Electric Vehicles: Impacts of Mileage Accumulation and Fast Charging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The impact of mileage accumulation and fast charging on driving range and battery energy of a light-duty battery electric vehicle (BEV), commercially available in North America, is being investigated. Two identical model BEVs are undergoing mileage accumulation on-road in Ottawa, Canada as well as testing on a chassis dynamometer in accordance with the SAE J1634 recommended test procedures. BEV1 is charged exclusively on DC fast-charging (DCFC) and BEV2 is charged exclusively on SAE AC Level 2 (ACL2). At the time of writing, the BEVs have been tested initially at 1,600 km, and then again after mileage accumulation to 15,000 km. Baseline results indicate that the two BEVs had a similar initial performance, and after 15,000 km the vehicles continue to have a similar driving range and useable battery energy despite the different charging methods. Both vehicles did, however, show decreased useable battery energy and recharge energy after 15,000 km of mileage accumulation and the resulting decrease in driving range varied between 0.4 and 13% depending on test conditions; these changes were not always statistically significant. Further testing is planned at approximately 15,000 km intervals up to 105,000 km. The next round of testing, at 34,000 km, will follow mileage accumulation at cold temperature, during an Ottawa, Canada winter.
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.
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 | 0.001 |
| 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 it