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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".