Fastest Route and Charging Optimization of an Electric Vehicle With Battery's Life Consideration
Bibliographic record
Abstract
The tremendous development in battery technology has made the use of electric vehicles (EVs) a reality and the growing usage of autonomous electric vehicles (AEVs) over the past couple of years has posed many serious challenges. To date, much IoT research exists on EV transportation systems in general, particularly in the routing, energy, and grid system balance. In this context, throughout this paper, we revisit the task of finding the fastest path for AEVs. In contrast to the state-of-the-art, we explore the capabilities of minimizing the traveling time and maximizing the battery life for the effective utilization of electric vehicles. We first model the problem mathematically to obtain optimal solutions for small instances, and then owing to the complexity of the optimization model, we propose several heuristic algorithms to solve the problem on large instances. Our analysis and evaluations, which are based on small and large networks of nodes, demonstrate the effectiveness of our proposed approaches and which one is superior in practice. We believe that this study provides a very strong step towards finding the optimal usage of AEVs in terms of time and battery life alike.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".