scholarly journals Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm

Energies ◽  
2016 ◽  
Vol 9 (2) ◽  
pp. 100 ◽  
Author(s):  
Xiangwei Guo ◽  
Longyun Kang ◽  
Yuan Yao ◽  
Zhizhen Huang ◽  
Wenbiao Li
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Electric Vehicle ◽  
Least Squares Method ◽  
State Of Charge ◽  
Joint Estimation ◽  
Power Battery ◽  
Battery State Of Charge ◽  
Kalman Filter Algorithm

Information Fusion for State Estimation of Power Battery in Electric Vehicle Based on Unscented Kalman Filter

2013 ◽  
Vol 303-306 ◽  
pp. 975-978
Author(s):  
Hong Yu Zheng ◽  
Chang Fu Zong
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Information Fusion ◽  
Unscented Kalman Filter ◽  
Accurate Estimate ◽  
Fusion Algorithm ◽  
Power Battery ◽  
Battery State Of Charge ◽  
New Generation

The power battery state of charge (SOC) in electric vehicles is not easy to measure accurately or apply a sensor but the expense is increased. However the variable of SOC is great importance to control of electric vehicles. A power battery model is built by the Partnership for a New Generation of Vehicles (PNGV) model to estimate the state of SOC. In order to make a high accurate estimate for SOC value, an information fusion algorithm based on unscented kalman filter (UKF) is introduced to design an observer. The test results show that the observer based information fusion and UKF are effective and accuracy, so it is may apply it the electric vehicle control and observation.

The State of Charge Estimation of Lithium Battery in Electric Vehicle Based on Extended Kalman Filter

2014 ◽  
Vol 953-954 ◽  
pp. 796-799
Author(s):  
Huan Huan Sun ◽  
Jun Bi ◽  
Sai Shao
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Extended Kalman Filter ◽  
Nonlinear Problems ◽  
Least Square Method ◽  
State Equation ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery State Of Charge

Accurate estimation of battery state of charge (SOC) is important to ensure operation of electric vehicle. Since a nonlinear feature exists in battery system and extended kalman filter algorithm performs well in solving nonlinear problems, the paper proposes an EKF-based method for estimating SOC. In order to obtain the accurate estimation of SOC, this paper is based on composite battery model that is a combination of three battery models. The parameters are identified using the least square method. Then a state equation and an output equation are identified. All experimental data are collected from operating EV in Beijing. The results of the experiment show  that the relative error of estimation of state of charge is reasonable, which proves this method has good estimation performance.

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

Prediction Method of Lithium Battery's State of Charge Based on No Trace of Calman Filter

2014 ◽  
Vol 912-914 ◽  
pp. 1888-1891
Author(s):  
Zhen Ping Cui ◽  
Yong Xin Qin ◽  
Hao Li
Keyword(s):  
Kalman Filter ◽  
Equivalent Circuit ◽  
Lithium Battery ◽  
Unscented Kalman Filter ◽  
Equivalent Circuit Model ◽  
Prediction Method ◽  
Open Circuit ◽  
State Of Charge ◽  
Battery State Of Charge ◽  
Kalman Filter Algorithm

The Thevenin equivalent circuit model is established for single lithium battery,current and voltage data to identify the parameters of the equivalent circuit is obtained by the discharge experiment, and the open circuit voltage and charge state relationship curve was obtained by curve fitting.On this basis, design the extended Kalman filter algorithm and unscented Kalman filter algorithm on the lithium battery state of charge, then use Matlab/Simulik simulation, the results of the state prediction of the two different algorithms are compared. The analysis results show that two kinds of algorithm are effective for single lithium battery state of charge estimation, and no trace of Calman filter algorithm can effectively solve the the problem of accuracy is not high of the extended Calman filter ,which due to the linear approximation.

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