Estimation of optical model parameters and their correlation matrix using Unscented Transform Kalman Filter technique

Simulation of fracture initiation, propagation, and arrest is a problem of interest for many applications in the scientific community. There are a number of numerical methods used for this purpose, and among the most widely accepted is the combined finite-discrete element method (FDEM). To model fracture with FDEM, material behavior is described by specifying a combination of elastic properties, strengths (in the normal and tangential directions), and energy dissipated in failure modes I and II, which are modeled by incorporating a parameterized softening curve defining a post-peak stress-displacement relationship unique to each material. In this work, we implement a data assimilation method to estimate key model parameter values with the objective of improving the calibration processes for FDEM fracture simulations. Specifically, we implement the ensemble Kalman filter assimilation method to the Hybrid Optimization Software Suite (HOSS), a FDEM-based code which was developed for the simulation of fracture and fragmentation behavior. We present a set of assimilation experiments to match the numerical results obtained for a Split Hopkinson Pressure Bar (SHPB) model with experimental observations for granite. We achieved this by calibrating a subset of model parameters. The results show a steady convergence of the assimilated parameter values towards observed time/stress curves from the SHPB observations. In particular, both tensile and shear strengths seem to be converging faster than the other parameters considered.

Download Full-text

Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter

Energies ◽

10.3390/en14041054 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1054

Author(s):

Kuo Yang ◽

Yugui Tang ◽

Zhen Zhang

Keyword(s):

Kalman Filter ◽

Parameter Identification ◽

Open Circuit Voltage ◽

Lithium Ion ◽

Open Circuit ◽

State Of Charge ◽

Recursive Least Squares ◽

Model Parameters ◽

Soc Estimation ◽

Battery Model

With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.

Download Full-text

Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement

Signals ◽

10.3390/signals2030027 ◽

2021 ◽

Vol 2 (3) ◽

pp. 434-455

Author(s):

Sujan Kumar Roy ◽

Kuldip K. Paliwal

Keyword(s):

Kalman Filter ◽

Speech Enhancement ◽

Linear Prediction ◽

Real Life ◽

Model Parameters ◽

Noise Variance ◽

Noisy Speech ◽

Kalman Gain ◽

Whitening Filter ◽

Prediction Coefficient

Inaccurate estimates of the linear prediction coefficient (LPC) and noise variance introduce bias in Kalman filter (KF) gain and degrade speech enhancement performance. The existing methods propose a tuning of the biased Kalman gain, particularly in stationary noise conditions. This paper introduces a tuning of the KF gain for speech enhancement in real-life noise conditions. First, we estimate noise from each noisy speech frame using a speech presence probability (SPP) method to compute the noise variance. Then, we construct a whitening filter (with its coefficients computed from the estimated noise) to pre-whiten each noisy speech frame prior to computing the speech LPC parameters. We then construct the KF with the estimated parameters, where the robustness metric offsets the bias in KF gain during speech absence of noisy speech to that of the sensitivity metric during speech presence to achieve better noise reduction. The noise variance and the speech model parameters are adopted as a speech activity detector. The reduced-biased Kalman gain enables the KF to minimize the noise effect significantly, yielding the enhanced speech. Objective and subjective scores on the NOIZEUS corpus demonstrate that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than some benchmark methods.

Download Full-text

Multi-day flow forecasting using the Kalman filter

Canadian Journal of Civil Engineering ◽

10.1139/l91-037 ◽

1991 ◽

Vol 18 (2) ◽

pp. 320-327 ◽

Cited By ~ 1

Author(s):

Murray A. Fitch ◽

Edward A. McBean

Keyword(s):

Kalman Filter ◽

Prediction Model ◽

Linear Prediction ◽

Measured Data ◽

System Model ◽

Parameter Estimates ◽

Model Parameters ◽

Forecasting Performance ◽

Optimal Forecasting ◽

Linear Prediction Model

A model is developed for the prediction of river flows resulting from combined snowmelt and precipitation. The model employs a Kalman filter to reflect uncertainty both in the measured data and in the system model parameters. The forecasting algorithm is used to develop multi-day forecasts for the Sturgeon River, Ontario. The algorithm is shown to develop good 1-day and 2-day ahead forecasts, but the linear prediction model is found inadequate for longer-term forecasts. Good initial parameter estimates are shown to be essential for optimal forecasting performance. Key words: Kalman filter, streamflow forecast, multi-day, streamflow, Sturgeon River, MISP algorithm.

