Design Optimization of a Multi-Level Converter Supercapacitor System for Electrochemical Impedance Spectroscopy in EV Fast-Charging Stations
Bibliographic record
Abstract
This paper presents a novel design method to optimize the volume of a high-power Electrochemical Impedance Spectroscopy (EIS) add-on system for Electric Vehicle (EV) Direct-Current Fast-Charging (DCFC) stations. The EIS system consists of a Supercapacitor (SC) energy storage system and an 800V bidirectional Flying Capacitor Multi-Level (FCML) dc-dc converter, which are co-designed to minimize the system volume. The contributions of this paper are the deterministic optimization algorithms for 1) SC energy storage systems and 2) dc-dc converters. The SC design algorithm minimizes the number of SC cells while ensuring the system meets the 4kW peak power and worst-case energy requirements for bidirectional power flow by generating dynamic power profiles. The FCML bidirectional dc-dc converter design algorithm optimizes the number of levels and phases, considering various commercially available power devices, component derating, and power losses for the converter. The algorithm generates a design space with optimized combinations of FCML-SC systems, revealing the global optimum design with a volume of 2.1L, which is 4 times smaller than the design employing an off-the-shelf converter and SC system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".