Reserved memory for Li-ion battery SOC estimation method in applications with EV

Abstract

The state-of-charge (SOC), state-of-health (SOH) estimation and prediction of lithium-ion batteries remaining useful life (RUL) are critical for the safety and reliability of battery management systems (BMS) in electric vehicles (EVs). In this paper, two different methods are used to estimate how much memory is needed to evaluate the SOC and their performances regarding tracking accuracy, memory volume and computational complexity. The SOC and SOH cannot be directly measured and estimation is influenced by many factors, such us battery aging, ambient temperature and the current rate. The complex interrelationship of these factors causes the difficulties in the pursuit of a precise SOC estimation method. The important issue of this study is to obtain the SOC estimation for a specific Li-ion battery based on the behaviour of the battery during operation. The analyzed Li-ion battery is part of a micro-grid (MG) belonging to the Advanced Electrical Systems Research Centre within the Research and Development Institute of Transilvania University of Brasov, which provides around 20 kWh storage capacity. Through simulations, the analyzed SOC estimation method is verified to demonstrate the computational complexity and accuracy.