Ontology-Driven Metrology Data Management for Wireless Charging, Battery Management and Predictive Maintenance in Electrical Vehicles
Bibliographic record
Abstract
The growing adoption of electrical vehicles (EVs) requires efficient data management for charging, battery performance, and predictive maintenance. This paper introduces an ontology-driven framework to ensure interoperability across diverse data sources using semantic web technologies. AI-powered predictive analytics enhance battery health monitoring and maintenance, while cybersecurity measures protect metrology data. By unifying domain-specific and agnostic ontologies, the proposed system enables seamless integration of heterogeneous datasets, improving real-time decision-making. The research demonstrates how semantic technologies, AI diagnostics, and secure data architectures enable a scalable, intelligent metrology ecosystem, advancing sustainable mobility, safer charging, and battery management. Experimental evaluations show that our predictive models achieve a battery health estimation$\mathbf{R}^{\mathbf{2}}$of$\mathbf{0. 9 9 1 3}$(XGBoost) with 233.78 W power consumption, which is suitable for real-time edge deployment, while maintaining 97% accuracy in identifying low RUL symptoms through semantic-enhanced AI analytics. These results highlight the practicality and efficiency of the proposed framework in modern EV systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".