Multirate strong tracking extended Kalman filter and its implementation on lithium iron phosphate (LiFePO4) battery system

2015 ◽  
Author(s):  
J. Jia ◽  
P. Lin ◽  
C. S. Chin ◽  
W. D. Toh ◽  
Z. Gao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Lithium Iron Phosphate ◽  
Iron Phosphate ◽  
Battery System ◽  
Lifepo4 Battery

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.

scholarly journals Lithium Iron Phosphate (LiFePO4) Battery Power System for Deepwater Emergency Operation

2017 ◽  
Vol 143 ◽  
pp. 348-353 ◽  
Author(s):  
W.D. Toh ◽  
B. Xu ◽  
J. Jia ◽  
C.S. Chin ◽  
J. Chiew ◽  
...  
Keyword(s):  
Power System ◽  
Lithium Iron Phosphate ◽  
Emergency Operation ◽  
Iron Phosphate ◽  
Battery Power ◽  
Lifepo4 Battery

A One-class Classifier Based on a Hybrid Topology to Detect Faults in Power Cells

2021 ◽  
Author(s):  
Esteban Jove ◽  
José-Luis Casteleiro-Roca ◽  
Héctor Quintián ◽  
Francisco Zayas-Gato ◽  
Gianni Vercelli ◽  
...  
Keyword(s):  
Lithium Iron Phosphate ◽  
Real Data ◽  
Iron Phosphate ◽  
Hybrid Classifier ◽  
Hybrid Topology ◽  
General Terms ◽  
Real Value ◽  
One Class Classifier ◽  
Lifepo4 Battery ◽  
Operating Points

Abstract The use of batteries became essential in our daily life in electronic devices, electric vehicles and energy storage systems in general terms. As they play a key role in many devices, their design and implementation must follow a thorough test process to check their features at different operating points. In this circumstance, the appearance of any kind of deviation from the expected operation must be detected. This research deals with real data registered during the testing phase of a lithium iron phosphate—LiFePO4—battery. The process is divided into four different working points, alternating charging, discharging and resting periods. This work proposes a hybrid classifier, based on one-class techniques, whose aim is to detect anomalous situations during the battery test. The faults are created by modifying the measured cell temperature a slight ratio from their real value. A detailed analysis of each technique performance is presented. The average performance of the chosen classifier presents successful results.

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.

scholarly journals An Accurate State of Charge Estimation Method for Lithium Iron Phosphate Battery Using a Combination of an Unscented Kalman Filter and a Particle Filter

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4536 ◽  
Author(s):  
Thanh-Tung Nguyen ◽  
Abdul Basit Khan ◽  
Younghwi Ko ◽  
Woojin Choi
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Lithium Iron Phosphate ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Iron Phosphate ◽  
State Of Charge ◽  
Superior Performance ◽  
Accurate Estimation ◽  
Soc Estimation

An accurate state of charge (SOC) estimation of the battery is one of the most important techniques in battery-based power systems, such as electric vehicles (EVs) and energy storage systems (ESSs). The Kalman filter is a preferred algorithm in estimating the SOC of the battery due to the capability of including the time-varying coefficients in the model and its superior performance in the SOC estimation. However, since its performance highly depends on the measurement noise (MN) and process noise (PN) values, it is difficult to obtain highly accurate estimation results with the battery having a flat plateau OCV (open-circuit voltage) area in the SOC-OCV curve, such as the Lithium iron phosphate battery. In this paper, a new integrated estimation method is proposed by combining an unscented Kalman filter and a particle filter (UKF-PF) to estimate the SOC of the Lithium iron phosphate battery. The equivalent circuit of the battery used is composed of a series resistor and two R-C parallel circuits. Then, it is modeled by a second-order autoregressive exogenous (ARX) model, and the parameters are identified by using the recursive least square (RLS) identification method. The validity of the proposed algorithm is verified by comparing the experimental results obtained with the proposed method and the conventional methods.

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