Modelling the stochastic charging behaviour of electric vehicles: a validated framework with user charging data
Bibliographic record
Abstract
This study develops a stochastic model with Phase-Type distribution capable of representing EV charging occurrences across structured, segmented temporal increments. The model is used to estimate the users' stochastic behaviours of charging occurrences, and its performance is validated via a real-world dataset of approximately 320, 000 charging sessions recorded over the span of one year. Key temporal variables are identified that significantly influence the frequency of charging events. Additionally, charging durations are modelled using a separate stochastic process to capture the essential characteristics of this variable. Results indicate that month-to-month charging activity is not strongly dependent, and charging activities tend to cluster in short time windows of 1 to 2 hours. Statistical validations further reveal the robust and stable performance of Phase-Type distribution. These emphasise the critical role of stochastic modelling of electric vehicle (EV) charging behaviour in enhancing the efficient management of underlying infrastructure, providing methodological insights and practical implications in deriving EV charging impacts on infrastructure and grid.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".