Development of the state of charge meter for Li/MnO/sub 2/ cells/batteries

2003 ◽  
Author(s):  
A. Nimberger ◽  
R. Lupu ◽  
S. Levi ◽  
E. Laufer ◽  
P. Klein ◽  
...  
Keyword(s):  
The State ◽  
State Of Charge

A new method of assessing the state of charge of implanted cardiac pacemaker batteries

1965 ◽  
Vol 16 (5) ◽  
pp. 717-721 ◽  
Author(s):  
C.Walton Lillehei ◽  
Anatolio B. Cruz ◽  
Irwin Johnsrude ◽  
Robert D. Sellers
Keyword(s):  
Cardiac Pacemaker ◽  
The State ◽  
State Of Charge ◽  
New Method

Monitoring the State-of-Charge in All-Iron Aqueous Redox Flow Batteries

2018 ◽  
Vol 165 (13) ◽  
pp. A3164-A3168 ◽  
Author(s):  
Noemí Aguiló-Aguayo ◽  
Thomas Bechtold
Keyword(s):  
The State ◽  
State Of Charge ◽  
Redox Flow Batteries ◽  
Flow Batteries

Enabling smart control by optimally managing the State of Charge of district heating networks

2020 ◽  
pp. 116286
Author(s):  
Costanza Saletti ◽  
Nathan Zimmerman ◽  
Mirko Morini ◽  
Konstantinos Kyprianidis ◽  
Agostino Gambarotta
Keyword(s):  
District Heating ◽  
The State ◽  
State Of Charge ◽  
Smart Control ◽  
Heating Networks

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.

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