Tuning of Computer Model Parameters in Managed Pressure Drilling Applications Using an Unscented Kalman Filter Technique

2005 ◽  
Author(s):  
Jan Einar Gravdal ◽  
Rolf Johan Lorentzen ◽  
Kjell Kaare Fjelde ◽  
Erlend Heggelund Vefring
Keyword(s):  
Kalman Filter ◽  
Computer Model ◽  
Unscented Kalman Filter ◽  
Model Parameters ◽  
Filter Technique ◽  
Managed Pressure Drilling

Tuning of Computer Model Parameters in Managed-Pressure Drilling Applications Using an Unscented-Kalman-Filter Technique

SPE Journal ◽  
2010 ◽  
Vol 15 (03) ◽  
pp. 856-866 ◽  
Author(s):  
Jan Einar Gravdal ◽  
Rolf J. Lorentzen ◽  
Kjell K. Fjelde ◽  
Erlend H. Vefring
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Synthetic Data ◽  
Pressure Profile ◽  
Model Parameters ◽  
High Pressure High Temperature ◽  
Managed Pressure Drilling ◽  
High Degree ◽  
Advanced Computer

Summary To manage the annular pressure profile during managed-pressure drilling (MPD) operations, simulations performed with advanced computer models are needed. To obtain a high degree of accuracy in these simulations, it is crucial that all parameters describing the system are as correct as possible. A new methodology for real-time updating of key parameters in a well-flow model by taking into account real-time measurements, including measuring uncertainty, is presented. Key model parameters are tuned using a recently developed estimation technique based on the traditional Kalman filter. The presented methodology leads to a more-accurate prediction of well-flow scenarios. Although the present study is motivated by applications in MPD, the idea of tuning model parameters should be of great importance in a wide area of applications. The performance of the filter is studied, using both synthetic data and real measurements from a North Sea high-pressure/high-temperature (HP/HT) drilling operation. Benefits by this approach are seen in more-accurate downhole-pressure predictions, which are of major importance for safety and economic reasons during MPD operations.

scholarly journals Battery state of charge estimation and online wheel slip ratio control for autonomous wheeled mobile robot

2021 ◽  
Author(s):  
Maral Partovibakhsh
Keyword(s):  
Kalman Filter ◽  
Power Consumption ◽  
Mobile Robot ◽  
Unscented Kalman Filter ◽  
Longitudinal Velocity ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Slip Ratio ◽  
Soc Estimation ◽  
Increasing Function

For autonomous mobile robots moving in unknown environment, accurate estimation of available power along with the robot power demand for each mission is paramount to successful completion of that mission. Regarding the power consumption, the control unit deals with two tasks simultaneously: 1) it has to monitor the power supply (batteries) state of charge (SoC) constantly. This leads to estimation of robot current available power. Besides, batteries are sensitive to deep discharge or overcharge. The battery SoC is an essential factor in power management of a mobile robot. Accurate estimation of the battery SoC can improve power management, optimize the performance, extend the lifetime, and prevent permanent damage to the batteries. 2) The dynamic characteristics of the terrain the robot traverse requires rapid online modifications in its behaviour. The power required for driving a wheel is an increasing function of its slip ratio. For a wheeled robot moving for driving a wheel is an increasing function of its slip ratio. For a wheeled robot moving on different terrains, slip of the wheels should be checked and compensated for to keep the robot moving with less power consumption. To reduce the power consumption, the target robot moving with less power consumption. To reduce the power consumption, the target of the control system is to keep the slip ratio of the driving wheels around the desired value of the control system is to keep the slip ratio of the driving wheels around the desired value. To fulfill the above mentioned tasks, in this thesis, to increase model validity of lithium-ion battery in various charge/discharge scenarios during the mobile robot operation, the battery capacity fade and internal resistance change are modeled by adding them as state variables to a state space model. Using the output measured data, adaptive unscented Kalman Filter (AUKF) is employed for online model parameters identification of the equivalent circuit model at each sampling time. Subsequently, based on the updated model parameters, SoC estimation is conducted using AUKF. The effectiveness of the proposed method is verified through experiments under different power duties in the lab environment through experiments under different power duties in the lab environment. Better results are obtained both in battery model parameters estimation and the battery SoC estimation in comparison with other Kalman filter extensions. Furthermore, for effective control of the slip ratio, a model-based approach to estimating the longitudinal velocity of the mobile robot is presented. The AUKF is developed to estimate the vehicle longitudinal velocity and the wheel angular velocity using measurements from wheel encoders. Based on the estimated slip ratio, a sliding mode controller is designed for slip control of the uncertain nonlinear dynamical system in the presence of model uncertainties, parameter variations, and disturbances. Experiments are carried out in real time on a four-wheel mobile robot to verify the effectiveness of the estimation algorithm and the controller. It is shown that the controller is able to control the slip ratio of the mobile robot on different terrains while adaptive concept of AUKF leads to better results than the unscented Kalman filter in estimating the vehicle velocity which is difficult to measure in actual practice.

