Long-Term Calendar Aging across Commercial Lithium-Ion Cell Chemistries - Part II: Modeling and Early Prediction

Abstract

Lithium-ion (Li-ion) batteries are widely used in applications such as mobility, stationary grid systems, and various consumer and commercial systems. In electric vehicles (EVs), batteries often remain idle, with only about 10% utilization. Moreover, in stationary battery energy storage systems (BESS) designed for peak shaving, resting periods tend to cause more aging effects than the operational cycles. Consequently, quantifying the effect of calendar aging is crucial. However, current calendar aging models are inadequate for accurately modeling and predicting the diverse aging behaviors of commercial Li-ion batteries. In this study, we analyze a new, long-term Li-ion battery calendar aging dataset, which includes eight different commercial battery types. We compare and validate three calendar aging models—semi-empirical, symbolic regression, and Long Short-Term Memory Network (LSTM). We examine the transferability of these models across various commercial cell types, offering insights into their generalizability. Additionally, we introduce a novel trajectory-to-trajectory approach for early prediction of calendar aging lifetime. For the first time, we investigate the model's ability to interpolate and extrapolate aging behavior under different storage temperatures. This research provides practical insights for optimizing battery management strategies in conditions dominated by calendar aging.