Efficiency Optimization for Blockchain-Enabled V2V Energy Trading With Dynamic Clustering Based on Deep Reinforcement Learning
Bibliographic record
Abstract
Vehicle-to-Vehicle (V2V) energy trading is a feasible solution to alleviate charging anxiety and enhance the range of electric vehicles (EVs). Clustering EVs can improve the efficiency of both energy transfer and V2V communication. However, in the complex large-scale Internet of Electric Vehicles (IoEV), the security and reliability of trading and communication, as well as the impact of cluster number and strategy on trading efficiency, require further discussion. This paper proposes a V2V energy trading system that leverages blockchain sharding and dynamic clustering to securely record energy trades as transactions on the blockchain. The system forms clusters using a clustering algorithm based on the mobility information and power levels of EVs, ensuring the reachability of energy trading. These trading clusters correspond to blockchain shards, enhancing transaction throughput through sharding scalability. Deep reinforcement learning (DRL) is employed to optimize the number of clusters, clustering algorithms, and parameters of the sharded blockchain system under security and latency constraints. The results demonstrate that the proposed scheme ensures timely power replenishment for low-power vehicles, improves overall economic utility and throughput for participants, and is effectively applicable to large-scale V2V energy trading scenarios.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".