scholarly journals An Estimation Algorithm of Extended Kalman Filter based on improved Thevenin Model for the management of Lithium Battery System

2019 ◽  
Vol 310 ◽  
pp. 032048
Author(s):  
Peng Li ◽  
Huan Chen
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Lithium Battery ◽  
Estimation Algorithm ◽  
Battery System

State of Charge Estimation Algorithm Based on Fractional Order Adaptive Extended Kalman Filter and Unscented Kalman Filter

2021 ◽  
pp. 1-32
Author(s):  
Weijie Liu ◽  
Hongliang Zhou ◽  
Zeqiang Tang ◽  
Tianxiang Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Fractional Order ◽  
Lithium Battery ◽  
Unscented Kalman Filter ◽  
Estimation Algorithm ◽  
Internal Resistance ◽  
Second Order ◽  
State Of Charge ◽  
Adaptive Extended Kalman Filter

Abstract Accurate estimation of battery state of charge (SOC) is the basis of battery management system. the fractional order theory is introduced into the second-order resistance-capacitance (RC)model of lithium battery and adaptive genetic algorithm is used to identify the parameters of the second-order RC model based on fractional order. Considering the changes of internal resistance and battery aging during battery discharge, the battery health state (SOH) is estimated based on unscented Kalman filter (UKF), and the values of internal resistance and maximum capacity of the battery are obtained. Finally, a novel estimation algorithm of lithium battery SOC based on SOH and fractional order adaptive extended Kalman filter (FOAEKF) is proposed. In order to verify the effectiveness of the proposed algorithm, an experimental system is set up and the proposed method is compared with the existing SOC estimation algorithms. The experimental results show that the proposed method has higher estimation accuracy, with the average error lower than 1% and the maximum error lower than 2%.

scholarly journals A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Heikki Hyyti ◽  
Arto Visala
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
Direction Cosine ◽  
Estimation Algorithm ◽  
Calibration Method ◽  
Attitude Estimation ◽  
Mems Sensors ◽  
Acceleration Sensors ◽  
Adaptive Extended Kalman Filter

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

scholarly journals A Fuzzy State-of-Charge Estimation Algorithm Combining Ampere-Hour and an Extended Kalman Filter for Li-Ion Batteries Based on Multi-Model Global Identification

2018 ◽  
Vol 8 (11) ◽  
pp. 2028 ◽  
Author(s):  
Xin Lai ◽  
Dongdong Qiao ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Algorithm ◽  
State Of Charge ◽  
Distribution Area ◽  
Model Parameters ◽  
Model Accuracy ◽  
Soc Estimation ◽  
Fuzzy Fusion ◽  
Fuzzy State

The popular and widely reported lithium-ion battery model is the equivalent circuit model (ECM). The suitable ECM structure and matched model parameters are equally important for the state-of-charge (SOC) estimation algorithm. This paper focuses on high-accuracy models and the estimation algorithm with high robustness and accuracy in practical application. Firstly, five ECMs and five parameter identification approaches are compared under the New European Driving Cycle (NEDC) working condition in the whole SOC area, and the most appropriate model structure and its parameters are determined to improve model accuracy. Based on this, a multi-model and multi-algorithm (MM-MA) method, considering the SOC distribution area, is proposed. The experimental results show that this method can effectively improve the model accuracy. Secondly, a fuzzy fusion SOC estimation algorithm, based on the extended Kalman filter (EKF) and ampere-hour counting (AH) method, is proposed. The fuzzy fusion algorithm takes advantage of the advantages of EKF, and AH avoids the weaknesses. Six case studies show that the SOC estimation result can hold the satisfactory accuracy even when large sensor and model errors exist.

