Comparative Investigation of Series and Parallel Hybrid Electric Vehicle (HEV) Efficiencies Based on Comprehensive Parametric Analysis
Bibliographic record
Abstract
In order to assess, analyze, and cross-compare efficiencies of hybrid electric vehicles (HEV), a true drive train analysis needs to be executed. There exist two popular HEV topologies (series and parallel), with relatively large motors. Generally, the drive train efficiency can be simply yielded out by calculating the losses at each power stage in a series or parallel drive train structure. However, the power component stage-based analysis is a practically deficient method. In order to have a fair efficiency comparison, some parameters that directly affect fuel consumption, such as drive train mass and control strategy should be taken into consideration. This paper aims at modeling both the series and parallel HEV drive trains, and computing their definite drive train efficiencies, which can be used as a comparative scale for various other HEV topologies. The advanced vehicle simulator (ADVISOR) software is used for modeling, simulation, and parametric analysis of series and parallel HEV drive trains.
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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 | 0.003 |
| 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".