scholarly journals A Novel Dual Correction Extended Kalman Filtering Algorithm for The State of Charge Real-Time Estimation of Packing Lithium-Ion Batteries

2020 ◽  
pp. 12706-12723
Author(s):  
HaoTian Shi ◽  
Keyword(s):  
Lithium Ion Batteries ◽  
Real Time ◽  
Kalman Filtering ◽  
Lithium Ion ◽  
Time Estimation ◽  
The State ◽  
State Of Charge ◽  
Extended Kalman Filtering ◽  
Filtering Algorithm ◽  
Real Time Estimation

Real-time estimation of state-of-charge in lithium-ion batteries using improved central difference transform method

2020 ◽  
Vol 252 ◽  
pp. 119787 ◽  
Author(s):  
Dong-Ji Xuan ◽  
Zhuangfei Shi ◽  
Jinzhou Chen ◽  
Chenyang Zhang ◽  
Ya-Xiong Wang
Keyword(s):  
Lithium Ion Batteries ◽  
Real Time ◽  
Lithium Ion ◽  
Time Estimation ◽  
State Of Charge ◽  
Central Difference ◽  
Transform Method ◽  
Real Time Estimation

A novel voltage matching-adaptive extended Kalman filtering construction method for the state of charge prediction of lithium-ion batteries

2021 ◽  
pp. 014233122110294
Author(s):  
Weihao Shi ◽  
Shunli Wang ◽  
Lili Xia ◽  
Peng Yu ◽  
Bowen Li
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Kalman Filtering ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Circuit Model ◽  
The State ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Extended Kalman Filtering

Accurately estimating the state of charge of lithium-ion batteries is of great significance to the development of the new energy industry. This research proposes a method for estimating the state of charge of lithium-ion batteries based on a voltage matching-adaptive extended Kalman filtering algorithm. The voltage matching part and the first-order resistance-capacitance (RC) part is combined into a new equivalent circuit model. This model improves the accuracy of voltage simulation at different charging and discharging stages through segment matching. Model-based adaptive extended Kalman filter algorithm adds a noise correction factor to adaptively correct the influence of noise on the estimation process and improve the estimation accuracy. The forgetting factor is introduced to improve the real-time performance of the algorithm. To verify the reliability of the model and algorithm, a multi-condition experiment is carried out on the lithium-ion battery. The verification results show that the simulation error of the circuit model to the working state of the lithium-ion battery is less than 0.0487V. The improved algorithm can accurately estimate the state of charge of lithium-ion batteries, the estimation accuracy of the discharge stage is 98.34%, and the estimation accuracy of the charging stage is 97.75%.

Configuration Estimation of Gough-Stewart Platforms Using Extended Kalman Filtering

2000 ◽  
Author(s):  
Ernest D. Fasse ◽  
Albert J. Wavering
Keyword(s):  
Real Time ◽  
Kalman Filtering ◽  
Time Estimation ◽  
Stewart Platform ◽  
Attitude Estimation ◽  
Extended Kalman Filtering ◽  
Filtering Algorithms ◽  
Satellite Attitude ◽  
Deterministic Methods ◽  
Real Time Estimation

Abstract This paper develops extended Kalman filtering algorithms for a generic Gough-Stewart platform assuming realistically available sensors such as length sensors, rate gyroscopes, and accelerometers. The basic idea is to extend existing methods for satellite attitude estimation. The nondeterministic methods are meant to be a practical alternative to existing iterative, deterministic methods for real-time estimation of platform configuration.

State of Charge Estimation of Lithium-Ion Batteries by Extended Kalman Filtering

2012 ◽  
Vol 8 (1) ◽  
pp. 332-336
Author(s):  
Kuo-Chang Han ◽  
Van-Tsai Liu ◽  
Edward Chiu ◽  
Yi Huis Lin
Keyword(s):  
Lithium Ion Batteries ◽  
Kalman Filtering ◽  
Lithium Ion ◽  
State Of Charge ◽  
Extended Kalman Filtering ◽  
State Of Charge Estimation
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