Electric Vehicle Charging Scheme for a Park-and-Charge System Considering Battery Degradation Costs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it