Efficiency and suitability analyses of varied drive train architectures for plug-in hybrid electric vehicle (PHEV) applications
Bibliographic record
Abstract
As is well known, plug-in hybrid electric vehicles (PHEVs) are predominantly dependant on the energy storage system (ESS), compared to regular HEVs. Although the series PHEV topology has been recently targeted as the prime choice for PHEV applications, it is unclear as to whether or not it is indeed the most efficient option. This paper models PHEVs in both series and parallel structures by using the forward/backward modeling method, to determine the most efficient drive train architecture for future PHEV applications. In general, 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 different control strategies should also be taken into consideration. This paper aims at modeling both the series and parallel PHEV drive trains, and further computing and comparing their absolute drive train efficiencies. Furthermore, the resultant ESS charging profiles, discharging profiles, as well as roundtrip efficiencies are calculated and studied.
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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 | 0.001 |
| 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".