The impact of EV battery cycle life on charge-discharge optimization in a V2G environment
Bibliographic record
Abstract
In addition to improving the ground transport and the environment through reduced greenhouse gas emissions, Electric vehicles (EVs) can support a number of power grid services through the Vehicle to Grid (V2G) system. If properly integrated, EVs can help in integrating renewable energy sources, providing various demand response and ancillary services. There are a number of challenges that should be addressed before EVs can effectively provide these services. The main challenge is the availability of power from EVs. This challenge is related to the EV battery capacity, the available power at the time of need and the battery cycle life. Battery cycle life is inversely proportional to the number of charge-discharge cycles the battery goes through. Therefore, the battery cycle life and the cost of degradation should carefully be included when optimizing the V2G operation. In this paper, we develop a novel iterative algorithm to predict the effect of static and dynamic electricity and regulation prices on the battery cycle life. We optimize the charge-discharge process by considering frequency regulation signals, the day ahead real time pricing and the predicted cycle life. We develop a case study for the charge scheduling problem using actual frequency regulation and hourly electricity pricing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".