An efficient soft switched DC-DC converter for electric vehicles
Bibliographic record
Abstract
This paper presents a new technique to improve the efficiency of the ZVS full-bridge dc-dc converter used to process the power between the high voltage traction battery and the 12V utility battery in a Plug-in Hybrid Electric Vehicle (PHEV). Efficient operation of the converter is crucial in order to maintain the energy of traction battery for a longer time and for increasing driving distance. Light load efficiency of the dc-dc converter is especially important because this converter is lightly loaded most of the time while the car is being driven. The passive asymmetrical auxiliary circuit used to extend the soft switching range, produces extra circulating currents that increases conduction losses. A new technique for controlling circulating currents in the auxiliary circuit is introduced that with a small increase in controller complexity, reduces conduction losses and improves the converter efficiency especially at light load. By proper duty cycle control of the full bridge switches, auxiliary circulating currents are reduced to the minimum possible values required for ZVS. While phase shift angle mainly serves as the output regulation control parameter, duty cycle is varied to keep converter in the soft switching region with minimum conduction losses. Theoretical analysis and operating principles as well as soft switching operation are discussed. Experimental results for a 2KW converter are presented that validate the significant improvement in efficiency and considerable saving of valuable energy storage.
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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 | 0.000 | 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 | 0.000 | 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".