scholarly journals State Estimation of Lithium Batteries for Energy Storage Based on Dual Extended Kalman Filter

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Xinming Xu ◽  
Di Wu ◽  
Lei Yang ◽  
Huai Zhang ◽  
Guangjun Liu
Keyword(s):  
Kalman Filter ◽  
Energy Storage ◽  
Extended Kalman Filter ◽  
Lithium Battery ◽  
Simulation Analysis ◽  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Open Loop ◽  
Estimation Accuracy ◽  
Battery Management System

In general, battery packs are monitored by the battery management system (BMS) to ensure the efficiency and reliability of the energy storage system. SOC and SOH represent the battery’s energy and lifetime, respectively. They are the core aspects of the battery BMS. The traditional method assumes that the SOC is determined by the integral of the current input and output from the battery over time, which is an open-loop-based approach and often accompanies by poor estimation accuracy and the accumulation of sensor errors. The contribution of this work is to establish a new equivalent circuit model based on the lithium battery external characteristic, and the battery parameters are identified by considering the influence of capacity fade, voltage rebound, and internal capacitance-resistance performance. The correlation between the ohmic internal resistance and real capacity is obtained by degradation test. Then, the dual extended Kalman filter (DEKF) is used to perform real-time prediction of the lithium battery state. And through the simulation analysis and experiments, the feasibility and precision of the estimation method are well proved.

A New Friction Estimation Approach for Precision Positioner

2007 ◽  
Author(s):  
Wen Wang ◽  
Xinxin Li ◽  
Zichen Chen
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Taylor Series ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Low Velocity ◽  
Friction Estimation ◽  
Simulation Results ◽  
Long Stroke

Precision positioner has been significantly developed as the rapid growth of MEMS and IC industries. As for short-stroke position, the loss of friction can be avoided by using flexible hinges. Long-stroke postioner, however, in which moved-to-be mass always stands on the guide-way part, a main source of friction, makes friction unavoidable. Friction estimation is based on certain filters, such as Extended Kalman filter (EKF). However, estimation accuracy of Kalman filter, especially at low-velocity movement, is not very well. To solve this problem, the paper proposes an estimation method based on DD2 to make an accurate estimation. And the result shows this method is promising in real-time friction estimation. After background introduction, in section 2, the relation of EKF and Taylor series and EKF implementation are reviewed and its limitations are noted as well. A briefly introduction to DD2 is given in Section 3 and friction estimation case comparing the simulation results of DD2 estimation with that of EKF described in Section 4, respectively. At last, conclusions are summarized.

scholarly journals Improvement of Torque Estimation for Series Viscoelastic Actuator Based on Dual Extended Kalman Filter

Actuators ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 258
Author(s):  
Hui Wei ◽  
Kui Xiang ◽  
Haibo Chen ◽  
Biwei Tang ◽  
Muye Pang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Viscoelastic Model ◽  
Estimation Method ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Motor Current ◽  
Output Torque ◽  
Torque Estimation ◽  
Viscoelastic Element

Adding damping such as viscoelastic element in series elastic actuators (SEA) can improve the force control bandwidth of the system and suppression of high frequency oscillations induced by the environment. Thanks to such advantages, series viscoelastic actuators (SVA) have recently gained increasing research interests from the community of robotic device design. Due to the inconvenience of mounting torque sensors, employing the viscoelastic elements to directly estimate the output torque is of great significance regarding the real-world applications of SVA. However, the nonlinearity and time-varying properties of viscoelastic materials would degrade the torque estimation accuracy. In such a case, it is paramount to simultaneously estimate the output torque state and viscoelastic model coefficients in order to enhance the torque estimation accuracy. To this end, this paper first completed the design of a rubber-based SVA device and used the Zenner linear viscoelastic model to model the viscoelastic element of the rubber. Subsequently, this paper proposed a dual extended Kalman filter- (DEFK) based torque estimation method to estimate the output torque and viscoelastic model coefficients simultaneously. The noisy observations of two Kalman filters were provided by motor current-based estimated torque. Moreover, the dynamic friction of harmonic drive of the designed SVA was modeled and compensated to enhance the reliability of current-based torque estimation. Finally, a number of experiments were carried out on SVA, and the experimental results confirmed the DEFK effectiveness of improving torque estimation accuracy compared to only-used rubber and only-used motor current torque estimation methods. Thus, the proposed method could be considered as an effective alternative approach of torque estimation for SVA.

