A New Control Approach Based on the Differential Flatness Theory for an AC/DC Converter Used in Electric Vehicles
Bibliographic record
Abstract
AC/DC converters used for charging high-voltage battery banks in electric vehicles from the utility mains, generally, consist of two stages. The first is a power factor correction (PFC) ac/dc boost converter to reduce the input current harmonics injected to the utility grid and convert input ac voltage to an intermediate dc voltage (dc-bus voltage). The second part is an isolated dc/dc converter for providing high-frequency galvanic isolation. This paper presents a novel intelligent control law based on the differential flatness theory to control the input power of the PFC stage which is determined by the charging characteristics of the high-energy battery bank, instead of controlling the intermediate dc-bus voltage at a constant value as done in the conventional controller. Application of the proposed control law to such an ac/dc converter helps improve the dynamic behavior of the input PFC stage compared to the conventional controller and also achieve load adaptive regulation of the intermediate dc-bus voltage. Such load-adaptive dc-bus voltage regulation allows the dc/dc full-bridge converter to operate optimally from no-load to full-load conditions unlike the conventional controller with constant dc-bus voltage which forces the dc/dc full-bridge to operate with very low duty ratios at no-load conditions. Experimental results from a 3-kW ac/dc converter are presented in the paper to validate the proposed control method. The improved converter performance and increased efficiency as compared to the conventional control method proves the superiority of the proposed technique.
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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) | 0.000 | 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) | 0.001 | 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".