Battery life impact of vehicle-to-grid application of electric vehicles
Bibliographic record
Abstract
In recent years, electric vehicles (EVs) have successfully been gaining a market foothold in many countries around the world. The number of plug-in vehicles is forecast to grow steadily to over 10 million vehicles before 2024. In many countries, concerns about the environmental impact of fossil fuel-based electricity production have resulted in a shift towards more sustainable power generation technologies. Electricity production from solar and wind, however, is intrinsically unsteady. Costly provisions therefore need to be made within electricity grids to ensure back-up power is available on demand. The power stored in the batteries of the growing fleets of EVs could potentially be a cost effective alternative to conventional spinning reserves through Vehicle-to-Grid (V2G) applications. Concerns arise over the use of EVs for V2G purposes; as such use is expected to negatively impact EV battery life. Data on additional battery degradation due to V2G is presently quite scarce. A detailed simulation model was developed to explore the various processes that impact EV battery life. The model was used in a simulation study to determine the battery life impact of various V2G scenarios in comparison to a base case of regular driving and charging. Scenarios with different driving styles, various charge levels and different V2G events were evaluated. The impact of fast charging of EVs on battery life was also considered. The study concluded that aggressive driving and fast charging have a great impact on EV battery life. A similar level of battery degradation was found for intense participation in V2G services, fully discharging the battery on a daily basis. However, less intense use of the EV battery can still provide useful V2G services with acceptable battery degradation. Manufacturers of EVs are therefore encouraged to implement V2G capability in their EVs.
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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 | 0.001 |
| 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 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".