Data-Driven Semi-Empirical Model Approximation Method for Capacity Degradation of Retired Lithium-Ion Battery Considering SOC Range

Abstract

The rapid development of the electric vehicle industry produces large amounts of retired power lithium-ion batteries, thus resulting in the echelon utilization technology of such retired batteries becoming a research hotspot in the field of renewable energy. The relationship between the cycle times and capacity decline of retired batteries performs as a fundamental guideline to determine the echelon utilization. The cycle conditions can influence the characteristics of the degradation of battery capacity; especially neglection of the SOC ranges of batteries leads to a large error in estimating the capacity degradation. Practically, the limited cycle test data of the SOC ranges of the retired battery cannot support a model to comprehensively describe the characteristics of the capacity decline. In this background, based on the limited cycle test data of SOC ranges, this paper studies and establishes a capacity degradation model of retired batteries that considers the factors affecting the battery cycle more comprehensively. In detail, based on the data-driven method and combined with the empirical model of retired battery capacity degradation, three semi-empirical modeling methods of retired battery capacity degradation based on limited test data of SOC ranges are proposed. The feasibility and accuracy of these methods are verified through the experimental data of retired battery cycling, and the conclusions are drawn to illustrate their respective scenarios of applicability.

Review on Modeling and SOC/SOH Estimation of Batteries for Automotive Applications

“…• Semi-empirical model: A semi-empirical approach combines elements of theoretical understanding with empirical data, which allows for a more accurate representation of the electrochemical processes within Li-ion batteries [109,110]. By incorporating both theoretical and experimental components, this model can provide insights into the battery's performance over its lifetime and aid with the prediction of degradation and capacity loss [111,112].…”