Design and Analysis of a Bi-Directional Solid-State Battery Protection System for Solid-State Transformer Based Grid Supporting Ev Charging Systems
Bibliographic record
Abstract
Solid-State Transformers (SSTs) offer higher power density and high-frequency galvanic isolation compared to traditional Low-Frequency Transformers (LFTs), making them ideal for grid-supporting Electric Vehicle (EV) charging applications. However, the demand for ultrafast battery protection in these systems has driven the adoption of solid-state circuit breakers (SSCBs). Despite their advantages, SSCB-based protection systems require further advancements to ensure reliability and efficiency. This research focuses on designing and analyzing an SSCBbased battery protection system for SST-enabled EV charging grids, addressing overvoltage and overcurrent fault scenarios. The proposed system integrates an SSCB with a SiC MOSFETenabled Dual Active Bridge (DAB) and incorporates a decisionmaking algorithm with false-tripping ride-through capability, accounting for key factors such as hysteresis currents. The performance of the DAB model with the SSCB is initially evaluated through MATLAB/Simulink simulations under overvoltage and overcurrent conditions at the battery-emulated load end. The simulated results are validated with hardware implementation using a SiC MOSFET-based SSCB. The experimental results achieved an ultrafast overvoltage detection in less than$490 \mu ~\mathrm{s}$results while the overcurrent protection system achieved ultrafast performance in less than$200 \mu$s which validated the ultrafast response of the proposed system. The findings demonstrate the system's robustness and effectiveness.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".