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.

scholarly journals Online Simultaneous State Estimation and Parameter Adaptation for Building Predictive Control

2013 ◽  
Author(s):  
Mehdi Maasoumy ◽  
Barzin Moridian ◽  
Meysam Razmara ◽  
Mahdi Shahbakhti ◽  
Alberto Sangiovanni-Vincentelli
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Convective Heat Transfer Coefficient ◽  
Model Parameters ◽  
Unknown Parameters ◽  
Modeling Framework ◽  
Parameter Adaptation ◽  
Building Model ◽  
Model Based ◽  
On Line

Model-based control of building energy offers an attractive way to minimize energy consumption in buildings. Model-based controllers require mathematical models that can accurately predict the behavior of the system. For buildings, specifically, these models are difficult to obtain due to highly time varying, and nonlinear nature of building dynamics. Also, model-based controllers often need information of all states, while not all the states of a building model are measurable. In addition, it is challenging to accurately estimate building model parameters (e.g. convective heat transfer coefficient of varying outside air). In this paper, we propose a modeling framework for “on-line estimation” of states and unknown parameters of buildings, leading to the Parameter-Adaptive Building (PAB) model. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) techniques are used to design the PAB model which simultaneously tunes the parameters of the model and provides an estimate for all states of the model. The proposed PAB model is tested against experimental data collected from Lakeshore Center building at Michigan Tech University. Our results indicate that the new framework can accurately predict states and parameters of the building thermal model.

scholarly journals Practical phase unwrapping of interferometric fringes based on unscented Kalman filter technique

2015 ◽  
Vol 23 (25) ◽  
pp. 32337 ◽  
Author(s):  
Zhongtao Cheng ◽  
Dong Liu ◽  
Yongying Yang ◽  
Tong Ling ◽  
Xiaoyu Chen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Phase Unwrapping ◽  
Filter Technique

scholarly journals Continuous Identification of Driver Model Parameters via the Unscented Kalman Filter

2019 ◽  
Vol 52 (28) ◽  
pp. 126-133 ◽  
Author(s):  
Yishen Zhao ◽  
Philippe Chevrel ◽  
Fabien Claveau ◽  
Franck Mars
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Driver Model ◽  
Model Parameters

scholarly journals SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization

2019 ◽  
Vol 9 (19) ◽  
pp. 4177
Author(s):  
Xiangwei Guo ◽  
Xiaozhuo Xu ◽  
Jiahao Geng ◽  
Xian Hua ◽  
Yan Gao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Equivalent Circuit ◽  
Parameter Optimization ◽  
Unscented Kalman Filter ◽  
Equivalent Circuit Model ◽  
Circuit Model ◽  
Second Order ◽  
Model Parameters ◽  
Soc Estimation ◽  
Adaptive Unscented Kalman Filter

State of charge (SOC) estimation is generally acknowledged to be one of the most important functions of the battery management system (BMS) and is thus widely studied in academia and industry. Based on an accurate SOC estimation, the BMS can optimize energy efficiency and protect the battery from being over-charged or over-discharged. The accurate online estimation of the SOC is studied in this paper. First, it is proved that the second-order resistance capacitance (RC) model is the most suitable equivalent circuit model compared with the Thevenin and multi-order models. The second-order RC equivalent circuit model is established, and the model parameters are identified. Second, the reasonable optimization of model parameters is studied, and a reasonable optimization method is proposed to improve the accuracy of SOC estimation. Finally, the SOC is estimated online based on the adaptive unscented Kalman filter (AUKF) with optimized model parameters, and the results are compared with the results of an estimation based on pre-optimization model parameters. Simulation experiments show that, without affecting the convergence of the initial error of the AUKF, the model after parameter optimization has a higher online SOC estimation accuracy.

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