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 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) | 0.001 | 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 teacher head, 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".