Optimization of Charging Strategies for New Energy Vehicles Based on Reinforcement Learning Algorithms
Bibliographic record
Abstract
With the popularization of new energy vehicles (NEVs) and the increasing severity of traffic congestion, charging difficulties have become a concern for people. Effective management and optimization of the NEV charging process have become increasingly important. This not only concerns the safety and stable operation of the power grid, but also directly affects the efficiency of road traffic, the utilization of renewable energy, and the charging experience and cost for users. The charging behavior of NEVs is a complex process that involves multiple considerations, such as grid load balancing, availability and efficiency of charging stations, user charging needs, and electricity prices. To address these issues, this paper proposes a NEV charging optimization strategy based on reinforcement learning (RL) algorithm, which can handle high-dimensional and complex environments and effectively deal with randomness and uncertainty factors. This strategy can not only reduce the load fluctuation of the power grid, improve the safety and stability of the power grid, but also reduce the charging time and cost of users, improve charging efficiency and user satisfaction. Meanwhile, by combining renewable energy, this strategy can also promote sustainable development, reduce reliance on traditional energy, and improve the utilization rate of renewable energy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".