Power battery state of charge estimation based on extended Kalman filter

2019 ◽  
Vol 11 (1) ◽  
pp. 014302 ◽  
Author(s):  
Qi Wang ◽  
Xiaoyi Feng ◽  
Bo Zhang ◽  
Tian Gao ◽  
Yan Yang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
State Of Charge ◽  
Power Battery ◽  
Battery State Of Charge ◽  
State Of Charge Estimation

scholarly journals Battery state-of-charge estimation using fractional extended Kalman filter with Mittag-Leffler memory

2020 ◽  
Vol 59 (4) ◽  
pp. 1919-1929 ◽  
Author(s):  
J.I. Hidalgo-Reyes ◽  
J.F. Gómez-Aguilar ◽  
V.M. Alvarado-Martínez ◽  
M.G. López-López ◽  
R.F. Escobar-Jiménez
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
State Of Charge ◽  
Battery State Of Charge ◽  
State Of Charge Estimation

State of Charge Estimation of Lithium-Ion Battery Using Extended Kalman Filter Based on a Comprehensive Model

2014 ◽  
Vol 496-500 ◽  
pp. 999-1002
Author(s):  
Hao Li ◽  
Sheng Yong Liu ◽  
Yue Yu
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Extended Kalman Filter ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
State Of Charge ◽  
Power Characteristic ◽  
Battery System ◽  
Power Battery ◽  
State Of Charge Estimation

The state of charge (SOC) is an important index for power battery system. To obtain its accurate value,a comprehensive equivalent circuit model that parameters change depend on SOC was estiblished in this paper by using the lithium-ion battery hybrid pulse power characteristic data. Then the Extended Kalman filter (EKF) method is applied to estimate the SOC under the working condition. Numerical simulations are conducted to verify the effectiveness of the model and the EKF method. The results show that the EKF method based on the dynamic model can satisfy the accuray requirements.

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