Modified High Frequency LCL Resonant Converter for E-Mobility Applications
Bibliographic record
Abstract
Owing to the CC mode restrictions of LLC converters and the control complexity involved, the LCL resonant network has gained popularity as a battery charging solution. Regardless of changes in the load, the LCL immittance converter functions as a continuous current source at resonance frequency. This feature has been used in a variety of ways on the transformer's primary or secondary side to accomplish soft switching and CC charging at operating frequencies near to 100 kHz. Large magnetics size in LCL converter topologies around 100 kHz restricts magnetic integration. The LCL-T resonant based DC-DC converter at 500KHz resonant frequency is examined in this work. To lower the value of the resonant inductor, an effort has been made to incorporate the transformer's leakage inductance into the resonant network. Since all of the switches in the converter operate at a set frequency, there is no control complexity and the converter exhibits a notable reduction in magnetic size at high frequencies. ZVS is achieved in all switches over the whole load range by the converter in full bridge topology. After a specific load value, the suggested converter shows a transition from constant current mode to constant voltage mode. In the converter study, the battery is modelled as a variable resistive load. A 2-kW system with a 400V input is designed and simulated in MATLAB/SIMULINK environment. The resonant network's frequency response at 500 kHz has been plotted using LTSpice. Simulation findings validate the converter attaining ZVS in all the switches in CC-CV charging mode.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".