Efficient Routing and Charging Strategy for Electric Vehicles Considering Battery Life: A Reinforcement Learning Approach
Bibliographic record
Abstract
The rapid evolution of battery technology has spurred the rise of electric vehicles (EVs). This paper contributes to the discourse on IoT applications in EV transportation systems, focusing on optimizing routing, energy management, and grid integration. Our study tackles the challenge of determining the most efficient route for autonomous EVs while accounting for battery charging requirements. Diverging from existing approaches, our work goes beyond mere minimization of travel time by incorporating considerations for maximizing battery longevity, thus enhancing overall electric vehicle efficiency. Recognizing the dynamic nature of EV operations, we model the problem as a Markov decision process and propose a reinforcement learning algorithm to address it. Through comprehensive analysis and evaluations across networks of varying scales, ranging from small to large node networks, we demonstrate the effectiveness of our proposed methodology. Moreover, we underscore its superiority in practical scenarios, emphasizing its potential for real-world deployment.
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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".