Experience with synchronous generator parameter identification using a Kalman filter

2010 ◽  
Author(s):  
Kang Lin ◽  
E Kyriakides ◽  
G T Heydt ◽  
N Logic ◽  
B Singh
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Synchronous Generator

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

Energies ◽  
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.

Parameter identification of the dynamic Winkler soil–structure interaction model using a hybrid unscented Kalman filter–multi-objective harmony search algorithm

2020 ◽  
Vol 23 (12) ◽  
pp. 2653-2668
Author(s):  
Javier Naranjo-Pérez ◽  
Javier Fernando Jiménez-Alonso ◽  
Andrés Sáez
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Soil Structure ◽  
Optimization Problem ◽  
Unscented Kalman Filter ◽  
Harmony Search ◽  
Soil Structure Interaction ◽  
Structure Interaction ◽  
Multi Objective ◽  
Winkler Model

Soil–structure interaction is a key aspect to take into account when simulating the response of civil engineering structures subjected to dynamic actions. To this end, and due to its simplicity and ease of implementation, the dynamic Winkler model has been widely used in practical engineering applications. In this model, soil–structure interaction is simulated by means of spring–damper elements. A crucial point to guarantee the adequate performance of the approach is to accurately estimate the constitutive parameters of these elements. To this aim, this article proposes the application of a recently developed parameter identification method to address such problem. In essence, the parameter identification problem is transformed into an optimization problem, so that the parameters of the dynamic Winkler model are estimated by minimizing the relative differences between the numerical and experimental modal properties of the overall soil–structure system. A recent and efficient hybrid algorithm, based on the combination of the unscented Kalman filter and multi-objective harmony search algorithms, is satisfactorily implemented to solve the optimization problem. The performance of this proposal is then validated via its implementation in a real case-study involving an integral footbridge.

scholarly journals Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Wenxian Duan ◽  
Chuanxue Song ◽  
Yuan Chen ◽  
Feng Xiao ◽  
Silun Peng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Life ◽  
Model Parameters ◽  
Simulation Results

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

Dynamic Modeling and Parameter Identification for Biological Networks

2011 ◽  
pp. 478-510 ◽  
Author(s):  
Fortunato Bianconi ◽  
Gabriele Lillacci ◽  
Paolo Valigi
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Extended Kalman Filter ◽  
Dynamic Modeling ◽  
Biological Networks ◽  
In Silico ◽  
High Gain ◽  
The Other ◽  
Sensitivity Analyses ◽  
Identification Techniques

Then, two different parameter identification techniques are presented for the proposed models. One is based on a least squares procedure, which treats the signals provided by a high gain observer; the other one is based on a Mixed Extended Kalman Filter. Prior to the estimation phase, identifiability and sensitivity analyses are used to determine which parameters can be and/or should be estimated. The procedures are tested and compared by means of data obtained by in silico experiments.

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