Channel characterization for power line communication in a hybrid electric vehicle
Bibliographic record
Abstract
In today's electric and conventional combustion engine vehicles, data communication between electronic control units is accomplished by sending communication signals over dedicated wires. The space requirement, weight, and installation costs for these wires can become significant, especially in electric vehicles (EVs) of the future, which are highly sophisticated electronic systems. The concept of reusing existing electricity wires, which are needed to power electronic components, for data communication, i.e., vehicular power line communications (V-PLC), is thus a promising means to reduce the amount of dedicated wiring and/or establish redundant communication buses especially for EVs. Previous work on V-PLC has mostly focused on combustion engine vehicles. In this paper, we present the methodology and results from a measurement campaign with the goal of characterizing the transmission conditions for V-PLC in a hybrid EV (HEV). Emphasis is given to the choice of measurement points (potential nodes of a V-PLC network) and the proper design of adapters for measurement equipment. The results presented here focus on channel transfer function and access impedance.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".