Fault-tolerant pose and inertial parameters estimation of an uncooperative spacecraft based on dual vector quaternions

2018 ◽  
Vol 233 (4) ◽  
pp. 1250-1269 ◽  
Author(s):  
Jianping Yuan ◽  
Xianghao Hou ◽  
Chong Sun ◽  
Yu Cheng
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Formation Flying ◽  
Fault Tolerant ◽  
Estimation Algorithm ◽  
Parameters Estimation ◽  
Small Satellites ◽  
Dual Vector ◽  
Inertial Parameters ◽  
Space Target

Estimating the parameters of an unknown free-floating tumbling spacecraft is an essential task for the on-orbit servicing missions. This paper proposes a dual vector quaternion based fault-tolerant pose and inertial parameters estimation algorithm of an uncooperative space target using two formation flying small satellites. Firstly, by utilizing the dual vector quaternions to model the kinematics and dynamics of the system, not only the representation of the model is concise and compacted, but also the translational and rotational coupled effects are considered. By using this modeling technique along with the measurements from the on-board vision-based sensors, a dual vector quaternion based extended Kalman filter for each of the two small satellites is designed. Secondly, both of the estimations from each small satellite will be used as inputs of the fault-tolerant algorithm. This algorithm is based on the fault-tolerant federal extended Kalman filter strategy to overcome the estimation errors caused by the faulty measurements, the unknown space environment and the computing errors by setting the appropriate ratios of the two estimations from the first step dual vector quaternions extended Kalman filter. Together with the first and second steps, a novel fault-tolerant dual vector quaternions federal extended Kalman filter using two formation flying small satellites is proposed by this paper to estimate the pose and inertial parameters of a free-floating tumbling space target. By utilizing the estimation algorithm, a good prior knowledge of the unknown space target can be achieved. Finally, the proposed dual vector quaternion federal extended Kalman filter is validated by mathematical simulations to show its robust performances.

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.

Target Tracking with Two Passive Infrared Sensors Augmented with Irradiance Ratio

2013 ◽  
Vol 427-429 ◽  
pp. 810-815
Author(s):  
Wei Hong ◽  
Ye Hu
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Extended Kalman Filter ◽  
Estimation Algorithm ◽  
Joint Estimation ◽  
Filter Method ◽  
Atmospheric Parameter ◽  
Infrared Sensors ◽  
Cluttered Environment ◽  
Passive Infrared

In target tracking using the passive infrared sensors, the principle of triangulation distance measurement is normally used as the basic method. However, when the target directions are nearly collinear relative to the baseline, this method merely based on EKF and angle measurements produces poor results. To solve this problem, we propose a target tracking solution based on dual infrared sensors in the cluttered environment. This method is a joint estimation algorithm of target motion state and atmospheric parameter such as the extinction coefficient. The method combines the probability data association algorithm with the augmented extended Kalman filter algorithm, into which we introduce the rate of infrared energy absorbed by the sensors at the ends of the baseline as additional measurement vector. Simulation results show that the proposed method performs better than the standard extended Kalman filter method, even in the case that the targets position is near the baseline in the cluttered environment.

scholarly journals Irnss Orbit and Clock Bias Estimation using Navic Ground Receiver Data: Extended Kalman Filter

2019 ◽  
Vol 9 (2) ◽  
pp. 713-717
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Orbit Determination ◽  
Sequential Estimation ◽  
Estimation Algorithm ◽  
Time Instant ◽  
Tropospheric Delay ◽  
Bias Estimation ◽  
Satellite Clock ◽  
Clock Error

The aim of this work is to precisely estimate the IRNSS satellite’s orbit and clock errors using NavIC receiver data. Orbit determination is required to precisely calculate the user/receiver position on the Earth. In this study, Bengaluru, Surat, Kolkata, and Hyderabad’s NavIC ground receivers’ data is considered for orbit estimation. The pseudo-range measurements received by the ground receivers have multiple errors added due to ionospheric delay, tropospheric delay, multipath delays, satellite clock errors, and some unmodeled effects. But, the major factor accounting for errors is the satellite clock error. Hence, along with position and velocity of the satellite, even the clock correction is estimated using Extended Kalman Filter (EKF). EKF is a sequential estimation algorithm which estimates satellite position, velocity and clock error at each time instant. In this paper, results of all seven IRNSS satellite’s orbit determination are discussed.

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