Dynamic Energy-Aware EV Charging Navigation in Interacting Transportation and Distribution Networks
Bibliographic record
Abstract
With electric vehicles (EVs) and charging facilities as a bridge, the coupling of transportation network (TN) and distribution network (DN) is getting closer, and EV charging navigation considering TN-DN convergence is a current research hotspot. In order to reduce the total cost of EV charging navigation and the impact of charging load on the grid, this paper proposes a novel bi-layer coordinated charging navigation model (Bi-CCNM). The upper layer model is to minimize the total cost of EV charging navigation. To precisely estimate travel costs, a dynamic spatio-temporal energy consumption estimation model is established, which considers the impact of dynamic traffic flow on EV travel resistance. The lower layer model aims to reduce energy exchange between the charging station (CS) and the main grid, and maximize the utilization of local renewable energy sources. To cope with the intermittent nature of renewable energy generation, this paper utilizes Vehicle-to-Grid (V2G) technology to effectively mitigate the impact of EV charging loads on the grid. To efficiently tackle the Bi-CCNM, the Joint Optimization algorithm combining Generalized Benders Decomposition and Logarithmic Barrier Function Method (JO-GBLB) is developed. Ultimately, an optimal solution can be obtained through the interaction of information between the two layers. Real-world case validates the effectiveness of the proposed the Bi-CCNM, energy consumption estimation model, and JO-GBLB algorithm. The results indicate which it provides a low-cost charging navigation solution while significantly reducing the power exchange between the CS and the main grid, effectively preventing safety issues caused by load fluctuations. Besides, the accuracy of energy consumption estimation of EVs increases by 5.1% - 6.7%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".