MELODI: An explainable machine learning method for mechanistic disentanglement of battery calendar aging

Abstract

Lithium-ion batteries (LIBs) are widely deployed, from grid-scale storage to electric vehicles. LIBs remain stationary most of their service life, where calendar aging degrades capacity. Understanding the mechanisms of LIB calendar aging is crucial for extending battery lifespan. However, LIB calendar aging is influenced by multiple factors, including battery material, its state, and storage environment. Calendar aging experiments are also time-consuming, costly, and lack standardized testing conditions. This study employs a data-driven approach to establish a cross-scale database linking materials, side-reaction mechanisms, and calendar aging of LIBs. MELODI (Mechanism-informed, Explainable, Learning-based Optimization for Degradation Identification) is proposed to identify calendar aging mechanisms and quantify the effects of multi-scale factors. Results reveal that cathode material loss drives up to 91.42 % of calendar aging degradation in high-nickel (Ni) batteries, while solid electrolyte interphase growth dominates in lithium iron phosphate (LFP) and low-Ni batteries, contributing up to 82.43 % of degradation in LFP batteries and 99.10 % of decay in low-Ni batteries, respectively. This study systematically quantifies calendar aging in commercial LIBs under varying materials, states of charge, and temperatures. These findings offer quantitative guidance for experimental design or battery use, and implications for emerging applications like aerial robotics, vehicle-to-grid, and embodied intelligence systems.