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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2603.13976 |
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| _version_ | 1866915862962765824 |
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| author | Esquivel, Paolo Harris, Mark A. Harris, Stephen J. |
| author_facet | Esquivel, Paolo Harris, Mark A. Harris, Stephen J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13976 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SAGE: Synthetic Aging for a Grid Environment Esquivel, Paolo Harris, Mark A. Harris, Stephen J. Applied Physics 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. |
| title | SAGE: Synthetic Aging for a Grid Environment |
| topic | Applied Physics |
| url | https://arxiv.org/abs/2603.13976 |