scholarly journals SOC Estimation of Modular Lithium Battery Pack Based on Adaptive Kalman Filter Algorithm

2019 ◽  
Vol 1345 ◽  
pp. 042069
Author(s):  
Zhi Duan Cai ◽  
Jing Yun Xu ◽  
Xiao Yue Sun ◽  
Li Di Quan
Keyword(s):  
Kalman Filter ◽  
Lithium Battery ◽  
Battery Pack ◽  
Adaptive Kalman Filter ◽  
Soc Estimation ◽  
Kalman Filter Algorithm

Battery SOC Estimation Based on Multi-Model Adaptive Kalman Filter

2011 ◽  
Vol 403-408 ◽  
pp. 2211-2215 ◽  
Author(s):  
Ke Xin Wei ◽  
Qiao Yan Chen
Keyword(s):  
Kalman Filter ◽  
State Of Charge ◽  
Model Parameters ◽  
Adaptive Kalman Filter ◽  
Single Model ◽  
Soc Estimation ◽  
Battery Model ◽  
Kalman Filter Algorithm ◽  
Estimation Precision ◽  
State Of Charge Estimation

This paper introduces multi-model adaptive kalman filter estimation algorithm.Based on the battery thevenin model,the multi-model adaptive kalman filter is applied to the battery SOC(state of charge) estimation, which solute the battery SOC estimation in conditions that the battery model parameters change caused by temperature changing. Simulation results show that compared to the single model kalman filter algorithm, Multi-Model adaptive kalman filter algorithm improves the estimation precision and reliability greatly.

The Experiments of Dual Kalman Filter in Lithium Battery SOC Estimation

2014 ◽  
Vol 494-495 ◽  
pp. 1509-1512 ◽  
Author(s):  
Jian Long Huang ◽  
Ying Nan Wang ◽  
Zhong Feng Wang ◽  
Feng Li Han ◽  
Li Gang Li
Keyword(s):  
Kalman Filter ◽  
Lithium Battery ◽  
Lithium Ion ◽  
Least Square Method ◽  
Least Square ◽  
Testing Laboratory ◽  
Soc Estimation ◽  
Battery Model ◽  
True State ◽  
Kalman Filter Algorithm

We use Thevenin battery model and Kalman filter algorithm to online estimate lithium-ion battery pack state of charge (SOC) in this paper. In order to improve the accuracy of the model we use least square method and Dual Kalman filter (DEKF) algorithms to identify the parameters of model. The battery model can reflect the true state of internal battery well. The principle of Kalman filter algorithm is introduced. The relevant battery testing laboratory is designed. The algorithm has better accuracy when online estimate SOC and adapt to the environment well from experimental results. Finally, the convergence and robustness of DEKF algorithm are verified. It solves the problems that initial estimates are not accuracy and cumulative error.

Research on Active Equalization of Lithium Battery via SOC

2014 ◽  
Vol 556-562 ◽  
pp. 2013-2016
Author(s):  
Yun Gan Wang ◽  
Zhong Feng Wang ◽  
J.L. Huang ◽  
Li Gang Li
Keyword(s):  
Kalman Filter ◽  
Equivalent Circuit ◽  
Extended Kalman Filter ◽  
Lithium Battery ◽  
Equivalent Circuit Model ◽  
High Accuracy ◽  
Battery Pack ◽  
Low Loss ◽  
Adaptive Parameters ◽  
Kalman Filter Algorithm

This paper presents an active equalization method for lithium battery via SOC. The high accuracy of SOC is promised by extended Kalman filter algorithm (EKF) based on the adaptive parameters equivalent-circuit model at all SOC region. Then a small size, low loss resonant soft-switching active equalization circuit is presented. That circuit combined with SOC do precisely balance the battery pack during charge and discharge processes, which can effectively improve the battery pack’s using life.

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

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.

Research on Adaptive Kalman Filter for INS

2017 ◽  
Vol 872 ◽  
pp. 316-320
Author(s):  
Kai Xia Wei
Keyword(s):  
Kalman Filter ◽  
Response Speed ◽  
Measured Data ◽  
Specific Method ◽  
Alignment Error ◽  
Initial Alignment ◽  
Adaptive Kalman Filter ◽  
Noise Interference ◽  
Kalman Filter Algorithm ◽  
The Stability

Due to sensor accuracy and noise interference and other reasons, the measured data may be inaccurate or even wrong. This will reduce the accuracy of the filter and the reliability and response speed of the Kalman filter, and even make the Kalman filter lose the stability. In this paper, a new INS initial alignment error model and observation model are derived for the errors in INS initial alignment. The adaptive Kalman filter is built to improve the stability and the accuracy of filter. The specific method is to make the adaptive Kalman filter manage to correct the filter online by getting the observed data. The simulation results show the proposed algorithm improves the accuracy of initial alignment in SINS, and prove the adaptive Kalman filter is effective. The main innovation in this paper is to manage to build the adaptive Kalman filter to modify the filter online by using the observed data. The adaptive Kalman filter algorithm improves the accuracy of the filter.

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