Integrated Three-Port Converter for Solar-Charged Electric Vehicle Applications
Bibliographic record
Abstract
Adding onboard solar generation to electric vehicles (EVs) is one way to help reduce their charging needs and/or increase their driving range. To maximize the potential benefit, differential power processing (DPP) converters can be implemented to maximize solar energy capture in partial shading conditions. Furthermore, an isolated high step-up converter is required to transfer solar energy to the high-voltage battery, and a buck converter is useful to create an efficient direct path from the solar cells to the low-voltage accessory bus. If implementing these power electronic requirements with separate converters, the component count is high and power density can suffer. Thus, this article proposes the first multiport converter with solar DPP, an isolated high-voltage output port, and a low-voltage port, which is uniquely suited for solar-charged EVs. The switch count is low, and a simple control strategy is proposed to allow separate control of the two output ports. A 200-W experimental prototype is built and tested and shows a peak efficiency of 96.7% when solar power is flowing to both the high-voltage and low-voltage ports.
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 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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".