Assessment of reduced-order unscented Kalman filter for parameter identification in 1-dimensional blood flow models using experimental data

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.

Download Full-text

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

Mathematical Problems in Engineering ◽

10.1155/2020/9502605 ◽

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.

Download Full-text