A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.336-338.784 ◽

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.

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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 ◽

10.3390/en13174536 ◽

2020 ◽

Vol 13 (17) ◽

pp. 4536 ◽

Cited By ~ 1

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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The Research of Power Battery SOC Estimation Based on Adaptive Kalman Filter Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.433-435.754 ◽

2013 ◽

Vol 433-435 ◽

pp. 754-759

Author(s):

Wei Bo Yu ◽

Ting Ting Yang ◽

Cui Yuan Feng ◽

Hong Jun Li

Keyword(s):

Kalman Filter ◽

Lithium Iron Phosphate ◽

Pulse Discharge ◽

Iron Phosphate ◽

Recursive Least Squares ◽

Model Parameters ◽

Adaptive Kalman Filter ◽

Soc Estimation ◽

Power Battery ◽

Kalman Filter Algorithm

Taking the lithium iron phosphate power battery as the research object, through analysis on characteristics of the battery, this paper chooses the improved second-order RC model as the model of battery whose complexity is moderate and it can better reflect the battery dynamic and static characteristics. Then by pulse discharge experiments and with improved recursive least squares algorithm to identify model parameters online, and puts forward up the adaptive kalman filtering algorithm to estimate battery SOC. The results show that the adaptive kalman filter algorithm can effectively improve battery SOC estimation precision.

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