A Robust Cascade Controller Based Phase Shifted Full Bridge Converter for Electric Vehicle Applications
Bibliographic record
Abstract
Phase Shifted Full Bridge (PSFB) converter is one of the most popular DC-DC converters used in electric vehicles (EVs) due to simple design, high stability and zero voltage switching (ZVS) property. In this paper, cascade control architecture is integrated with PSFB converter operating in continous conduction mode (CCM) by designing controllers using the bode plot technique considering a simple resistive load. Small signal models for inductor current to duty cycle ($\mathrm{G}_{\text {id }}$) and output voltage to duty cycle ($\mathrm{G}_{\mathrm{vd}}$) are obtained using the state space averaging approach. By using the pole placement technique, controllers for based on $\mathrm{G}_{\text {id }}$ and $\mathrm{G}_{\text {vd }}$ are tuned. It is found that $G_{\text {id }}$ and $G_{\text {vd }}$ behaved like first order systems due to which a high bandwidth (BW) could be set. The developed controllers were tested for changes in the output voltage setpoints at $10 \mathrm{~V} / \mathrm{ms}$, load change at $10 \mathrm{~A} / \mathrm{ms}$ and input sinusoidal perturbations at different magnitudes and frequencies. The compensated system performed extremely well with a settling time ($t_{s}$) of 8 ms for output setpoint changes at 10 $\mathrm{V} / \mathrm{ms}$, overshoot in output voltage and $\mathrm{t}_{\mathrm{s}}$ of 650 mV and 10 ms respectively. For load changes at $10 \mathrm{~A} / \mathrm{ms}$, overshoot and $t_{s}$ were 650 mV and 10 ms respectively. Ripple tests were carried out at different magnitudes and frequencies to check the impact on low voltage side (LV). In addition, the controller was also tested for parametric variations in output inductor and capacitor.
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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) | 0.004 | 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".