Online Estimation of the State of Charge of a Lithium Ion Cell

2006 ◽  
Keyword(s):  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Online Estimation ◽  
Lithium Ion Cell

Online Estimation of the State of Charge of a Lithium Ion Cell

2019 ◽  
Vol 3 (27) ◽  
pp. 191-208
Author(s):  
Shriram Santhanagopalan ◽  
Ralph E. White
Keyword(s):  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Online Estimation ◽  
Lithium Ion Cell

Online estimation of the state of charge of a lithium ion cell

2006 ◽  
Vol 161 (2) ◽  
pp. 1346-1355 ◽  
Author(s):  
Shriram Santhanagopalan ◽  
Ralph E. White
Keyword(s):  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Online Estimation ◽  
Lithium Ion Cell

Battery cell modeling and online estimation of the state of charge of a lithium-ion battery

2018 ◽  
Vol 41 (5) ◽  
pp. 412-418 ◽  
Author(s):  
I-Haur Tsai ◽  
Kuan-Hsun Yu ◽  
Alexander Tseng ◽  
Jia-Yush Yen ◽  
Tseng-Ti Fu ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Battery Cell ◽  
Online Estimation ◽  
Cell Modeling

scholarly journals A New Predictive Model for the State-of-Charge of a High-Power Lithium-Ion Cell Based on a PSO-Optimized Multivariate Adaptive Regression Spline Approach

2016 ◽  
Vol 65 (6) ◽  
pp. 4197-4208 ◽  
Author(s):  
Juan Carlos Alvarez Anton ◽  
Paulino J. Garcia Nieto ◽  
Esperanza Garcia Gonzalo ◽  
Juan Carlos Viera Perez ◽  
Manuela Gonzalez Vega ◽  
...  
Keyword(s):  
Predictive Model ◽  
High Power ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Multivariate Adaptive Regression Spline ◽  
Regression Spline ◽  
Lithium Ion Cell ◽  
Adaptive Regression

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.

Accuracy analysis of the State-of-Charge and remaining run-time determination for lithium-ion batteries

Measurement ◽  
2009 ◽  
Vol 42 (8) ◽  
pp. 1131-1138 ◽  
Author(s):  
V. Pop ◽  
H.J. Bergveld ◽  
P.H.L. Notten ◽  
J.H.G. Op het Veld ◽  
P.P.L. Regtien
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Accuracy Analysis ◽  
Time Determination ◽  
Run Time

The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer

2014 ◽  
Vol 63 (4) ◽  
pp. 1614-1621 ◽  
Author(s):  
Jun Xu ◽  
Chunting Chris Mi ◽  
Binggang Cao ◽  
Junjun Deng ◽  
Zheng Chen ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Proportional Integral ◽  
State Of Charge Estimation
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