SAGE: Synthetic Aging for a Grid Environment

Abstract

Grid-scale battery degradation unfolds over multi-year timescales under coupled electrochemical, thermal, and operational feedbacks difficult to capture using laboratory data or proprietary field datasets. This scarcity limits the development of degradation-aware algorithms and digital twins that require long-horizon, physically consistent ground truth. Here we present SAGE (Synthetic Aging for a Grid Environment), an open-source, physics-informed simulation framework that generates hour-resolved, multi-decade operating histories and degradation trajectories for heterogeneous battery energy storage system (BESS) fleets. The framework couples stochastic environmental drivers, market-based dispatch, electro-thermal behavior, aging kinetics, and asset-level heterogeneity within a transparent, externally parameterized architecture. We validate physical consistency through hierarchical tests, including Arrhenius temperature acceleration, thermal stratification, and emergent wear-out statistics. Simulations demonstrate how intrinsic heterogeneity in thermal environments and manufacturing naturally produces dispersion in state-of-health trajectories without imposed statistical failure assumptions. SAGE serves as a benchmarking platform for optimization, state estimation, and machine learning, enabling reproducible research in grid-scale energy storage modeling.