Fast Kalman SLAM

2010 ◽  
Vol 44-47 ◽  
pp. 3174-3179
Author(s):  
Wu Zhou ◽  
Chun Xia Zhao ◽  
Mian Hao Zhang
Keyword(s):  
Kalman Filter ◽  
Computational Complexity ◽  
Extended Kalman Filter ◽  
Simulation Analysis ◽  
Unscented Kalman Filter ◽  
Estimation Accuracy ◽  
Map Building ◽  
Complex Environments ◽  
Path Component ◽  
High Computational Complexity

When Simultaneous Localization and Map Building is carried out in complex environments, reduction of computational complexity is a key problem. With a view to the high computational complexity of particle filter, a SLAM solution named ‘Fast Kalman SLAM’ is introduced. Adopting the ‘decomposition’ idea in the FastSLAM algorithm, Fast Kalman SLAM factors the joint SLAM state into a path component and a conditional map component. The robot pose is estimated recursively with Mean Extended Kalman Filter (MEKF) or Unscented Kalman Filter (UKF), while the map with Extended Kalman Filter (EKF). Simulative experiments are carried out to evaluate the performance of the presented algorithm. And Simulation analysis is made for the presented algorithm. The experimental results indicate that the new algorithm reduces computational complexity greatly and ensures estimation accuracy at the same time.

Research on Active Equalization of Lithium Battery via SOC

2014 ◽  
Vol 556-562 ◽  
pp. 2013-2016
Author(s):  
Yun Gan Wang ◽  
Zhong Feng Wang ◽  
J.L. Huang ◽  
Li Gang Li
Keyword(s):  
Kalman Filter ◽  
Equivalent Circuit ◽  
Extended Kalman Filter ◽  
Lithium Battery ◽  
Equivalent Circuit Model ◽  
High Accuracy ◽  
Battery Pack ◽  
Low Loss ◽  
Adaptive Parameters ◽  
Kalman Filter Algorithm

This paper presents an active equalization method for lithium battery via SOC. The high accuracy of SOC is promised by extended Kalman filter algorithm (EKF) based on the adaptive parameters equivalent-circuit model at all SOC region. Then a small size, low loss resonant soft-switching active equalization circuit is presented. That circuit combined with SOC do precisely balance the battery pack during charge and discharge processes, which can effectively improve the battery pack’s using life.

scholarly journals A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3809 ◽  
Author(s):  
Yushi Hao ◽  
Aigong Xu ◽  
Xin Sui ◽  
Yulei Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Measurement Noise ◽  
Model Errors ◽  
State Process ◽  
Stability And Accuracy ◽  
The Impact

Recently, the integration of an inertial navigation system (INS) and the Global Positioning System (GPS) with a two-antenna GPS receiver has been suggested to improve the stability and accuracy in harsh environments. As is well known, the statistics of state process noise and measurement noise are critical factors to avoid numerical problems and obtain stable and accurate estimates. In this paper, a modified extended Kalman filter (EKF) is proposed by properly adapting the statistics of state process and observation noises through the innovation-based adaptive estimation (IAE) method. The impact of innovation perturbation produced by measurement outliers is found to account for positive feedback and numerical issues. Measurement noise covariance is updated based on a remodification algorithm according to measurement reliability specifications. An experimental field test was performed to demonstrate the robustness of the proposed state estimation method against dynamic model errors and measurement outliers.

scholarly journals Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations

2016 ◽  
Vol 2016 ◽  
pp. 1-24 ◽  
Author(s):  
Romy Budhi Widodo ◽  
Chikamune Wada
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Compensation Model ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
The One ◽  
The Right ◽  
External Acceleration

Attitude estimation is often inaccurate during highly dynamic motion due to the external acceleration. This paper proposes extended Kalman filter-based attitude estimation using a new algorithm to overcome the external acceleration. This algorithm is based on an external acceleration compensation model to be used as a modifying parameter in adjusting the measurement noise covariance matrix of the extended Kalman filter. The experiment was conducted to verify the estimation accuracy, that is, one-axis and multiple axes sensor movement. Five approaches were used to test the estimation of the attitude: (1) the KF-based model without compensating for external acceleration, (2) the proposed KF-based model which employs the external acceleration compensation model, (3) the two-step KF using weighted-based switching approach, (4) the KF-based model which uses thethreshold-basedapproach, and (5) the KF-based model which uses the threshold-based approach combined with a softened part approach. The proposed algorithm showed high effectiveness during the one-axis test. When the testing conditions employed multiple axes, the estimation accuracy increased using the proposed approach and exhibited external acceleration rejection at the right timing. The proposed algorithm has fewer parameters that need to be set at the expense of the sharpness of signal edge transition.

