Uncovering Patterns in Electric Vehicle Charging: A Data-Driven Approach
Bibliographic record
Abstract
According to the International Energy Agency, the worldwide count of electric vehicles is projected to surpass 130 million by 2030. Over the past 10 years, this growth has led to much research on electric vehicles, ranging from preliminary studies and experimental testbeds to data analysis. This study examines the charging behavior of electric vehicle users using the Caltech JPL site data set. Using several methods, such as the intersection-based clustering algorithm and more well-known ones like k-means and ward linkage, user behavior patterns are examined focusing on connection time, session duration, and energy delivered. Four distinct user groups with unique charging patterns and energy demands are identified. Notably, afternoon users exhibit a 23–77 % split in high energy demand, with early afternoons being the peak period. Encouraging high-demand users to charge in the morning and on weekends would optimize the operation of the charging networks. These findings have important implications for shaping electric vehicle charging infrastructure, grid management, and energy distribution.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".