Multiport DC-DC Converter for Integrating Energy Systems in All-Electric Vehicles
Bibliographic record
Abstract
In this paper, a new non-isolated multiport dc-dc converter (MPC) of non-inverting buck-boost configuration is proposed for integrating multiple energy resources in automotive applications. A typical example of such automotive application is an electric vehicle (EV), powered by one or more renewable energy sources (RESs) and consisting of one or more energy storage systems (ESSs), e.g. batteries and supercapacitors. The inputs to the MPC are clustered based on source or storage and integrated using uni- or bi-directional switches, respectively. It is capable of bi-directional operation between the storage cluster and the dc link, allowing for a simultaneous transfer of energy from more than one source of varying voltage levels (irrespective of its' cluster) to the dc link. The proposed MPC is analysed for four inputs, comprising of two per cluster in this paper. As compared to existing MPCs in literature, the proposed converter utilizes a fixed number (two) of inductors and is robust such that it requires only one additional switch to integrate any extra energy storage or source in a respective cluster. Different operating modes of the proposed MIC are numerically verified and validated on OPAL-RT's OP5700 hardware-in-the-loop (HIL) platform.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".