Estimation of the dynamic parameters of a battery model under the hysteresis effect employing a hybrid algorithm of particle swarm optimization and grey wolf optimizer

Mashayekh, Hasan; Rezazadeh, Alireza

doi:10.30503/eecs.2020.251679.1014

Estimation of the dynamic parameters of a battery model under the hysteresis effect employing a hybrid algorithm of particle swarm optimization and grey wolf optimizer

Document Type : Original Article

Authors

Hasan Mashayekh

Alireza Rezazadeh

Faculty of Electrical and Computer Engineering, Shahid Beheshti University, Evin, Tehran, Iran

10.30503/eecs.2020.251679.1014

Abstract

An accurate estimation of battery model parameters is essential for dynamic simulation of electric vehicles. Generally, parameterizing battery models are difficult and complex. Therefore it requires powerful estimation algorithms to overcome time-consuming and computational costs.
In this paper, the dynamic parameters of a battery model were estimated at 8 different temperatures and under the hysteresis effect. The estimation is based on a hybrid algorithm of particle swarm optimization and grey wolf optimizer. By this hybridization the ability of exploitation in particle swarm optimization and the ability of exploration in grey wolf optimizer improved and both variants were empowered. The algorithm was implemented to estimate parameter values by minimizing the error between experimental data and the predicted results to find an optimal solution for an accurate model. Following a comparison with G.Plett’s, the results indicated that the proposed algorithm can reach higher precision in the battery behavior because of the lower error possibility.

Keywords

Battery

parameter estimation

enhanced self-correcting model

hysteresis