- Microsoft releases Surface battery data to standardize fragmented test environments
- Battery data set reveals inconsistencies between lithium-ion testing methods and tools
- The open format aims to reduce repeated engineering work between battery research teams.
Microsoft has contributed a standardized battery data set through its Surface Battery Development team to the Linux Foundation initiative known as the LF Energy Battery Data Alliance.
The release coincides with the introduction of the Battery Data Format, an open specification designed to improve consistency and interoperability in battery data workflows.
The data set focuses on cell architecture design variations, allowing direct comparison between multiple Li-ion configurations, including end-tab, mid-tab, and multi-tab designs.
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Raw test data is made openly accessible
The data set has been made publicly accessible through a repository, where it appears primarily as current and voltage time series measurements collected during controlled test cycles.
The format defines a structured approach for experimental, simulation and metadata-rich data sets.
It can be shared and reused across laboratories, software tools, and engineering environments without major modifications.
The Linux Foundation notes that the contribution is more than a routine data release, noting that it “reflects more than a release of an independent data set.”
The organization adds that it demonstrates how an emerging standard can be applied in real-world test scenarios rather than remaining conceptual.
Battery data has remained fragmented across institutions, vendors and platforms, often requiring customized handling before analysis can begin.
The Battery Data Format features a unified schema supported by ontology-based definitions derived from initiatives such as BattINFO, enabling machine-readable metadata and compatibility with broader linked data practices.
This structure allows data sets generated under different conditions or by different cycling systems to be consistently combined and analyzed.
It also supports compatibility between independently developed analytical models, reducing the need to repeatedly prepare data between research groups.
The data set contributed by Microsoft focuses on variations of lithium-ion cell architecture, including end-tab, mid-tab, and multi-tab configurations.
It includes initial performance benchmarks and cycle aging measurements, allowing engineers to examine how design differences influence degradation patterns over time.
These comparisons are often difficult when data sets originate from incompatible systems or follow inconsistent naming conventions.
Supporting tools within the Battery Data Format ecosystem include Python libraries for validation and conversion utilities that transform vendor-specific formats into standardized data sets.
The Battery Data Alliance includes a variety of research institutions and companies, with participation from groups such as SINTEF, the Faraday Institution, and several university laboratories.
Wider development of the format has also incorporated contributions from projects such as PyProBE and modeling frameworks such as PyBaMM, linking experimental data with simulation workflows.
Although the biggest names in the industry are missing, the Linux Foundation maintains that shared data sets are necessary for advanced computational analysis.
“Universal data management standards for every segment of the battery community are required for data creation to unlock the power of AI algorithms designed to identify everything from new candidate electrode materials to improved battery pack construction and cell life,” said Gabe Hege, president of the LF Energy Battery Data Alliance.
The data set released by Microsoft is the inaugural entry into a vendor-neutral data warehouse, although participation from other major manufacturers remains uncertain.
“This is a call to action,” said Noah Paulson, a battery scientist at Argonne. “We are trying to energize and organize the battery community to contribute their data… to enable powerful data science methods to catalyze advances.”
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