrained fading factor. Finally, the effectiveness of this method was verified by the test of Hybrid Pulse Power Characteristic and Beijing Bus Dynamic Stress Test. Results show that under HPPC conditions, compared with other methods, the algorithm in the paper estimates that the SOC error of the battery remains between -0.38% and 0.948%, reducing the absolute maximum error by 51.5% at least and the average error by 62.7% at least. Moreover, under the condition of Beijing Bus Dynamic Stress Test the algorithm estimates the SOC error of the battery stays between -0.811% and 0.526%, the SOC estimation errors are all within 0.2% after operation of ten seconds. Compared with other methods, the absolute maximum error can be reduced by 42.7% at least, the average error is reduced by 95% at least. Finally, the test proves that the method is of higher accuracy, better convergence and stronger robustness.

Proportionate RLS with l1 norm regularization: Performance analysis and fast implementation

Abstract: Sparse recursive least squares (RLS) adaptive filter algorithms achieve faster convergence and better performance than the standard RLS algorithm under sparse systems. Existing methods are designed by either incorporating a sparse regularization term, e.g., l1-norm, into the standard RLS cost function or introducing a proportionate matrix into the updating equation. As the principles behind the two designs are complementary, we propose in this paper to combine them together and develop an enhanced method named the l1-proportionate RLS (l1-PRLS). Theoretical performance analysis of the proposed l1-PRLS is carried out in terms of the first-order and second-order transient property, and steady-state property. Based on the analysis, guidances for the selection of adaptive parameters are obtained. With proper choices of relevant parameters, the l1-PRLS is guaranteed to be stable and achieve better performance than existing methods. To improve the applicability of the l1-PRLS, a fast implementation named the l1-proportionate stable fast transversal filter (l1-PSFTF) is also derived. Simulation results from sparse system identifications support the theoretical analysis.

Improving Recursive Dynamic Parameter Estimation of Manipulators by knowing Robot's Model integrated in the Controller

Abstract: By identifying the manipulator's model that is integrated in some industrial model-based controllers, recursive parameters’ estimation algorithms can be enhanced to have a better performance in online applications. In this paper, two improvements on recursive estimation of robot's dynamic parameter estimation are addressed. Firstly, the internal model can serve to initialize the parameters in recursive estimation algorithms, as the Recursive Least-Squares (RLS) and the Recursive Instrumental Variables (RIV). Secondly, the commanded position, which is used by the controller as a reference trajectory, can replace the external simulation of the dynamic model needed for recursive algorithms as the RIV. These two improvements make recursive algorithms more suitable for online application, specially RIV, where no data filtering nor external simulation needs to be done. Offline experimental validation on the KUKA LBR iiwa R820 is carried out, showing its feasibility for online application.

State of charge estimation for the Vanadium Redox Flow Battery based on Extended Kalman filter using modified parameter identification

Abstract: ABSTRACT Vanadium Redox Flow Battery (VRFB) is widely utilized in energy storage due to its excellent characteristics. Credible knowledge of the state of charge (SOC) is a pre-condition for the effective health management of batteries. The SOC estimation depends on the second-order RC equivalent circuit model (ECM) parameters identified by the forgetting factor recursive least squares (FF-RLS). Considering the time-varying characteristics of the model parameters, an adaptive forgetting factor recursive least squares (AFF-RLS) based on the gradient descent method is proposed to identify the EMC parameters. The forgetting factor can be obtained based on the error between the terminal voltage measurement and terminal voltage estimation. The proposed joint estimator has been verified by performing charge and discharge experiments for VRFB single cell. The mean error, maximum estimated error and root mean square error of SOC under 6 A pulse discharging current are 1.38 × 10−3, 3.76 × 10−5, and 1.38 × 10−5. The result indicates that AFF-RLS is robust against outliers of model parameters and improve the anti-interference of EKF. In addition, this paper finds that the different value of learning rate can affect the sensitive and anti-interference of EKF.

Four-Stage Recursive Least Squares Algorithm for CARARMA Systems

Abstract: This paper derives four -stage recursive least squares algorithm for Controlled Autoregressive Autoregressive Moving Average (CARARMA) systems. By applying the decomposition technique, (CARARMA) system decompose into four subsystems that include one parameter vector each. The proposed algorithm improves the accuracy of estimated parameters and decrease the computational burden compared with recursive generalized least squares algorithm. The simulation example is given to indicate the efficiency of the algorithm.

Low Complexity RLS Adaptive Filters

Abstract: This paper presents two adaptive filters with a reduced arithmetic complexity which are based on the Recursive Least Squares (RLS) algorithms. The first one is the cascaded adaptive filter. The second one is the adaptive filter with the diagonalized correlation matrix of the input signal. The both filters have a reduced arithmetic complexity comparing to the direct implementation of the adaptive filter. The cost of the reduction is some degradation of the adaptive filter performance. The reduction is achieved only if the RLS algorithms with quadratic complexity are used. The computational procedures and the arithmetic complexities of the considered adaptive filters are the same, but the performance is different. This paper presents the RLS algorithms based on the Matrix Inversion Lemma (MIL). However, all the results and conclusions are valid for any RLS algorithms with quadratic complexity. The paper demonstrates the considered adaptive filter performance via simulation.

A Recursive Least-Squares with a Time-Varying Regularization Parameter

Abstract: Recursive least-squares (RLS) algorithms are widely used in many applications, such as real-time signal processing, control and communications. In some applications, regularization of the least-squares provides robustness and enhances performance. Interestingly, updating the regularization parameter as processing data continuously in time is a desirable strategy to improve performance in applications such as beamforming. While many of the presented works in the literature assume non-time-varying regularized RLS (RRLS) techniques, this paper deals with a time-varying RRLS as the parameter varies during the update. The paper proposes a novel and efficient technique that uses an approximate recursive formula, assuming a slight variation in the regularization parameter to provide a low-complexity update method. Simulation results illustrate the feasibility of the derived formula and the superiority of the time-varying RRLS strategy over the fixed one.