Assessment of Level 1 and Level 2 Electric Vehicle Charging Efficiency
Bibliographic record
Abstract
North American plug-in electric vehicle (EV) sales are projected to grow steadily in the next decade, and EVs are expected to become a significant portion of the vehicle feet. Widespread electrification of personal transport will require coordination between the electricity and transportation sectors. The coordination could include application of electric efficiency incentives that are commonly used in the energy sector for more efficient products, such as energy star appliances. The use of incentives for more efficient vehicles and charging equipment will facilitate a faster transition to this transformative technology. If EVs and electric vehicle supply equipment (EVSE) are found to be eligible for incentives, electric utilities could create programs that would accelerate EV deployment. The efficiency of 120-V Level 1 and 240-V Level 2 EVSE was compared with FleetCarma logger data collected from 1,008 Chevrolet Volt charging events. On average, Level 2 charging was 3% more efficient than Level 1 charging, but this percentage increased with shorter charge times. When less than 2 kW-h was drawn from the grid, Level 2 charging was 13% more efficient than Level 1. Although Level 2 charging was more efficient at all temperatures, the differences in efficiency were greater at high (above 708F) and low (less than 538F) temperatures. The greatest efficiency gains for Level 2 charging are expected at public charging locations, where charge times tend to be shorter and weather conditions more variable, as opposed to residential sites. Providing incentives for efficient EV charging infrastructure through utility and government programs will ensure optimal long-term investment in this new technology, reduce energy use, and facilitate more rapid uptake of EVs. EV charging infrastructure is eligible for federal funding under the Moving Ahead for Progress in the 21st Century Act. Thus, transportation agencies could include efficiency benchmarks as a requirement for project financing.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".