Plug-in vs. Wireless Charging: Life Cycle Energy and Greenhouse Gas Emission Analysis of an Electric Bus System
Bibliographic record
Abstract
Vehicle electrification through implementation of electric vehicles (EVs) with rechargeable batteries has the potential to significantly reduce the greenhouse gas emissions compared to a fleet of internal combustion engine vehicles (ICEVs). Wireless charging, as opposed to plug-in charging, is an alternative charging method for electric vehicles (EVs) with rechargeable batteries and can be applicable to EVs with fixed routes, such as transit buses. This thesis study adds to the current research of EV wireless charging by utilizing the Life Cycle Assessment (LCA) to provide a comprehensive framework for comparing the life cycle energy demand and greenhouse gas emissions associated with a stationary wireless charging all-electric bus system to a plug-in charging all-electric bus system. Life cycle inventory analysis of both plug-in and wireless charging hardware was conducted, and battery downsizing, vehicle lightweighting and use-phase energy consumption are modeled. A bus system in Ann Arbor and Ypsilanti area in Michigan is used as the basis for bus system modeling. Results show that the wirelessly charged battery can be downsized to 27-44% of a plug-in charged battery. The associated reduction of 12-16% in bus weight for the wireless buses can induce a reduction of 5.4-7.0% in battery-to-wheel energy consumption. In the base case, the wireless charging system is comparable to the plug-in charging system in terms of life cycle energy consumption and greenhouse gas emissions. To further improve the energy and environmental performance of a wireless charging electric bus system, it is important to focus on key parameters including carbon intensity of the electric grid and wireless charging efficiency. If the wireless charging efficiency is improved to the same level as the assumed plug-in charging efficiency (90%), the wireless charging system would emit 6.3% less greenhouse gases than the plug-in charging system. Keywords: Wireless charging; Plug-in charging; Life cycle assessment; Vehicle
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".