Over the past decade, the exponential growth of the Electric Vehicle (EV) industry has experienced unprecedented surge and diversification. This multifaceted field results in building comprehensive, cross-domain, extensible, sophisticated knowledge management systems that incorporate future needs and address battery-related information integration challenges. It needs sophisticated knowledge representation techniques such as ontologies and knowledge graphs (KGs) leveraging federated approaches to integrate diverse, disparate data from distributed sources, resolve data interoperability challenges, and also present in a standardized format to build various battery-related services on top of virtual data integration layer, without the need for data materialization. We propose the state-of-the-art federated virtual knowledge graph (FVKG) framework embedded with the virtualized knowledge graph (VKG) methodology to handle the auspicious challenges effectively across distributed environments. The suggested FVKG framework offers a unified view of scattered data sources and different models to create a virtual data federation leveraging Ontop, resolving data bottlenecks efficiently. The FVKG assists in automated data mapping from diverse, relational sources, enabling intuitive queries based on domain-centric federated ontology and loads into the VKG intelligently. The FVKG utilizes a virtualized technique to reduce data migration, guarantees low latency and freshness, and facilitates real-time access while upholding integrity and coherence throughout the federation system. The FVKG incorporates ontology-based data access (OBDA) to build a monolithic ontological model, integrating ontology-driven artifacts and ensuring semantic alignment using schema mapping techniques. As a result, the FVKG targets enabling more efficient battery performance analysis, predictive maintenance, and strategic decision-making in the EV ecosystem.