Optimization of PHEV Power Split Gear Ratio to Minimize Fuel Consumption and Operation Cost
Bibliographic record
Abstract
A Plug-in Hybrid Electric Vehicle (PHEV) is a vehicle powered by a combination of an internal combustion engine and an electric motor with a battery pack. The battery pack can be charged by plugging the vehicle to the electric grid and from using excess engine power. The research activity performed in this thesis focused on the development of an innovative optimization approach of PHEV Power Split Device (PSD) gear ratio with the aim to minimize the vehicle operation costs. Three research activity lines have been followed: * Activity 1: The PHEV control strategy optimization by using the Dynamic Programming (DP) and the development of PHEV rule-based control strategy based on the DP results. * Activity 2: The PHEV rule-based control strategy parameter optimization by using the Non-dominated Sorting Genetic Algorithm (NSGA-II). * Activity 3: The comprehensive analysis of the single mode PHEV architecture to offer the innovative approach to optimize the PHEV PSD gear ratio.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".