Development of Empirical Model for Li-ion batteries and Application to Dynamic Aging Pattern

Abstract

Recently, with the commercialization of many products using lithium-ion batteries, including Electric Vehicles (EVs), there has been significant interest in the capacity loss of the battery. Consequently, numerous studies have been conducted to investigate this topic. In this study, I aimed to estimate the capacity loss of the electric vehicle (EV) battery under various operating conditions. To achieve this, I integrated an empirical model for estimating capacity loss through modeling and a CAP method that connects models under different conditions. The validation will be conducted by estimating the state of the degraded battery in the form of a pattern that changes in various ways. As a result, it was shown that high-accuracy capacity loss estimation is possible with a small amount of computational power. This can be achieved by substituting stress factors extracted from pattern aging data into the model. Based on this, cost-effective on-board State of Health (SOH) estimation is expected to be possible.