Toward Accurate and Efficient Sorting of Retired Lithium-Ion Batteries: A Data-Driven-Based Electrode Aging Assessment Approach

Retired batteries (RBs) for second-life applications offer promising economic and environmental benefits. However, accurate and efficient sorting of RBs with discrepant characteristics persists as a pressing challenge. In this study, we propose an electrode aging assessment approach to address this concern. First, we introduce three novel electrode aging parameters (EAPs) by investigating the aging-induced relative position shifts of electrode and battery open-circuit voltage (OCV) curves. Compared with conventional sorting indices, these EAPs are capable of intuitively interpreting the impact of primary aging mechanisms on battery electrodes, i.e., loss of lithium inventory (LLI) and loss of active material (LAM). Second, we present a data-driven scheme for rapid EAP estimation. This scheme only relies on a number of 15 OCV feature points with fixed magnitudes and varying differential charge amounts to capture essential OCV characteristics at different levels of aging, thereby eliminating the need for substantial and successive OCV data collection and alleviating the associated computational costs. In addition, we employ an adaptive affinity propagation (adAP) algorithm to sort RBs that unlocks the necessity of predetermining the clustering number. The validation results prove the effectiveness of the proposed approach in contrast to capacity-based benchmark methods.