Optimized Interleaved Synchronous Buck Converter Design for a Two-Stage 3.6 kW Auxiliary Power Module in Electric Vehicles
Bibliographic record
Abstract
As the automotive market demands improved driving range in electric vehicles (EVs), highly efficient power electronics solutions are a focal point for researchers. Specifically, the low-voltage high-current nature of auxiliary power modules (APMs) with power ratings in the kilowatt range pose a challenge in hindering overall driving range of EVs. Three-port converters (TPCs) enable a highly efficient and power dense means of integrating the DC-DC portion of the on-board charger with the APM in a consolidated solution. To minimize the volume of the three-winding transformer, integrating the TPC, a two-stage APM is assumed in this article in which the second-stage consists of a synchronous buck converter (SBC) capable of 48 V to 9–16 V at 3.6 kW. With a maximum current of 250 A, phase interleaving of the SBC is necessary to improve overall power conversion efficiency. To ensure a sufficient balance of total volume and power loss, a design optimization framework for the SBC is presented in this article. Considerations of parameter variation, device and passive losses, and choice of non-coupled versus coupled inductors are the basis of the developed analytical models used in loss evaluation and inductor design. Results of the framework for 2060 unique designs are presented wherein trade-offs for three optimal designs are discussed yielding the final hardware demonstrator. Experimental results for power conversion efficiency are compared to the analytical model yielding a root mean square error of ~0.24% over the converter operating range.
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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 | 0.001 |
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".