Batteries: solve end-to-end production challenges
Battery performance cannot be understood from electrochemical results alone. The result depends on the materials, formulation, processing conditions, cell build, testing method, and production history behind it.
When analytical testing, cell data, and manufacturing records live in separate systems, teams lose time reconstructing full traceability. It becomes harder to compare cells, investigate unexpected behavior, connect material changes to performance, or carry learning from the bench into scale-up.
Uncountable’s battery R&D software connects analytical testing with electrochemical performance and production context. Barcoded sample tracking keeps material and sample identities intact, while connected records allow teams to trace results across R&D, testing, and manufacturing workflows.
This solution brief is for battery and materials teams that need to move from bench-scale learning to scalable production without losing the data behind cell performance.
Inside this solution brief:
- How to connect analytical testing with electrochemical performance
- Ways to preserve sample identity through barcoded tracking
- Full traceability from material and formulation to cell result
- How connected records support bench-to-scale-up decisions
- Why structured battery data matters for AI-supported R&D
Make every cell result part of a connected production story; not a number separated from the work that produced it. For more depth, read the battery R&D data guide.
FAQs
This guide covers how Uncountable connects analytical testing (PSD, ICP, XRD, TGA) with battery testing so materials and process teams can see how a formulation or process change affects cell performance, and how a barcoded sample-tracking workflow carries traceability from raw material through cell assembly, formation, and cycling QC.
It's built for two groups working from opposite ends of the same problem: materials and advanced-materials teams running formulation and process work from bench-top to pilot-line, and cell manufacturers who need the through-line from materials characterization to electrochemical performance, in one place, for every build.
Analytical testing measures material properties directly, using methods like PSD, ICP, XRD, and TGA. Battery testing measures electrochemical performance through cycling, which can take weeks or months to return results. Uncountable tracks both under the same record, so when e-chem results come back, the testing context, what was changed and why, is still there to make them actionable.
Every sample is barcoded and tracked as it moves through the workflow: for example, a cathode active material becomes a slurry, then a pressed cathode, which is combined with an anode, electrolyte, and separator into a cell assembly, then carried through formation and cycling QC. Nothing is logged by hand, and the full genealogy of a cell stays on one connected record.
Models are trained on your own formulation, process, and cycling data to help rank the next builds worth running, not a generic model. Structure comes first: because the underlying data is connected and structured, the machine learning built on top of it is grounded in your actual R&D history.
