A Study on the Impact of Driving Quality on the Energy Consumption of Plug-in Hybrid Electric Vehicles (PHEV)
Bibliographic record
Abstract
Plug-in hybrid electric vehicles (PHEVs) represent a critical technology for reducing emissions and enhancing energy efficiency, making their energy consumption assessment of paramount importance. This study investigates the impact of varying driving qualities on the energy consumption of PHEVs under the World Light Vehicle Test Cycle (WLTC). The assessment of driving quality adheres to the SAE J2951[1] standard. Through energy consumption tests conducted on a PHEV in pure electric mode under different driving qualities, six metrics were employed: Energy Rate (ER), Distance Rate (DR), Energy Efficiency Rate (EER), Absolute Speed Change Rate (ASCR), Root Mean Square Speed Error (RMSSE), and Inertial Work Rate (IWR). The results indicate that these metrics significantly reflect the impact of driving quality on energy consumption, with aggressive driving leading to higher energy usage. Although all driving qualities meet the requirements of the current Chinese energy consumption testing standard GBT 19753[2], the observed energy consumption differences due to varying driving qualities highlight the inadequacies of the current testing methods in evaluating and controlling driving quality. This underscores the necessity for improving energy consumption testing methods to more accurately assess the actual energy performance of PHEVs and to provide consumers with more reliable energy consumption information.
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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 | 0.002 |
| 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) | 0.001 | 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".