Multi-FPGA Co-simulation of SST and DAB in HIL: A Case Study for EV Charging Station Application
Bibliographic record
Abstract
In this energy transition era, the penetration of power electronic converters is increasing rapidly for diverse applications. High-fidelity Central Processing Unit (CPU) and Field-Programmable Gate Array (FPGA)-based solutions for Hardware-In-the-Loop (HIL) simulation are becoming obligatory for the study of these bulk power converters which encompass a high number of switches operating at very high switching frequencies. This paper presents a co-simulation of two solutions, i.e., a Modular Voltage Source Converter (MVSC) solver and Electrical Hardware Solver (eHS), with multi-FPGA-based real-time and HIL-based simulations to address the stated issue. The MVSC solver can handle large modular converters while eHS allows flexibility on converter topology. To show the flexibility and capability of these solvers, a DC-bus-based Electrical Vehicle (EV) charging station with Solid State Transformer (SST) and Dual Active Bridges (DABs) has been modeled in the real-time simulation platform, RT-LAB. Both converters, SST and DAB, have been simulated on a multi-FPGA solution. Both FPGAs communicate via Small Form-factor Pluggable (SFP) fiber optic using Aurora protocol. In addition, an external controller was used to control the DAB to demonstrate HIL operation. This high-fidelity simulation of an EV-charging station has been validated in the HIL testbench and results are presented in the paper.
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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".