Evaluation of Real-World Fuel Consumption of Hybrid-Electric Passenger Car Based on Speed-Specific Vehicle Power Distributions
Bibliographic record
Abstract
Fuel consumption differs between the hybrid electric vehicle (HEV) and the conventional vehicle (CV). However, traditional fuel consumption models developed for CVs are commonly applied to HEVs, which leads to uncertainties in the quantitative evaluation of energy consumption for passenger cars in traffic road networks. Considering the internal combustion engine (ICE) operating modes of hybrid vehicles among varying vehicle specific power (VSP) demand, we present a method to incorporate the HEV ICE speed to develop speed-specific VSP distributions for real-world driving conditions. Using vehicle trajectory and fuel consumption data in real traffic conditions, the results of this study show that the application of methods developed for CVs leads to a significant underestimation of fuel consumption for HEVs when the average speed is in the high-speed range (over 50 km/h) and a significant overestimation of fuel consumption when the average speed is in the low-speed range (below 30 km/h). The average relative error of the measured fuel consumption factor in each speed bin is 7.1% compared with real-world observations, which is an unacceptably large error. This paper proposes a method to develop the speed-specific VSP distribution, considering whether the internal combustion engine (ICE) of HEVs is on or off. This approach reduces the average relative error of the obtained fuel consumption compared with real-world observations to 2.2%, and the measuring accuracy at different average speeds is significantly improved. This method enhances the functionality and applicability of the VSP theory-based traffic energy model for HEVs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".