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 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 | 0.001 | 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 | 0.000 |
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".