Accelerating Analysis and Prediction of Lithium‐Ion Battery State of Health

With the increase in green power generation and the construction of energy storage systems, the demand for lithium‐ion batteries has also increased rapidly. Many lithium‐ion batteries are expected to be recycled in the future due to aging and decay, then being sorted and reused according to their status. However, the accuracy of the current equivalent circuit model predictions for screening and classifying recycled lithium‐ion batteries still needs improvement to ensure precise battery classification. This study develops a novel equivalent circuit model by measuring the electrochemical impedance inside the battery to fit the impedance. Subsequently, a trend line is established based on the fitting results and the trend line is finally used to predict and verify the state of health (SOH) of other identical retired lithium‐ion batteries. This approach makes the screening process for obsolete batteries faster and more precise. Through the prediction of SOH, the accuracy of the equivalent circuit model constructed herein is as high as 99.38%. The average overall prediction error is 2.57%, and the highest is only 3.58%. This study bridges the gap between experimental data and theoretical analysis, and contributes to advancing the understanding of degradation mechanisms and the management of electrochemical systems.