Charging-Related State Prediction for Electric Vehicles Using the Deep Learning Model
Bibliographic record
Abstract
Electric vehicles (EVs) are becoming the potential contender for the conventional gasoline vehicles in view of the environment-friendly and energy-efficient characteristics. The prediction of EV charging-related states (defined in this study as home charge, outside charge, home stop, outside stop, low-battery travel, and high-battery travel) could help to identify the future charging demand (power consumption) of EV individuals. Specifically, it could guide the operation and management of charging facilities and also provide tailored charger availability information based on users’ real-time locations. This study aims to predict charging-related states of individual EVs using a deep learning approach. We first propose a tangible approach to convert EV trajectory data into state sequences and then develop a bidirectional gated recurrent unit model with attention mechanism (Bi-GRU-Attention) to forecast EV states. A sensitivity analysis is conducted to tune and/or calibrate parameters in the model based on plug-in hybrid EV trajectories dataset collected in Shanghai, China. Experiment results show that (i) the proposed method could achieve an average accuracy of 77.15% with a 1-hour prediction length and it outperforms the baseline models for all tested prediction lengths; (ii) it is also revealed that the prediction accuracy varies dramatically with different states and time periods. Among all states, the proposed model has a higher prediction accuracy on “home stop” (89.0%). As for time periods, the EV states around 08:00 am and 04:00 pm are hard to predict, and a comparatively low prediction accuracy (close to 60%) is obtained; and (iii) the stability and robustness analysis implies that the proposed model is stable and insensitive to SOC noise or season.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".