Li-ion Battery Models and A Simplified Online Technique to Identify Parameters of Electric Equivalent Circuit Model for EV Applications

Abstract

Reducing the computational burden and improving the accuracy are in the two opposite sides of every electrical equivalent circuit model (EECM) for batteries. In this paper, a novel identification method is developed to estimate EECM parameters for any Li-ion battery with the aim of reducing computational burden and improving the accuracy of estimation under high C-rates of charge/discharge cycles for electric vehicle (EV) applications. The proposed parameter identification method is implemented on the second-order EECM. A step by step execution of the new identification method is presented which is based on circuit analysis and Particle Swarm Optimization (PSO). A comparison is carried out between obtained and the Pseudo-Two-Dimension (P2D) electrochemical model results. Moreover, experiments are carried out on two Li-ion battery cells, NCR18650 Panasonic and Mp176065 to verify the accuracy of the proposed method. The included results demonstrate the performance of the proposed method regarding improving accuracy and applicability as well as reducing required memory.