Performance Enhancement of Second-Life Lithium-Ion Batteries Based on Gaussian Mixture Model Clustering and Simulation-Based Evaluation for Energy Storage System Applications

Lithium-ion batteries (LIBs) are widely deployed in electric vehicles due to their high energy density and long cycle life. Even after retirement, typically at around 80% of their rated capacity, LIBs can still be repurposed for second-life applications such as residential energy storage systems (ESSs). However, effectively grouping these heterogeneous cells is crucial to optimizing performance of the module. Retired LIBs can be effectively repurposed for numerous second-life applications such as ESSs, and other power backups. In this paper, we compare four clustering approaches including random grouping, equal-number Support Vector Clustering, K-means, and an equal-number Gaussian Mixture Model (GMM) to organize 60 retired cells into 48 V modules consisting of 15-cell groups. We verify the performance of each method via simulations of a 15S2P configuration, focusing on the standard deviation of final charge voltage, average charge throughput, delta capacity, and coulombic efficiency. Based on the evaluation metrics analyzed after regrouping the battery cells and simulating them for second-life ESS applications, the GMM-based clustering method demonstrates better performance.