Smart Optimization of Dynamic Wireless Charging for Electric Vehicles Using GPS Intelligence and Machine Learning
Bibliographic record
Abstract
This study presents a robust, GPS-driven optimization framework for Dynamic Wireless Charging (DWC) in Electric Vehicles (EVs), targeting energy efficiency and intelligent route planning in urban settings. Using a simulated 50point drive between Halifax and Dartmouth, a Random Forest Regressor predicts energy consumption based on variables such as speed, elevation, and distance. To support timely charging decisions, a hybrid cost function selects the optimal station by minimizing a score based on geographic proximity and inverse charging capacity. Compared to a nearest-station baseline, the framework reduced charging time by $42 \%$, detour distance by $46 \%$, and improved remaining battery level by $133 \%$. Although trained on a synthetic dataset, the model captures realistic EV energy patterns. Due to the dataset’s limited size, extensive statistical validation (e.g., cross-validation or real-world generalization) was not performed. However, results aligned well with theoretical expectations. This work demonstrates how lightweight, machine-learning-based approaches can enable practical EV route optimization and sets the stage for future integration with live GPS telemetry, dynamic pricing, and realtime decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".