Cyberphysical Fault Detection for Electric Vehicle Platoons using Graph Convolution Network
Bibliographic record
Abstract
Smart city infrastructure requires increasing deployment of electric vehicle supply equipment (EVSE) to match projected electric vehicle (EV) adoption. However, connected EV platoons pose physical and cyber-risks to charging stations—and by extension the smart grid—by the nature of their size and network surface. This work tackles the problem prophylactically by proposing a health-monitoring technique for EV platoons to identify vehicles that have a battery fault or have been exposed to cyberattacks prior to charging. Our method uses measurements acquired during vehicle operation and leverages the underlying communication structure of the platoon by using a spatiotempo-ral graph neural network (GNN). The GNN utilizes alternating layers of spatial feature extraction using Chebyshev convolution and time-series feature extraction using the attention mechanism. We compare our results with those obtained using learning-based methods that do not exploit the graph structure of data and demonstrate that our spatiotemporal GNN outperforms the baseline models after training on cyberphysical fault scenarios. Additionally, we discuss our future work into the advantages offered by GNNs for platoons.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".