Parameter Identification of Battery Model Based on Forgetting Factor Recursive Least Square Method

2018 ◽  
Author(s):  
Shuang Kou ◽  
Xianwu Gong ◽  
Qingchuan Zhu ◽  
Guiping Wang
Keyword(s):  
Parameter Identification ◽  
Least Square Method ◽  
Least Square ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Battery Model ◽  
Model Based ◽  
Recursive Least Square Method

scholarly journals Lithium battery parameter identification and SOC estimation based on dual-polarized model

2021 ◽  
Vol 300 ◽  
pp. 01013
Author(s):  
Hui Xia ◽  
Changlei Li ◽  
Yusang Xu ◽  
Xuehong Liu
Keyword(s):  
Lithium Battery ◽  
Estimation Error ◽  
Equivalent Circuit Model ◽  
Least Square Method ◽  
Base Station ◽  
Least Square ◽  
Model Parameters ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Recursive Least Square Method

An equivalent circuit model of dual polarization (DP) of lithium battery was established according to the application characteristics of lithium battery under the standby condition of 5G base station. On the basis of the model, recursive least square method with forgetting factor (RLS) was used to identify the model parameters. Finally, the Unscented Kalman filtering (UKF) was used to estimate the SOC of lithium battery in real time with the identified model parameters. The simulation and experimental results showed that the combined estimation using recursive least square method with forgetting factor (RLS) and UKF could greatly improve the estimation accuracy of lithium battery SOC, reduce the estimation error, and further verify the accuracy and effectiveness of the whole modeling.

Sign in / Sign up

Export Citation Format

Share Document