Charge the North: findings from the complete data set of the world's largest electric vehicle study
Bibliographic record
Abstract
Charge the North is an electric vehicle (EV) study aimed at identifying the opportunities and challenges in EV load management through the collection of vehicle-side charging and driving data. Over the course of the project, FleetCarma, with the support of Natural Resources Canada and 10 Canadian electric utilities, collected driving data equivalent to a total of 12.4 million miles and 4,700 megawatt hours (MWh) of charging data through 1,000 EV owners across the nation. The report expands on findings from Charge North with data from 3,944 electric vehicles. This includes 40 EV makes and models, 10,010,535 charging slices grouped into 761,096 charging windows, 2.3 million hours of charging representing 8,576 MWh, and 28.9 million miles of driving data, making this the largest and most comprehensive up-to-date data set on EV charging. EVs on the road today are vastly different than they were as recently as five years ago. Long-range Battery Electric Vehicles (LR BEV), the most unpredictable and demanding class of electric vehicles, represented over 66% of all new EV sales in the US for 2019. These vehicles present a need for service territory-specific profiling studies on account of the changes in charging and driving behaviour they have incited. Service territory-specific load profiles and driving data will provide utilities with more insight into the areas where these risks are most likely. The increasing level of electric vehicle (EV) adoption and the advances in EV battery and charging technology are estimated to impact the distribution infrastructure.
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 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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".