Design and Control of Split-Pi DC-DC Converter for Vehicle to Grid and Grid to Vehicle Applications with Development of Energy Management System in MATLAB/Simulink
Bibliographic record
Abstract
Electric vehicles (EVs) are very useful for reducing carbon emission and energy-efficient transportation.Green energy and minimization of emissions are the needs which are regularly thriving automakers to produce electric transportations.Electric vehicles market is highly increasing day by day and its share will be growing even more higher in the upcoming future.To build up EV battery chargers need AC-DC converters and DC-DC converters.EV chargers can optimize vehicle-to-grid (V2G) and grid-to-vehicle (G2V) operations through properly using bidirectional DC-DC converters.The Split-Pi converter is a recently invented DC-DC converter that can support V2G and G2V operation with its bidirectional functionalities.This paper presents a detailed analysis and control of Split-Pi converter for V2G and G2V operation, and development of energy management system.The energy management combination of Lithium-Ion batteries and supercapacitors in EVs can minimize cost, maximizing its range, efficiency and reliability.The EV charging system employing Split-Pi converter analyzed for V2G and G2V applications has been designed in the MATLAB/Simulink platform.Although many topologies and ideas are modified regarding those applications, there are still some processes to identify the new methodologies.Split-Pi converter-based battery and energy management system must be taken into consideration to prevent battery problems such as battery aging, power losses, and slow charging.Both battery lifetime and efficiency can be improved by this way.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".