Open-Source Energy Storage Management Tools from DOE
summary
DOE funds ROVI to monitor 15 long‑duration storage projects across six national labs and builds an open data hub that ingests system, lab and field data into Battery Archive and links models via PyBaMM. Tooling uses scikit‑learn elastic net baselines and a TensorFlow hybrid ML+physics model validated on 10+ Li‑ion datasets to predict early degradation. Typical systems have ~200,000 cells (720M floats ≈ 2.8 GB per dump), creating real challenges for ingestion, parameter identification and cell‑to‑cell variability capture.
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chapters (19)
- 0:00Energy storage challenges
- 1:02Energy storage for the grid
- 3:56Long-duration storage costs
- 6:22Battery development timeline
- 9:08Rapid Operational Validation goals
- 11:15Data hub for energy storage
- 13:42Modeling complexity from cells
- 14:25Physics-based battery modeling
- 17:59Battery analysis techniques
- 19:21Open-source battery parameters
- 21:05Predicting battery degradation
- 22:10Scaling models to modules
- 23:34PyBaMM simulation
- 25:41Cell-to-cell variation challenges
- 26:44Machine learning for batteries
- 29:10Hybrid physics-AI models
- 30:55Accelerating tech development
- 32:00Open-source software challenges
- 33:02Q&A
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