Electric Vehicle Charging Scheme for a Park-and-Charge System Considering Battery Degradation Costs
Bibliographic record
Abstract
This paper studies the electric vehicle (EV) charging scheduling problem of a park-and-charge system with the objective to minimize the EV battery charging degradation cost while satisfying the battery charging characteristic. First, we design the operating model of the system while taking the interests of both customers and parking garage into consideration. Subsequently, a battery degradation cost model is devised to capture the characteristic of battery performance degradation during the charging process. Taking into account the developed battery degradation cost model, EV charging scheduling problem is explored and a cost minimization problem is formulated. To make the problem tractable, we investigate the features of the problem and decompose the problem into two subproblems. A vacant charging resource allocation algorithm and a dynamic power adjustment algorithm are proposed to obtain the optimal solution of cost minimization. Several simulations based on realistic EV charging settings are conducted to evaluate the effectiveness and applicability of the proposed methods in discussed charging scenarios. Simulation results exhibit the superior performance of the proposed algorithms in achieving the most degradation cost reduction and the lowest peak power load compared with other benchmark solutions, which is beneficial for both customers and charging operators.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".