Open AccessA New Dynamic Parameters V-RC Model of Battery Parameter Identification
Author(s) -
Liguo Zhang,
Jing Gao,
Le Liu,
Guangxuan Miao,
Jintao Qi,
Han Xu,
Jiacui Luan
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/790/1/012018
Subject(s) - battery (electricity) , voltage , state of charge , control theory (sociology) , equivalent circuit , estimation theory , system identification , computer science , engineering , algorithm , data modeling , electrical engineering , power (physics) , physics , control (management) , artificial intelligence , quantum mechanics , database
Different state of charge (SOC), batteries have different model parameters. This paper proposes a dynamic parameter identification method for battery models under different SOC. Firstly, dynamic parameter V-RC model of battery was designed according to the hysteresis loops characteristics and the polarization characteristics of the battery voltage during the charge-discharge process. Next, the test program was designed which can complete the lithium battery charge and discharge data acquisition, including identification of battery model parameters for AC signal injection. The relationship between OCV and SOC are obtained by using the least squares curve fitting method, which realizes the function fitting of the model’s parameters. Then, battery transient response experiment indicating that have different parameters under different SOC. Finally, test results can show that open-circuit voltage (OCV) error of the new model is lower than constant parameter model OCV error. The error of the fixed parameter model OCV is 2.6%, while the OCV error of the dynamic parameter model is 1.6%. Dynamic parameter VRC model can more accurately reflect the actual state of the battery.