Workflows and Principles for Collaboration and Communication in Battery Research

TL;DR

The paper addresses the challenge of cross‑disciplinary collaboration in battery research by identifying vocabulary and data curation bottlenecks that slow progress. It presents a FAIR‑oriented workflow that combines BattINFO ontologies, open data practices, automated processing, and probabilistic model parameterization (DFN/SPMe/SPM) implemented with EP‑BOLFI and Kadi4Mat, demonstrated on GITT data. The results show how this approach uncovers mismatches in data interpretation, yields probabilistic diffusivity estimates with quantified uncertainties, and enables interoperable, machine‑readable data pipelines. The work provides a scalable blueprint for transparent, reusable battery data pipelines that can connect laboratories worldwide and accelerate discovery of durable, high‑performance materials.

Abstract

Interdisciplinary collaboration in battery science is required for rapid evaluation of better compositions and materials. However, diverging domain vocabulary and non-compatible experimental results slow down cooperation. We critically assess the current state-of-the-art and develop a structured data management and interpretation system to make data curation sustainable. The techniques we utilize comprise ontologies to give a structure to knowledge, database systems tenable to the FAIR principles, and software engineering to break down data processing into verifiable steps. To demonstrate our approach, we study the applicability of the Galvanostatic Intermittent Titration Technique on various electrodes. Our work is a building block in making automated material science scale beyond individual laboratories to a worldwide connected search for better battery materials.

Figures (7)

• Figure 1: Visualization of the 3D reconstructions of the (a) NMC active phase in the positive electrode and of the (b) graphite and (c) Silicon-Oxide phases in the negative electrode. Below those are the calculated particle size distributions for the (d) NMC, (e) graphite, and (f) Silicon phases.
• Figure 2: Finalized workflow for handling GITT and/or EIS data. Solid lines indicate a Record/dataset, while dashed lines indicate a Workflow/sub-task. The four bigger coloured sections represent the raw-to-interoperable data conversion (top left, blue), laboratory-to-interoperable report conversion (bottom left, green), discerning static and dynamic measurement features (top right, orange), and model parameterization (bottom right, red).
• Figure 3: Results for the diffusivity of the active material from one set of GITT data in delithiation direction, via direct calculations (a) and from fitting electrochemical models (b). The labels read as follows: $\Delta U_s / \Delta U_t$ refers to the original GITT method Weppner1977, $\Delta U_s / \Delta U_t (\Delta t \downarrow)$ refers to the same method applied to only a suitably small time segment (90), $\partial U_s / \partial \sqrt{t}$ refers to the differential formulation of the original GITT method, $\partial U_s / (\partial \sqrt{(t + \tau} - \sqrt{t})$ refers to a correction for overlapping relaxation phenomena Kang2021, $\partial \eta_s / (\partial \sqrt{(t + \tau} - \sqrt{t})$ additionally removes the OCP prior to diffusivity calculation, and SPM, SPMe, and DFN refer to the fitted electrochemical models. The best direct approach is plotted in black in (b) as well for comparison.
• Figure 4: The predictive parameterization posterior of a GITT measurement in delithiation direction. The highlighted square-root slopes $\gamma$ are used for fitting. The constant-current pulse lasts 0.6, and we show only the relevant part of the following rest. The square-root features used for parameterization are noted down for experiment (orange) and optimal simulation (green) in [power-half-as-sqrt]0.5. The large posterior 95 % confidence interval is a consequence of the non-matchable pulse square-root feature.
• Figure 5: A plot detailing the overpotential components in a GITT measurement in delithiation (a) and lithiation (b) direction. Only the two largest contributions are relevant in the delithiation direction, which are the OCP and particle concentration overpotential. The oscillation between the two is a result of a rapid change in OCP slope. All contributions are equally important in the lithiation direction. In particular, we see that the particle concentration overpotential shows a minor contribution overall, which makes this a measurement of the electrolyte rather than of the electrode.