Design and Validation of a Bidirectional DC-DC Converter Control for Electric Vehicles Using FPGA-in-the-Loop Methodology
Bibliographic record
Abstract
Electric vehicles (EVs) are an alternative to fossil-fuel-powered vehicles. However, high prices make them inaccessible for mass market adoption. Power electronics are a key enabler of vehicle electrification. In this work, the design of a bidirectional converter control is performed for application in EVs. A bidirectional topology with step-up and step-down capabilities is designed. Proportional-Integral-Derivative (PID) is the elementary control method and the most popular in power converters due to its ease of implementation, scalability, low hardware-resources requirement, and high switching frequency capability. New Wide Band Gap semiconductor devices allow to increase switching frequency to reduce the size of the converter. Design validation through in-the-loop methodologies verify that algorithms are ready for chip deployment identifying design flaws early through its development. In this work, the control algorithm for a bidirectional DC-DC converter employing WBG devices at EV power ratings is implemented. Step-up and step-down modes of operation in a cascaded bidirectional topology are analyzed with input voltage of 400 VDC, 13kW power rating, 500 kHz switching frequency and FPGA-in-the-loop (FIL) validation. FIL methodology proved a cost-effective approach to verify that control algorithms are capable for hardware deployment without the need for expensive hardware setups.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".