Download Full-text

Optical Model Parameters for the Ar-36+α and Ca-40+α Systems

10.1063/1.1945044 ◽

2005 ◽

Cited By ~ 2

Author(s):

L. Generalov

Keyword(s):

Optical Model ◽

Model Parameters

Download Full-text

Modeling of temporal groundwater level variations based on a Kalman filter adaptation algorithm with exogenous inputs

Journal of Hydroinformatics ◽

10.2166/hydro.2016.063 ◽

2016 ◽

Vol 19 (2) ◽

pp. 191-206 ◽

Cited By ~ 5

Author(s):

Emmanouil A. Varouchakis

Keyword(s):

Kalman Filter ◽

Groundwater Level ◽

Auxiliary Information ◽

Model Parameters ◽

Adaptation Algorithm ◽

Arx Model ◽

Resources Management ◽

Temporal Modelling ◽

The Mean ◽

First Time

Reliable temporal modelling of groundwater level is significant for efficient water resources management in hydrological basins and for the prevention of possible desertification effects. In this work we propose a stochastic method of temporal monitoring and prediction that can incorporate auxiliary information. More specifically, we model the temporal (mean annual and biannual) variation of groundwater level by means of a discrete time autoregressive exogenous variable (ARX) model. The ARX model parameters and its predictions are estimated by means of the Kalman filter adaptation algorithm (KFAA) which, to our knowledge, is applied for the first time in hydrology. KFAA is suitable for sparsely monitored basins that do not allow for an independent estimation of the ARX model parameters. We apply KFAA to time series of groundwater level values from the Mires basin in the island of Crete. In addition to precipitation measurements, we use pumping data as exogenous variables. We calibrate the ARX model based on the groundwater level for the years 1981 to 2006 and use it to predict the mean annual and biannual groundwater level for recent years (2007–2010). The predictions are validated with the available annual averages reported by the local authorities.

Download Full-text

A Fuzzy State-of-Charge Estimation Algorithm Combining Ampere-Hour and an Extended Kalman Filter for Li-Ion Batteries Based on Multi-Model Global Identification

Applied Sciences ◽

10.3390/app8112028 ◽

2018 ◽

Vol 8 (11) ◽

pp. 2028 ◽

Cited By ~ 9

Author(s):

Xin Lai ◽

Dongdong Qiao ◽

Yuejiu Zheng ◽

Long Zhou

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Estimation Algorithm ◽

State Of Charge ◽

Distribution Area ◽

Model Parameters ◽

Model Accuracy ◽

Soc Estimation ◽

Fuzzy Fusion ◽

Fuzzy State

The popular and widely reported lithium-ion battery model is the equivalent circuit model (ECM). The suitable ECM structure and matched model parameters are equally important for the state-of-charge (SOC) estimation algorithm. This paper focuses on high-accuracy models and the estimation algorithm with high robustness and accuracy in practical application. Firstly, five ECMs and five parameter identification approaches are compared under the New European Driving Cycle (NEDC) working condition in the whole SOC area, and the most appropriate model structure and its parameters are determined to improve model accuracy. Based on this, a multi-model and multi-algorithm (MM-MA) method, considering the SOC distribution area, is proposed. The experimental results show that this method can effectively improve the model accuracy. Secondly, a fuzzy fusion SOC estimation algorithm, based on the extended Kalman filter (EKF) and ampere-hour counting (AH) method, is proposed. The fuzzy fusion algorithm takes advantage of the advantages of EKF, and AH avoids the weaknesses. Six case studies show that the SOC estimation result can hold the satisfactory accuracy even when large sensor and model errors exist.

Download Full-text

An All-Region State-of-Charge Estimator Based on Global Particle Swarm Optimization and Improved Extended Kalman Filter for Lithium-Ion Batteries

Electronics ◽

10.3390/electronics7110321 ◽

2018 ◽

Vol 7 (11) ◽

pp. 321 ◽

Cited By ~ 9

Author(s):

Xin Lai ◽

Wei Yi ◽

Yuejiu Zheng ◽

Long Zhou

Keyword(s):

Particle Swarm Optimization ◽

Kalman Filter ◽

Lithium Ion Batteries ◽

Extended Kalman Filter ◽

Particle Swarm ◽

Lithium Ion ◽

State Of Charge ◽

Model Parameters ◽

Swarm Optimization ◽

Soc Estimation

In this paper, a novel model parameter identification method and a state-of-charge (SOC) estimator for lithium-ion batteries (LIBs) are proposed to improve the global accuracy of SOC estimation in the all SOC range (0–100%). Firstly, a subregion optimization method based on particle swarm optimization is developed to find the optimal model parameters of LIBs in each subregion, and the optimal number of subregions is investigated from the perspective of accuracy and computation time. Then, to solve the problem of a low accuracy of SOC estimation caused by large model error in the low SOC range, an improved extended Kalman filter (IEKF) algorithm with variable noise covariance is proposed. Finally, the effectiveness of the proposed methods are verified by experiments on two kinds of batteries under three working cycles, and case studies show that the proposed IEKF has better accuracy and robustness than the traditional extended Kalman filter (EKF) in the all SOC range.

Download Full-text