Advantage and feasibility of wireless charging electric bus systems
Bibliographic record
Abstract
The electric vehicles can be charged through plug-in chargers but there are challenges such as heavy battery packs (e.g.electric buses with large batteries), and high battery costs.An alternative charging method of wireless charging where wireless power transfer technology is applied may overcome the problem with plug-in charging.Due to limited operational ranges of battery-electric buses, two range remedy methods are available: (a) regular plug-in battery charging with backup vehicles; (b) en-route wireless charging during service where wireless charging takes place while a bus is loading and un-loading passengers.Thus, costly backup vehicles could be eliminated and battery packs can be downsized as well.This paper compares two charging scenarios plug-in charging and stationary wireless charging for all-electric bus systems and compare them to conventional diesel buses, with respect to costs, battery downsizing potential and energy consumption rates.A model is developed to evaluate plug-in and wireless charging electric bus systems and conventional diesel bus systems.A city's transit bus system is selected for a case study on the plug-in charging and stationary wireless charging systems, together with diesel buses.The plug-in charging and stationary wireless charging systems are modelled through the case study.The wirelessly charged battery for electric buses can be downsized by 46% of the plug-in charged battery, thus significantly decreasing the cost and weight of battery packs for electric buses.Energy consumption rates for wirelessly charged buses also decrease, resulting from reduced bus weight.Simulation results showed that if 10% vehicle mass reduction is achieved by implementing wireless charging, energy consumption of electric buses can be reduced by 5.5%.In addition, wireless charging systems have the advantages of increased safety and city aesthetics, and the potential to make road transportation more intelligent.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".