scholarly journals A Sensitivity-Based Group-Wise Parameter Identification Algorithm for the Electric Model of Li-Ion Battery

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 4377-4387 ◽  
Author(s):  
Wen-Jing Shen ◽  
Han-Xiong Li
Keyword(s):  
Parameter Identification ◽  
Identification Algorithm ◽  
Li Ion Battery ◽  
Electric Model ◽  
Li Ion

The Method Research of Parameter Identification of Li-Ion Battery Base on Least Square Method

2012 ◽  
Vol 490-495 ◽  
pp. 3854-3858
Author(s):  
Jin Feng ◽  
Yong Ling He ◽  
Ran Bao
Keyword(s):  
Parameter Identification ◽  
Equivalent Circuit ◽  
Equivalent Circuit Model ◽  
Least Square Method ◽  
Least Square ◽  
Voltage Response ◽  
Li Ion Battery ◽  
Model Parameter ◽  
Battery Model ◽  
Li Ion

This paper described the theory of parameter identification of Li-ion battery base on least square method. The result of voltage response by HPPC test is discussed. The model parameter is identified through least square method. The battery model is built with Matlab/Simulink and the identified parameter is verified. The result indicated that the least square method is suitable for parameter identification of Li-ion battery equivalent circuit model.

Identification of Parameters in Li-Ion Battery Model by Least Squares Method with Variable Forgetting Factor

2019 ◽  
Vol 17 (07) ◽  
pp. 1950027
Author(s):  
Xiong Wei ◽  
Mo Yimin ◽  
Zhang Feng
Keyword(s):  
Least Squares ◽  
Electric Vehicle ◽  
Least Squares Method ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Li Ion Battery ◽  
Forgetting Factor ◽  
Battery Model ◽  
Li Ion ◽  
Variable Forgetting Factor

The inaccuracy of the battery model of an electric vehicle will seriously affect the safe operation of the electric vehicle. This paper aims to design a better identification method for Li-ion battery model parameters to improve the accuracy of the model. A least squares method was developed with variable forgetting factor (VFF) to identify the parameters of a second-order resistor-recapacitor (RC) model of Li-ion battery. After using the identified parameters, the battery model can reliably and accurately track the variability of the actual working state of the energy storage system. Results at different values of the forgetting factor were analyzed to determine the principle for selecting the value of the forgetting factor, and disclose the impacts of the factor values on model accuracy. Finally, the proposed identification algorithm was tested through comparison between results of the model simulation and experimental data. This method provides an important basis for subsequent development of accurate state-of-charge (SOC) and state-of-health (SOH) estimation algorithms.

Sign in / Sign up

Export Citation Format

Share Document