Aging Estimation and Clustering of Used EV Batteries for Second-Life Applications

This study presents an integrated machine learning framework to evaluate the aging states of lithium-ion batteries and to classify them according to their second-life application potential. The methodology combines two key components: a set of regression models to estimate critical health indicators, such as capacity and internal resistance, and a classification stage to group batteries based on these parameters. The proposed models were trained and validated using the NASA Battery Aging Datasets. Through an in-depth analysis of environmental conditions, the study identifies their influence on aging metrics, reinforcing the relevance of the input features selected. Furthermore, a clustering-based approach was employed to validate the classification performance and to reveal the link between a battery’s operation and its aging in the Euclidean space. The results show accurate predictions without signs of overfitting or underfitting, and the classification framework proved robust across the evaluated cases. This suggests that the proposed method can serve as a scalable and adaptable tool to guide battery repurposing strategies. Overall, the findings contribute to bridging the gap between battery diagnostics and real-world energy storage applications, offering practical insights to optimize second-life deployment.