scholarly journals Estimation of lithium-ion battery state-of-charge using an extended kalman filter

2021 ◽  
Vol 10 (4) ◽  
pp. 1759-1768
Author(s):  
Mouhssine Lagraoui ◽  
Ali Nejmi ◽  
Hassan Rayhane ◽  
Abderrahim Taouni
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
The State ◽  
Operating Conditions ◽  
State Of Charge ◽  
Ohmic Loss ◽  
Battery Management System ◽  
Transfer Resistance

The main goal of a battery management system (BMS) is to estimate parameters descriptive of the battery pack operating conditions in real-time. One of the most critical aspects of BMS systems is estimating the battery's state of charge (SOC). However, in the case of a lithium-ion battery, it is not easy to provide an accurate estimate of the state of charge. In the present paper we propose a mechanism based on an extended kalman filter (EKF) to improve the state-of-charge estimation accuracy on lithium-ion cells. The paper covers the cell modeling and the system parameters identification requirements, the experimental tests, and results analysis. We first established a mathematical model representing the dynamics of a cell. We adopted a model that comprehends terms that describe the dynamic parameters like SOC, open-circuit voltage, transfer resistance, ohmic loss, diffusion capacitance, and resistance. Then, we performed the appropriate battery discharge tests to identify the parameters of the model. Finally, the EKF filter applied to the cell test data has shown high precision in SOC estimation, even in a noisy system.

scholarly journals Analysis of the Sensitivity of Extended Kalman Filter-Based Inertia Estimation Method to the Assumed Time of Disturbance

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 483 ◽  
Author(s):  
Davide del Giudice ◽  
Samuele Grillo
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Situational Awareness ◽  
Renewable Energy Sources ◽  
Estimation Method ◽  
Electric Power System ◽  
Estimation Methods ◽  
Frequency Regulation ◽  
Time Required ◽  
Primary Frequency

The frequency behavior of an electric power system right after a power imbalance is determined by its inertia constant. The current shift in generation mix towards renewable energy sources is leading to a smaller and more variable inertia, thereby compromising the frequency stability of modern grids. Therefore, real-time inertia estimation methods would be beneficial for grid operators, as their situational awareness would be enhanced. This paper focuses on an inertia estimation method specifically tailored for synchronous generators, based on the extended Kalman filter (EKF). Such a method should be started at the time of disturbance, which must be estimated accurately, otherwise additional errors could be introduced in the inertia estimation process. In this paper, the sensitivity of the EKF-based inertia estimation method to the assumed time of disturbance is analyzed. It is shown that such sensitivity is influenced by the initially assumed inertia constant, the use time of the filter and by the time required for primary frequency regulation to be activated.

scholarly journals Influence of Sampling Delay on the Estimation of Lithium-Ion Battery Parameters and an Optimized Estimation Method

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1878
Author(s):  
Bing Jiang ◽  
Zeqi Chen ◽  
Feifan Chen
Keyword(s):  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Test System ◽  
Recursive Least Squares ◽  
Battery Management System ◽  
Battery Management ◽  
First Order ◽  
The Mathematical Model ◽  
Synchronization Accuracy

The equivalent-circuit model (ECM) is widely used in online estimating the parameters and states of lithium-ion batteries. However, the sampling delay between the voltage and current of a battery is generally overlooked, which is unavoidable in a modular battery management system (BMS) and would lead to wrong results in the estimation of battery parameters and states. In this paper, with the first-order resistor–capacitor (RC) model as our battery model, we analyze the influence mechanism of sampling delay and then propose an optimized method for online estimating battery parameters. The mathematical model derived from the first-order RC model and the approximation method of first-order derivative are optimized. The recursive least squares (RLS) algorithm is used for identifying the parameters of the model. In order to verify the proposed method, a modular battery test system with high sampling frequency and high synchronization accuracy is developed. The experiment results indicate that the sampling delay would cause the estimation process to fluctuate, and the optimized method effectively improves the tolerance range of sampling delay.

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