SAGE: Synthetic Aging for a Grid Environment

TL;DR

SAGE is an open-source simulation framework that models multi-decade battery degradation in grid energy storage, integrating environmental, operational, and aging factors for research and development.

Contribution

It introduces a physics-informed, multi-scale simulation platform that generates realistic long-term battery degradation data for grid applications.

Findings

Heterogeneity in thermal environments causes natural dispersion in health trajectories.

Validation confirms physical consistency through multiple hierarchical tests.

Framework enables reproducible research and benchmarking in energy storage modeling.

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.