The State of Charge of Electrodes and Its Effects on Lithium-Iron-Phosphate Battery Simulation

2016 ◽  
Vol 72 (11) ◽  
pp. 1-10 ◽  
Author(s):  
H.-H. Huang ◽  
W.-L. Chung ◽  
K.-C. Liao ◽  
C.-F. Lee ◽  
J.-Y. Tien ◽  
...  
Keyword(s):  
Lithium Iron Phosphate ◽  
Iron Phosphate ◽  
The State ◽  
State Of Charge

scholarly journals Impedance spectra classification for determining the state of charge on a lithium iron phosphate cell using a support vector machine

2015 ◽  
Vol 13 ◽  
pp. 127-132 ◽  
Author(s):  
P. Jansen ◽  
D. Vergossen ◽  
D. Renner ◽  
W. John ◽  
J. Götze
Keyword(s):  
Support Vector Machine ◽  
Kalman Filter ◽  
Lithium Iron Phosphate ◽  
Iron Phosphate ◽  
The State ◽  
State Of Charge ◽  
Divide And Conquer ◽  
Binary Search Tree ◽  
Support Vector ◽  
Impedance Spectra

Abstract. An alternative method for determining the state of charge (SOC) on lithium iron phosphate cells by impedance spectra classification is given. Methods based on the electric equivalent circuit diagram (ECD), such as the Kalman Filter, the extended Kalman Filter and the state space observer, for instance, have reached their limits for this cell chemistry. The new method resigns on the open circuit voltage curve and the parameters for the electric ECD. Impedance spectra classification is implemented by a Support Vector Machine (SVM). The classes for the SVM-algorithm are represented by all the impedance spectra that correspond to the SOC (the SOC classes) for defined temperature and aging states. A divide and conquer based search algorithm on a binary search tree makes it possible to grade measured impedances using the SVM method. Statistical analysis is used to verify the concept by grading every single impedance from each impedance spectrum corresponding to the SOC by class with different magnitudes of charged error.

Lithium Iron Phosphate Power Battery State of Charge Estimation Based on the Extended Kalman Filter

2013 ◽  
Vol 336-338 ◽  
pp. 784-788
Author(s):  
Ming Li ◽  
Yang Jiang ◽  
Jian Zhong Zheng ◽  
Xiao Xiao Peng
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Lithium Iron Phosphate ◽  
Equivalent Circuit Model ◽  
Estimation Algorithm ◽  
Iron Phosphate ◽  
Open Circuit ◽  
The State ◽  
State Of Charge ◽  
Power Battery

In order to estimate the state of charge (SOC) of lithium iron phosphate (LiFePO4) power battery, the state space model that fit for kalman filter to estimate was established on the basis of PNGV equivalent circuit model. In the case that considering the influence factors such as power battery charge and discharge current, environmental temperature and battery state of health, an improved composite SOC estimation algorithm based on extended kalman filter (EKF) algorithm was proposed, this proposed algorithm integrated using EKF algorithm, improved Ah counting method and open circuit voltage method to estimate SOC. The simulation results show that the proposed algorithm can track the change of the power battery SOC effectively, verify the validity of the proposed algorithm.

Particle filter for state of charge and state of health estimation for lithium–iron phosphate batteries

2013 ◽  
Vol 239 ◽  
pp. 705-710 ◽  
Author(s):  
Simon Schwunk ◽  
Nils Armbruster ◽  
Sebastian Straub ◽  
Johannes Kehl ◽  
Matthias Vetter
Keyword(s):  
Particle Filter ◽  
Lithium Iron Phosphate ◽  
Iron Phosphate ◽  
State Of Charge ◽  
State Of Health

Improved Method for State of Charge Estimation of Lithium Iron Phosphate Power Batteries

2013 ◽  
Vol 712-715 ◽  
pp. 1956-1959 ◽  
Author(s):  
Ming Li ◽  
Yang Jiang ◽  
Jian Zhong Zheng ◽  
Xiao Xiao Peng
Keyword(s):  
Lithium Iron Phosphate ◽  
Estimation Method ◽  
Iron Phosphate ◽  
Open Circuit ◽  
State Of Charge ◽  
Counting Method ◽  
Initial State ◽  
Soc Estimation ◽  
Power Battery ◽  
Environment Temperature

To resolve the problems that the initial state of charge (SOC) and the available capacity of batteries are difficult to estimate when using the Ah counting method, in this paper An improved SOC estimation method was proposed that combined with the open circuit voltage (OCV) method and Ah counting method based on the analysis and consideration of the battery available capacity variation caused by charge and discharge current, environment temperature and battery state of health (SOH). The precision of the proposed method was validated by using Federal Urban Driving Schedule (FUDS) test of a Lithium iron phosphate (LiFePO4) power battery. The SOC estimate error using the proposed method relative to a discharge test was better than the Ah counting method.

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