Online estimation of model parameters and state-of-charge of Lithium-Ion battery using Unscented Kalman Filter

2012 ◽  
Author(s):  
M. Partovibakhsh ◽  
Guangjun Liu
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Model Parameters ◽  
Online Estimation ◽  
Estimation Of Model Parameters

scholarly journals Unscented Kalman Filter based State of Charge Estimation for the Equalization of Lithium-ion Batteries on Electrical Vehicles

2019 ◽  
Vol 9 (6) ◽  
pp. 4876-4882
Author(s):  
Y. Muratoglu ◽  
A. Alkaya
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Unscented Kalman Filter ◽  
Balance Control ◽  
Lithium Ion ◽  
State Of Charge ◽  
Model Parameters ◽  
Battery Model ◽  
State Of Charge Estimation

Accurate state of charge estimation and robust cell equalization are vital in optimizing the battery management system and improving energy management in electric vehicles. In this paper, the passive balance control based equalization scheme is proposed using a combined dynamic battery model and the unscented Kalman filter based state of charge estimation. The lithium-ion battery is modeled with a 2nd order Thevenin equivalent circuit. The combined dynamic model of the lithium-ion battery, where the model parameters are estimated depending on the state of charge, and the unscented Kalman filter based state of charge, are used to improve the performance of the passive balance control based equalization. The experimental results verified the superiority of the combined dynamic battery model and the unscented Kalman filter algorithm with very tight error bounds. Furthermore, these results showed that the presented passive balance control based equalization scheme is suitable for the equalization of series-connected lithium-ion batteries.

scholarly journals Comparison of Nonlinear Filtering Methods for Estimating the State of Charge of Li4Ti5O12Lithium-Ion Battery

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Jianping Gao ◽  
Hongwen He
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
Parametric Modeling ◽  
State Of Charge ◽  
Battery Management System ◽  
Extend Kalman Filter ◽  
Soc Estimation ◽  
Model Based

Accurate state of charge (SoC) estimation is of great significance for the lithium-ion battery to ensure its safety operation and to prevent it from overcharging or overdischarging. To achieve reliable SoC estimation for Li4Ti5O12lithium-ion battery cell, three filtering methods have been compared and evaluated. A main contribution of this study is that a general three-step model-based battery SoC estimation scheme has been proposed. It includes the processes of battery data measurement, parametric modeling, and model-based SoC estimation. With the proposed general scheme, multiple types of model-based SoC estimators have been developed and evaluated for battery management system application. The detailed comparisons on three advanced adaptive filter techniques, which include extend Kalman filter, unscented Kalman filter, and adaptive extend Kalman filter (AEKF), have been implemented with a Li4Ti5O12lithium-ion battery. The experimental results indicate that the proposed model-based SoC estimation approach with AEKF algorithm, which uses the covariance matching technique, performs well with good accuracy and robustness; the mean absolute error of the SoC estimation is within 1% especially with big SoC initial error.

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