Performance of Fast Electric Vehicle Charging Stations by Energy Storage Devices based Grid Connected Hybrid Renewable Energy Sources
Bibliographic record
Abstract
Electric Vehicles (EVs) are becoming a trending concept in present research fields. Many kinds of EVs are developed worldwide and are running successfully on roads. Hence, charging stations must be established at many places and also highly required to improve their performance as well as maintenance. An efficient as well as fast charging station is proposed in this paper where powered by grid connected hybrid renewable energy sources. A detailed mathematical analysis is compressed to make this work more effective. Loads at different locations across the south India is considered while developing the proposed method. Considering the availability of energy resources in those regions, their dynamism, and techno-economic feasibility, a hybrid grid-connected renewable energy system, complete with a battery storage unit and power conversion module, has been designed for the proposed charging stations. These sites/places are also connected to ecofriendly EV charging stations, which come with various incentives. The optimization results indicate that after incorporating on-grid hybrid renewable energy resources, there is a significant reduction in key performance metrics. Specifically, the Levelized Cost of Energy (LCOE) has decreased by 87.50%, and the Net Present Cost (NPC) has dropped by 95.26%. This translates to a reduction in LCOE from ${\$}$ 0. 0 3 3 8 4 per kWh to ${\$}$ 0. 0 0 4 0 7 per kWh, and a decrease in NPC from ${\$}$0.09172 to ${\$}$0.00435 million, respectively.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".