A novel differential voltage segment-based rejection and clustering method for enhancing short and long term consistency in reuse of retired batteries

Retired battery reuse presents significant economic and environmental advantages, with battery consistency being a critical factor for optimizing performance in second-life applications. Current research methods have the following deficiencies: Feature selection lacks a good cost-efficiency balance, hindering practical scalability; Clustering algorithms are deficient in robustness, adaptability, and initialization sensitivity, restricting the use in multidimensional battery datasets; Verification experiments often ignore comprehensive metrics, failing to consider long-term consistency in real applications. To address these issues, this paper introduces an innovative approach to enhance the uniformity of retired battery systems, incorporating four key advancements: First, coefficient of variation (CV) analysis is applied to differential voltage (DV) curves to identify critical DV segments. Second, a hybrid algorithm integrating the nearest neighbor technique with fuzzy C-means (FCM) clustering is introduced for optimal battery grouping, improving long-term capacity retention by at least 4.8–9.9 % compared to conventional methods. Third, a distance-based outlier detection method is proposed, which enhances long-term system consistency by 15.7 % relative to density-based approaches. Finally, a weighted-average methodology is developed to systematically assess battery consistency. Collectively, these innovations establish a comprehensive framework for improving the reliability and sustainability of retired battery systems in secondary use scenarios.