Rapid Health Diagnosis Method for Retired Lithium-ion Batteries based on Electrochemical Impedance Spectroscopy

The screening and reuse of retired lithium-ion bat-teries(LiBs) have become significant challenges for the sustainable development of the energy storage industry. Accurate diagnosis of the State of Health(SOH) of retired batteries is the core step in achieving their effective screening and reuse. However, due to insufficient historical operational data of retired batteries, the existing estimation methods have problems such as poor accuracy and long time consumption, making it challenging to fulfill the requirements of real-world applications. Therefore, this work proposes a rapid estimation method for the SOH of retired LiBs based on Electrochemical Impedance Spectroscopy(EIS). This method utilizes the fast and easy availability of EIS to explore the intrinsic relationship between EIS and retired battery capacity through Artificial Neural Networks(ANN), and then establishes a SOH estimation model for retired batteries. Finally, experiments were conducted on 90 real retired electric vehicle batteries. The results reveal that this method has an average estimation error of no more than 1.3 % and takes less than 8 minutes to estimate.