'Journey Mapping', re-defined drive cycle: an accurate vehicle performance prediction tool
Bibliographic record
Abstract
With the increasing popularity of hybrid electric vehicles (HEVs), updating their test procedures to have more accurate vehicle performance prediction has become essential. Traditionally, vehicles are tested using standardised drive cycles, which are not sufficient to represent real-life driving scenarios for different conditions that a vehicle might encounter during its life-cycle across all users. This results in high discrepancies between the predicted and the actual vehicle performance. As such, this study highlights the application of a novel concept called Journey Mapping (JM), which re-defines drive cycles to provide more realistic and accurate vehicle performance prediction, for studying a test HEV's performance. JM incorporates real-life conditions that might influence a vehicle during its journey from an origin to a destination. The JM model was able to predict the test HEV's performance with only about 2% error, on average, between the predicted and the actual performance, for the scope of this study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".