Reconfigurable Battery Control With State-of-Health Awareness for Service-Life Extension in DC Fast-Charge Stations
Bibliographic record
Abstract
Battery-Assisted DC Fast Charger (BA-DCFC) stations use stationary Battery Energy Storage Systems (BESS) to reduce grid-demand fees and reduce the costs associated with the operation and installation of Electric Vehicle (EV) fast charging stations. The service-life and reliability of a BA-DCFC station are significantly influenced by the longevity of the BESS, which is degraded by variations in cell capacity and impedance. BA-DCFC architectures with a reconfigurable BESS (rBESS) can enhance station efficiency and power density, compared to architectures with a fixed-configuration BESS (fBESS). Despite these advantages, the rBESS architecture inherently results in non-uniform cell utilization and aging. To mitigate these challenges, this paper proposes a control algorithm for the rBESS that actively reduces non-uniform aging while achieving higher efficiency than a benchmark fBESS design. A custom hardware-in-the-loop implementation of the rBESS BA-DCFC architecture is used to experimentally demonstrate the inherent non-uniform utilization of the rBESS despite high system-level efficiency during a 37-kW EV charging test. Long-term simulations, incorporating experimental data and aging models are used to compare the proposed control algorithm in the rBESS architecture against the fBESS architecture across various BESS capacities in terms of service-life and reliability. The proposed algorithm improves station service-life by 6.9–7.7% and increases EV charging capacity by up to 15.2%, compared to fBESS systems.
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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) | 0.001 | 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 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".