Beschreibung
The rapid adoption of AI and data-driven approaches in battery research has brought a critical challenge into focus: the need for structured, interoperable data. Although battery-related data from experiments, simulations, and literature is increasingly available, it remains highly heterogeneous and difficult to standardize and reuse. Consequently, the performance and scalability of data-driven methods are often constrained by the quality, accessibility and interoperability of the underlying data. This presentation explores the data infrastructure needed to support robust data-driven battery modelling. In the context of the FULL-MAP project (GA No. 101192848), we address challenges related to data collection, organization and standardization across different sources and research workflows. Particular emphasis is placed on aligning data practices with the FAIR principles. A key outcome of this effort is the development of a Dataset Map, which offers a structured overview of available datasets, detailing their origin, format and suitability for modeling applications. The talk also presents ongoing work on the extension of the BattINFO ontology, which enables consistent, machine-readable representations of battery data. The presentation emphasizes that structured, interoperable, and semantically enriched data is essential for data-driven modelling and AI applications in battery research and discusses how well-designed data infrastructure can bridge the gap between data generation and advanced modelling approaches.| Zeitraum | 24 Juni 2026 → 25 Juni 2026 |
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| Ereignistitel | Digibatt Symposium |
| Veranstaltungstyp | Konferenz |
| Ort | Oslo, NorwegenAuf Karte anzeigen |
| Bekanntheitsgrad | International |
Research Field
- Vehicle System Simulation