Whole-Day Driving Prediction Control Strategy: Analysis on Real-World Drive Cycles
Bibliographic record
Abstract
The Whole-Day Driving Prediction (WDDP) concept is a unique plug-in hybrid electric vehicle (PHEV) control strategy that uses the day's planned travels to determine if a small range-extending engine should be turned on at the start of each day. This control strategy allows for the use of a very small engine, in contrast to commercially available PHEVs, which generally have engines large enough to propel the vehicle. This paper presents modeling and simulation results for WDDP vehicles on the real-world logged driving cycles of 100 drivers who were each logged for between 2 and 6 weeks. The simulation results show that with an engine size between 5 and 7 kW, the WDDP vehicle with a 35-kWh battery has similar range capabilities to a pure EV with a 60-kWh battery. The consequence is that purchase price can be decreased while keeping similar range performance, encouraging a higher market penetration of plug-in vehicles. The results of this paper show that the WDDP concept is viable for real-world use, and has the potential to reduce plug-in vehicle costs while achieving driving ranges similar to the new long-range EVs recently introduced to the market.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".