Electrochemical Model Parameter Identification of Lithium-Ion Battery with Temperature and Current Dependence

Abstract

Battery modelling and state estimation are crucial for lithium-ion batteries applied in electrical vehicles (EVs). In this work, a simplified electrode-average electrochemical model of a lithium-ion battery that adopts a polynomial approximation and a three-variable method to reduce the order of the solid and electrolyte phase diffusion equations is designed. A novel parameter identification method considering temperature and current is also proposed to reduce the parameter deviation caused by different working conditions. The model parameters are identified by the genetic algorithm (GA) offline at different temperatures and currents to create lookup tables for online estimation. Furthermore, 3.5 Ah NCM 18650-type cells are chosen to validate the simplified model and the proposed estimation method. The results indicate that the proposed scheme is accurate, simple and flexible for current and temperature changes under different operation conditions.