Toward Anomaly Representation in Lithium-Ion Batteries: An Ontology-Based Approach.

In today's energy-dependent world, ensuring the safety and efficiency of lithium-ion batteries is crucial. Early representation of anomalies becomes essential for optimizing performance, reducing disruptions, and prolonging battery lifetime in electric vehicle applications. This objective necessitates the integration of data from distributed and heterogeneous sources, a challenge traditionally tackled by semantic web technologies. In response, this paper introduces an ontology-based model that capitalizes on representing anomalies in lithium-ion batteries. Ontologies play a vital role in representing knowledge in a machine-interpretable format. Our approach enriches sensor data with contextual information, employing structured concepts, rules, and semantics specifically designed for representing anomalies in lithium-ion batteries. [ABSTRACT FROM AUTHOR]