Demonstrating Linked Battery Data to Accelerate Knowledge Flow in Battery Science
Dechent P, Barbers E, Clark S, Lehner S, Planden B, Adachi M, Sauer DU, Howey DA, Paarmann S (2026)
Publication Language: English
Publication Type: Journal article, Original article
Publication year: 2026
Journal
URI: https://sciencedirect.com/science/article/pii/S001346862602284X?via%3Dihub
DOI: 10.1016/j.electacta.2026.150404
Abstract
More than 20,000 papers mentioning 'lithium-ion battery' were indexed by OpenAlex for 2025 alone, far more than any researcher can read. This paper discusses strategies based on structured, semantic, and linked data as well as Large Language models (LLM) to manage this information overload. Structured data follows a predefined, machine-readable format; semantic data includes metadata for context; linked data references other semantic data, forming a web of interconnected information. We use the battery-related ontology BattINFO, to standardise terms and enable automated data extraction and analysis. We compiled a corpus of more than 450,000 battery publications, converted manufacturer data sheets of more than 380 commercial cells into BattINFO-based JSON-LD, and linked each cell to the publications that tested it. Although only a first step, we show how this approach makes literature-based battery research considerably faster, and we invite the community to contribute. We provide the structured data and the tools to access them as open source.
Authors with CRIS profile
Involved external institutions
Forschungszentrum Jülich / Research Centre Jülich (FZJ)
Germany (DE)
University of Oxford
United Kingdom (GB)
Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen
Germany (DE)
SINTEF Energy Research / Sintef Energi AS
Norway (NO)
Helmholtz-Institut Münster (HI MS)
Germany (DE)
Germany (DE)
University of Oxford
United Kingdom (GB)
Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen
Germany (DE)
SINTEF Energy Research / Sintef Energi AS
Norway (NO)
Helmholtz-Institut Münster (HI MS)
Germany (DE)
How to cite
APA:
Dechent, P., Barbers, E., Clark, S., Lehner, S., Planden, B., Adachi, M.,... Paarmann, S. (2026). Demonstrating Linked Battery Data to Accelerate Knowledge Flow in Battery Science. Electrochimica Acta. https://doi.org/10.1016/j.electacta.2026.150404
MLA:
Dechent, Philipp, et al. "Demonstrating Linked Battery Data to Accelerate Knowledge Flow in Battery Science." Electrochimica Acta (2026).
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