Abstract
Energy storage is an important technical means to increase the consumption of renewable energy and reduce greenhouse gas emissions. Electrochemical energy storage, represented by lithium-ion batteries, has a promising developmental prospect. The performance of lithium-ion batteries continues to decline in the process of application, and the differences between batteries are increasing. Therefore, accurate estimation of the state of health (SOH) of batteries is the key to the safe and efficient operation of energy storage systems. In this paper, the electrochemical impedance spectroscopy (EIS) characteristics of Li-ion batteries under different states of charge and health were studied. Three groups of Li-ion battery impedance module values under different frequencies were selected as characteristic parameters, and the SOH estimation model of Li-ion batteries was built by using the support vector regression (SVR) algorithm. The results show that: the model with the second group of frequency-point combinations as characteristic parameters takes into account both accuracy and efficiency; the cumulative time of the characteristic frequency test and SOH evaluation of lithium-ion batteries is less than 10 s; and this technology has good engineering application value.