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