Susan J. Babinec
Program strategy, Argonne ACCESS
DOE's Rapid Operational Validation Initiative (ROVI) is intended to address critical gaps in data needs to evaluate the long-term performance of energy storage technologies and to accelerate the pace for technology development. The six lab ROVI team (ANL-INL-NLR-ORNL-PNNL-Sandia) is primarily focused on accelerating the time from lab to market for long duration energy storage (LDES) systems by employing data-driven tools to predict their operational lifetimes. ROVI will develop a suite of adaptive AI/ML modeling tools based on data from laboratory testing and model simulations that will be validated with data from field deployed systems supported by DOE's Office of Electricity (OE) and Office of Clean Energy Demonstrations (OCED).
There are many innovative energy storage technologies being developed today that are promising candidates to achieve important cost and performance targets, such as DOEβs Long Duration Storage Shot, and ultimately reach widespread commercial deployment needed to facilitate a reliable, clean, and affordable electricity system of the future. The focus of the Office of Electricityβs Rapid Operational Validation Initiative (ROVI) is to greatly reduce time required for emerging energy storage technologies to go from lab to market by developing new tools that will accelerate the testing and validation process needed to ensure commercial success. To develop these tools, ROVI will employ innovative data science methods such as artificial intelligence and machine learning that will leverage large sets of energy storage performance data at different scales to facilitate generating lifetime performance predictions for new technologies with minimal real-time testing required.
DOE program page βROVI researchers and program leads across the Department of Energy and its national laboratories. Names link to individual profile pages where available.
Program strategy, Argonne ACCESS
Translational AI
Electrochemical modeling
Cell fabrication and testing, CAMP Facility
State-of-health estimation
Data infrastructure
AI/ML for lithium-ion
Technology manager
Accelerated testing
Validation and deployment testing
Equivalent circuit models
Lifetime and use-case modeling
Program leadership
Energy storage leadership
AI/ML for flow batteries
Flow battery modeling
Data requirements
Energy storage leadership
Featured paper
Simultaneous prognosis of 28 battery health descriptors across six lithium-ion cathode chemistries, including an unprecedented mean absolute error of 19 cycles for end-of-life on a fast-charging LFP dataset.
Read the paper (PDF) β Β· doi:10.1016/j.xcrp.2024.101928ROVI develops open-source tools for battery data handling, state-of-health estimation, and prognostics. Public code is hosted in the ROVI GitHub organization.
Browse repositories, open issues, and contribute.
Utilities for reading and manipulating battery testing data.
PythonApache-2.0Fit state of health (SOH) to batteries using state estimation techniques.
PythonMITROVI DataHub utility for interacting with the DataHub from Python.
PythonMIT| Repository | Description | Language | License |
|---|---|---|---|
| rovidh | ROVI DataHub utility | Python | MIT |
| auto-soh | Fit state of health to batteries using state estimation | Python | MIT |
| battery-data-toolkit | Read and manipulate battery testing data | Python | Apache-2.0 |
| services-demonstration | Pipeline from data upload to a system health dashboard | Python | MIT |
| collecting-battery-datasets | Download and standardize data used by ROVI modeling teams | Python | MIT |
| model-to-prognosis | Example pipeline: model β synthetic data β AI β prognosis | Python | MIT |
| storage-requirement-estimation | Estimate ROVI data storage requirements | Python | MIT |
| thevenin | Equivalent circuit models in Python (fork of NatLabRockies/thevenin) | Python | BSD-3-Clause |
| progpy | NASA prognostics packages for remaining-useful-life modeling (fork of nasa/progpy) | Python | NASA OSA |
| rovi-org.github.io | Source for this website | HTML | MIT |
Talks, webinars, and briefings by the ROVI team.
May 2026 Β· ORNL
Demonstration of the ROVI DataHub to the DOE Office of Electricity Energy Storage program, combining Li-ion system streams from Sandia with redox flow battery datasets from PNNL.
ORNL news story β Watch video β2023 Β· DOE Office of Electricity
Presented by Vinod Siberry (DOE OE) and Vincent Sprenkle (PNNL), with panelists Yuliya Preger (SNL), Srikanth Allu (ORNL), Noah Paulson (ANL), Jie Bao (PNNL), and Kevin Gering (INL) covering data requirements, infrastructure, AI/ML, and accelerated testing.
Slides (PDF) β2023 Β· DOE Office of Electricity
Data requirements and reference performance tests for Li-ion systems contributing to ROVI.
Slides (PDF) β2021 Β· Industry Roundtable
Launch discussion with industry and national laboratory participants on validating new storage technologies faster than conventional testing allows.
Slides (PDF) βROVI works with technology developers, utilities, and industry groups to shape data requirements and validate models against field-deployed systems.
Long-duration solid-flow battery demonstration with Argonne and Idaho National Laboratories.
Solid-flow batteryMulti-day zinc battery demonstration as an alternative to diesel backup during utility power shutoffs.
Zinc battery24-hour vanadium flow battery demonstration.
Vanadium flowThese projects were selected under the DOE Long-Duration Energy Storage Demonstrations lab call, a source of field-deployed systems for validating ROVI models.
Industry Roundtable participant; EPRI energy storage data guidelines inform ROVI data requirements.
Industry Roundtable participant bringing a performance-risk and insurance perspective.
BCI / Cleantech Strategies Flow Battery Working Group engagement on flow battery data needs.
Interested in contributing data to ROVI? Project teams can complete the workplan template on the OE ROVI page and send it to ESGC@hq.doe.gov.
As part of the development of a DataHub, the ROVI team has developed a flexible and secure data architecture that can ingest complex data streams and supply consolidated data sets for project evaluations and/or advance machine learning approaches.
Advanced AI/ML models for predicting battery degradation and performance across various operating conditions and environmental factors.
Comprehensive validation protocols ensuring data quality and consistency across multiple laboratory and field deployment sites.
Live monitoring and analysis capabilities for continuous assessment of energy storage system performance and health.
Multi-laboratory collaboration framework enabling shared data access and coordinated research efforts across DOE facilities.
Access the main ROVI testing and validation platform for energy storage technologies.
Visit Platform βReal-time monitoring and analytics dashboards for ROVI data streams and system performance.
View Dashboards βComprehensive API documentation for integrating with ROVI data services and endpoints.
API Docs βOpen-source tools and libraries for battery data analysis and machine learning workflows.
Explore Toolkit β