Modular Multiport Electric-Vehicle DC Fast-Charge Station Assisted by a Dynamically Reconfigurable Stationary Battery
Bibliographic record
Abstract
Multiport battery-assisted dc fast charger (BA-DCFC) stations enable high charge rates of electric vehicles (EVs) at sites that have insufficient grid capacity. However, BA-DCFCs are expensive and inefficient due to the battery energy storage system (BESS) and high-power isolated dc–dc converters that interface the BESS and EV charging ports. This article presents a modular multiport dc linear fast charger (LFC) for EVs, which uses a stationary, reconfigurable battery energy storage system (rBESS), a current-mode, digitally controlled bidirectional linear regulator (LR), and a contactor matrix. The LFC LR increases the overall station efficiency and eliminates the volume and cooling complexity associated with the high-power magnetics, high-voltage capacitors, and high-frequency switches of state-of-the-art (SotA) isolated dc–dc converters. Compared to a SotA isolated dc–dc converter, the LR experiences 83.6% less loss for the same rated EV charge power. Over a day of typical operation, a three-port LFC station is shown to have 28% less loss than a state-of-the-art BA-DCFC, with an average efficiency of 93.4%. The LFC achieves redundancy at the station, BESS, and LR levels while also integrating active balancing of state-of-charge/health without auxiliary switched-mode converters. The proposed system is verified using full-scale transient simulations and experimental results from a scaled, hardware-in-loop implementation with a custom LR and series-connected battery modules.
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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 | 0.001 | 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 | 0.001 |
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 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".