Design and Control of a Multiport Bidirectional Converter for Fuel Cell Range Extended Vehicles With Onboard Solar Generation
Bibliographic record
Abstract
Though electric vehicles have the benefits of zero tailpipe emissions and convenient overnight charging, other challenges remain such as limited driving ranges, slow refueling while on-the-go, lithium supply issues, and emissions from some sources of electricity generation (e.g., coal). Fuel cell powered vehicles address the refueling time issue, which will also ease range concerns if hydrogen fueling stations are available. Furthermore, on-board solar generation can replace a portion of the vehicle’s grid charging needs and extend driving range. For both options, a smaller battery could be used, meaning less lithium is required. However, the power electronic architecture for such a solar fuel cell range extended vehicle (S-FCREV) would be complex and costly with conventional separate converters. Thus, this paper proposes the first practical multi-port converter that can perform all S-FCREV requirements with a low component count, including partial electrical isolation. This paper also proposes a simple Triple PWM and Triple Phase Shift (TPTPS) control scheme to enable flexible power flow between different power sources. A 3.3-kW prototype is designed, built, and tested to validate the topology and control. The experimental results show that the proposed topology achieves 97.8% peak efficiency with the proposed control.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".