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 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".