Rapid Parameterization of Lithium-ion Batteries using Frequency Window Identification Technique for On-board Charge Control and Battery Management
Bibliographic record
Abstract
According to current industry practice for an onboard charger power electronic control and ingenious battery management in an electric vehicle, primarily an equivalent circuit model and data-driven techniques are used for state estimations such as state of charge, health, power, and temperature. Electrochemical impedance spectroscopy (EIS) is a non-destructive and one of the most accurate methods that has been widely used in laboratory environments for lithium-ion battery characterization and estimating the degradation and aging mechanisms. Typically, performing one EIS test over a frequency ranging from 10 kHz to 10 mHz takes 1 to 2 hours, making the method impractical to use in an onboard battery management system. Time requirement increases exponentially due to the failure of a test and the requirement of a wider frequency band. Therefore, to leverage the attractive benefit of EIS for a practical battery management system and to optimize the requirement of test time a novel approach to EIS frequency window identification is proposed in this paper. Through the experimental data and analysis, it is evident that the entire EIS spectrum can be divided into small frequency windows and the proposed frequency window could infer sufficient information to depict battery aging and degradation profile.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".