Electric Vehicles: Impacts of Mileage Accumulation and Fast Charging
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".