Knowledge representation of battery manufacturing supported by an integrated ontology system
Year: 2025
Type: article
Abstract: The fast-moving field of battery technologies including variations of materials and manufacturing processes requires decision support that is based on a knowledge base that represents and integrates the multiple chemistries, processes, data sources (e.g. sensors), characterisation methods etc. Moreover, a digital twin approach also needs to relate the virtual representation to “real” data. We present a single conceptual framework of all the diverse knowledge sources relevant to constructing a battery Digital Twin in the BatCAT project. The project compiled an extensive set of competency questions and relevant metadata and mapped them to existing ontologies, in particular to the growing ecosystem of EMMO and its domain ontologies, including CHAMEO, the Electrochemistry Ontology and the Battery Domain Ontology. In addition, it was found that the Sensor Network Ontology can easily be aligned, based on conceptualisations of key classes and object properties that are similar to EMMO. Further ontologies that can be utilised going forward include the Chemical Substance ontology and the Battery Value Chain Ontology. As a result, the BatCAT ontology requires very few additional concepts, ensuring semantic interoperability in the battery domain. The Digital Twin is represented in EMMO by two interconnected semiotic triangles.
Author: Goldbeck, Gerhard
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949698483
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