Wireless Opportunity Charging as an Enabling Technology for EV Battery Size Reduction and Range Extension: Analysis of an Urban Drive Cycle Scenario
Bibliographic record
Abstract
Opportunity charging of electric vehicles (EVs) during brief stops is an important application of wireless power transfer (WPT). Irrespective of the specific WPT technology used, it is possible to quantify the effect of opportunity charging on EVs using energy calculations. This paper presents an analysis of the potential reduction in battery size and extension in EV range enabled by opportunity charging, using urban driving cycle data and various charging power levels. Traction power expended for acceleration, and to overcome air drag and rolling friction are considered. Depending on the extent of opportunity charging, battery size reduction from 6% to 85% is possible. Alternatively, retaining the battery size at its base value, a range extension between 7% and 600% is realizable. Although the results are shown for a particular velocity profile, the generalized analysis method presented in this paper can cater to various types of driving cycles.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".