Well-to-wheels energy efficiency analysis of plug-in electric vehicles including varying charging regimes
Bibliographic record
Abstract
ABSTRACT \nWell-to-Wheels Energy Efficiency Analysis and Optimal Energy Management Strategy for Varying Electric Vehicle Operational Characteristics \nEbrahim Saeidi Dehaghani \nTransition to electric vehicles (EVs) is already under way. EVs were demonstrated to be the most fuel economic and emission free among other propulsion technologies. Electric and plug-in hybrid electric vehicles (EVs/PHEVs) can have a large impact on greenhouse gases (GHGs) reduction, increase in fuel economy and higher fuel efficiency. This thesis seeks to investigate the Well-to-Wheels (WTW) energy efficiency analysis of Electric Vehicles (EVs) in Canada. The main idea behind this research work is to analyze step by step energy efficiency, which is one of the key factors for EVs technology acceptance. Penetration of battery electric and more electric vehicles (BEVs/MEVs) into vehicle fleet, affects load demand as well as electricity market. Smart charging of EVs can remove a lot of stress from electricity grid. Effect of home charging of EVs/PHEVs on electricity demand in the province of Quebec was analyzed. More recently, EVs have been looked at as distributed sources of energy, whereby they could back up the power grid during critical high demand periods. With the help of an on-board battery pack, EVs can act as distributed generators and feedback energy to the AC grid. However, efficiency of energy conversion could become an issue in this power flow. Hence, in this thesis stage-by-stage efficiency of vehicle-to-grid (V2G) power flow was evaluated. In addition, feasibility of using EVs in international islanding to sustain the local grid in the event of an emergency was analyzed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".