State of Health Estimation of Lithium-Ion Batteries based on the CC-CV Charging Curve and Neural Network

Abstract

ABSTRACT Nowadays, the lithium-ion batteries are used as a major component of power in many applications. This battery faces the aging phenomenon that results in its degradation and reduced efficiency in supplying and storing the required energy during its life cycle. Therefore, one of the important aspects of a battery management system is the State-of-Health (SOH) to ensure battery safety and reliability. SOH estimation, which indicates the aging level of a battery, is a challenging issue due to the complex aging mechanisms and factors that cause battery degradation. On the other hand, the application of each of the SOH estimation methods is limited under a number of compulsory constraints such as battery operation under full discharge cycles. The batteries are rarely fully discharged under actual operating conditions; therefore, so in this paper, according to the CC-CV charging protocol, two health indices (HIs) that are less dependent on the starting the charging cycle are introduced. Here, these two indices prove to be highly correlated with capacity changes over the life of the battery. Due to the proper performance of the neural network in fitting the nonlinear curves, it is used to establish a correlation between these indices and the battery SOH. The analysis and evaluation of the performance confirms the consistency and efficiency of the proposed method so that the maximum estimation error is less than 2%.