New Open Battery Data Format Promotes Battery Data Interoperability
What you'll learn:
- The Battery Data Format (BDF) is an open, community-driven, format for data generated in battery testing labs.
- It provides a well-structured data framework that makes possible the seamless collection and exchange of battery test data between battery researchers, designers, and manufacturers, plus the OEMs that integrate them into products.
- The initial release of BDF includes a Python-based tool for reading/writing datasets and validating metadata and Conversion tools for transforming vendor-specific formats (e.g., Arbin, MACCOR) into BDF.
The Battery Data Alliance (BDA) has released the Battery Data Format (BDF), an open, community-driven standard for data generated in battery testing labs. The BDA’s consortium of industry stakeholders created it to bring order, interoperability, and transparency to researchers, designers, and manufacturers who work with battery data. It offers compatibility with leading modeling frameworks (PyBaMM, BattMo) and analysis platform.s
Specifically, BDF addresses two main challenges:
- Data consistency: With a common format, labs and cycler brands can eliminate the inconsistencies in data structure that arise with each software update.
- Model compatibility: A unified format means battery model developers can easily adapt their models to accept BDF data, making it possible for scientists to experiment with multiple models without custom coding each time.
By providing a unified schema and a consistent structure for experimental, simulation, and metadata-rich battery datasets, BDF is expected to be most valuable for use cases that generate and process large quantities of test data. They include battery designers and manufacturers, as well as the OEMs that need to integrate them into products. It will also benefit R&D labs that have to work with many different customers and equipment suppliers.
Once adopted, the unified data standard is also expected to improve the economics of the growing market for so-called “second-life” applications, which refurbish EV battery packs for use as replacements or as storage elements in wind or solar power systems.
Built on Collaborative Global Research
The development of BDF builds on extensive contributions from leading institutions across academia, industry, and government:
- BattINFO Ontology: BDF is aligned with the terminology and structure defined by the BattINFO Ontology from the Battery2030+ and BIG-MAP projects, ensuring consistent definitions, machine-readable metadata, and compatibility with broader FAIR linked-data practices.
- Faraday Institution’s PyProBE and BDX: Python Processing for Battery Experiments (PyProBE) is an open-source Python package designed to simplify and accelerate the process of analyzing data from battery cyclers. It was created at Imperial College London within the Faraday Institution’s Multi-scale Modelling project and has been instrumental in validating BDF’s column naming conventions and metadata definitions. Faraday Institution is funding the modification of PyProBE to adopt BDF-aligned naming, enabling interoperability between BDF and BDX (Battery Data eXchange), the optimized, binary format used by PyProBE to provide file size and processing performance benefits.
- Microsoft Open Battery Dataset Contribution: Microsoft plans to release a new battery dataset in the BDF format, providing the community with a high-quality reference dataset for benchmarking, tooling development, and educational use.
- Ohm BDF Converter Contribution: Ohm (YC W23) will contribute the BDF converter to the community. This web-based tool enables users to upload raw cycler data files and download a BDF-compliant .csv, streamlining adoption of the BDF standard across laboratories and workflows. Ohm’s converter supports major commercial cycler data formats, is freely available, and is designed to accelerate interoperability and reduce friction in transitioning to BDF-aligned data practices. Ohm is a pioneering leader in PhD-level, industrial AI agents purpose-built for battery science.
- Largest Open-Source Battery Data Contribution in the BDF (August 2025): Collaboration between Empa, ETH Zurich, EPFL, and SINTEF produced a dataset from 199 coin-cell batteries featuring both NMC/graphite and LFP/graphite chemistries, each tested for 1,000 cycles under fully automated, precisely controlled workflows in BDF.
These collaborations reinforce BDF as a standard shaped by real-world data workflows, from experimental cycling and materials characterization to physics-based models such as PyBaMM and BattMo.
Initial Scope of the BDF
- Intended to facilitate use and comparison of cycler time-series data.
- The BDF provides a fixed table schema for time-series battery data, which is supplemented with a machine-readable application ontology for integration with the Semantic Web.
- The BDF application ontology is defined as an extension of the BattINFO domain ontology that provides interoperability within the broader field of battery data.
- An immediate next step will be launching a parallel format for storing metadata for the BDF.
Future development will focus on formats for other types of lab data, such as impedance data.
Early software support includes:
- BDF Python Library for reading/writing datasets and validating metadata.
- Conversion tools for transforming vendor-specific formats (e.g., Arbin, MACCOR) into BDF.
- Reference visualization tools (web and notebook-based) for quick exploration of BDF datasets.
- Faraday Institution’s PyProBE.
- A BDF converter contributed by Ohm.
A public specification, documentation, examples, and reference datasets are available at
https://batterydataalliance.enexrgy.
Organizations interested in participating in the BDF working group or contributing datasets are invited to contact [email protected].
About the Author
Lee Goldberg
Contributing Editor
Lee Goldberg is a self-identified “Recovering Engineer,” Maker/Hacker, Green-Tech Maven, Aviator, Gadfly, and Geek Dad. He spent the first 18 years of his career helping design microprocessors, embedded systems, renewable energy applications, and the occasional interplanetary spacecraft. After trading his ‘scope and soldering iron for a keyboard and a second career as a tech journalist, he’s spent the next two decades at several print and online engineering publications.
Lee’s current focus is power electronics, especially the technologies involved with energy efficiency, energy management, and renewable energy. This dovetails with his coverage of sustainable technologies and various environmental and social issues within the engineering community that he began in 1996. Lee also covers 3D printers, open-source hardware, and other Maker/Hacker technologies.
Lee holds a BSEE in Electrical Engineering from Thomas Edison College, and participated in a colloquium on technology, society, and the environment at Goddard College’s Institute for Social Ecology. His book, “Green Electronics/Green Bottom Line - A Commonsense Guide To Environmentally Responsible Engineering and Management,” was published by Newnes Press.
Lee, his wife Catherine, and his daughter Anwyn currently reside in the outskirts of Princeton N.J., where they masquerade as a typical suburban family.
Lee also writes the regular PowerBites series.