Adaptive Kalman filters for nonlinear finite element model updating

Song, Mingming; Astroza, Rodrigo; Ebrahimian, Hamed; Moaveni, Babak; Papadimitriou, Costas

doi:10.1016/j.ymssp.2020.106837

Cited by 76 publications

(49 citation statements)

References 48 publications

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“…The system noise covariance matrix (Q) is then estimated using the difference between a-posteriori and a-priori states. The optimization is performed in real-time for each time instant [41][42][43]50]. The assumptions for the ASVSF-VBL are as follows,…”

Section: Noise Adaptation For Smooth Variable Structure Filtermentioning

confidence: 99%

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Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries

et al. 2021

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Battery Management Systems (BMSs) are used to manage the utilization of batteries and their operation in Electric and Hybrid Vehicles. It is imperative for efficient and safe operation of batteries to be able to accurately estimate the State of Charge (SoC), State of Health (SoH) and State of Power (SoP). The SoC and SoH estimation must remain robust and accurate despite aging and in presence of noise, uncertainties and sensor biases. This paper introduces a robust adaptive filter referred to as the Adaptive Smooth Variable Structure Filter with a time-varying Boundary Layer (ASVSF-VBL) for the estimation of the SoC and SoH in electrified vehicles. The internal model of the filter is a third-order equivalent circuit model (ECM) and its state vector is augmented to enable estimation of the internal resistance and current bias. It is shown that system and measurement noise covariance adaptation for the SVSF-VBL approach improves the performance in state estimation of a battery. The estimated internal resistance is then utilized to improve determination of the battery’s SoH. The effectiveness of the proposed method is validated using experimental data from tests on Lithium Polymer automotive batteries. The results indicate that the SoC estimation error can remain within less than 2% over the full operating range of SoC along with an accurate estimation of SoH.

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Section: Noise Adaptation For Smooth Variable Structure Filtermentioning

confidence: 99%

“…Zhang proposed an adaptive KF for joint polarization tracking [34]. In [42], a comparison between different adaptive strategies for EKF is presented. In [43], sufficient conditions for noise covariance identification of a KF have been introduced.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries

et al. 2021

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“…Modeling errors are often the most critical and influential source of uncertainty in modeling, model updating and response predictions, especially for civil structures, due to their complexity and large-scale size [ 37 , 52 ]. Different modeling assumptions and simplifications can introduce errors in the numerical models, including, but not limited to, structural linearity assumption, boundary conditions simplification, unmodeled non-structural components, material property uncertainty, discretization, geometry errors, and connection simplification.…”

Section: Uncertainty Quantification and Propagation Through Hierarmentioning

confidence: 99%

Accounting for Modeling Errors and Inherent Structural Variability through a Hierarchical Bayesian Model Updating Approach: An Overview

Song

Behmanesh

Moaveni

et al. 2020

Sensors

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Mechanics-based dynamic models are commonly used in the design and performance assessment of structural systems, and their accuracy can be improved by integrating models with measured data. This paper provides an overview of hierarchical Bayesian model updating which has been recently developed for probabilistic integration of models with measured data, while accounting for different sources of uncertainties and modeling errors. The proposed hierarchical Bayesian framework allows one to explicitly account for pertinent sources of variability such as ambient temperatures and/or excitation amplitudes, as well as modeling errors, and therefore yields more realistic predictions. The paper reports observations from applications of hierarchical approach to three full-scale civil structural systems, namely (1) a footbridge, (2) a 10-story reinforced concrete (RC) building, and (3) a damaged 2-story RC building. The first application highlights the capability of accounting for temperature effects within the hierarchical framework, while the second application underlines the effects of considering bias for prediction error. Finally, the third application considers the effects of excitation amplitude on structural response. The findings underline the importance and capabilities of the hierarchical Bayesian framework for structural identification. Discussions of its advantages and performance over classical deterministic and Bayesian model updating methods are provided.

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“…The methods of health state prediction for sensors can be divided into three categories: analytical-based models, data-driven-based intelligent learning models, and qualitative knowledge-based models. In a research object with an accurate math model, the analyticalbased model has been widely used, such as Kalman Filter 3,4 and improved Kalman Filter. 5,6 However, it is difficult to establish a unified mathematical model which can predict the health states of sensors precisely due to the wide variety of sensors.…”

Section: Introductionmentioning

confidence: 99%

A new model based on belief rule base and membership function (BRB-MF) for health state prediction in sensor

Yin

Shi

Peng

et al. 2022

Advances in Mechanical Engineering

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Health state prediction is an effective way to improve the reliability for sensors. In the process of sensor degradation, it is difficult to obtain more effective monitoring data. And in the classification of health states, how to identify the adjacent state is also a problem. This paper proposed a health state prediction model based on belief rule base (BRB) and membership function (MF), which is called BRB-MF. In the model, BRB can make full use of expert knowledge and poor effective data. In the prediction results of BRB, it may be not completely logical or not entirely appropriate facing adjacent states of sensor. In order to solve the problem, MF is used to continue the analysis of the predicted results of BRB. In the BRB-MF model, the covariance matrix adaptation evolutionary strategies (CMA-ES) optimization algorithm is used to update the model parameters to make up for the uncertainty of expert knowledge. In the end, the brightness sensor of the rail vehicle LED lighting system is taken as a case study. The results show that the BRB-MF model can predict the health state of sensor with a high accuracy and a reasonable state.

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Adaptive Kalman filters for nonlinear finite element model updating

Cited by 76 publications

References 48 publications

Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries

Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries

Accounting for Modeling Errors and Inherent Structural Variability through a Hierarchical Bayesian Model Updating Approach: An Overview

A new model based on belief rule base and membership function (BRB-MF) for health state prediction in sensor

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