Improving the Regulation Range of EV Battery Chargers With <italic>L3C2</italic> Resonant Converters
Bibliographic record
Abstract
The demand for electric vehicles has expanded rapidly for both industrial and transportation applications. In parallel, new battery technologies capable of deeply discharging and powering electric vehicles over long periods of time have been introduced and made available in the market. Due to the increasing complexity of charging algorithms, battery chargers are exposed to demanding operating requirements. Chargers should not only work under different loading conditions (from absolutely zero to maximum output power) but also regulate a wide output voltage range (from near zero to 1.5 times nominal voltage). In this paper, a multiresonant L3C2 resonant converter is introduced that can cover nearly all of the regions in the battery V-I plane, from near zero output voltage, zero output current to maximum output power. By adding one capacitor, the topology is able to extend the operating frequency beyond the LLC topology and achieve significant regulation improvements. Near free-ripple charging current for batteries charging is achieved in different states of charge without using burst mode operation, effectively increasing the battery life cycle. In addition, soft transitions are obtained for all the switches (MOSFETs and diodes) resulting in high efficiency, reliability, power density, and low-noise operation of the charger. A complete set of simulation and experimental results, extracted from a 96-VDC 950-W L3C2 resonant converter, demonstrates the characteristic features of the proposed topology for battery charger applications, while providing a comparative example with the LLC topology.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".