The Effects of Test Profile on Lithium-ion Battery Equivalent-Circuit Model Parameterization Accuracy
Bibliographic record
Abstract
Accurate modelling of battery cells is crucial for the safety and longevity of the battery system. The equivalent-circuit battery model (ECM) is widely used because it provides good accuracy at a relatively low computational cost. The accuracy of an ECM depends primarily on the model parameters, which can be identified using optimization algorithms based on experimental data. This study investigates the effect of test profiles on the accuracy of the ECM by comparing (1) pulse tests of various lengths (2) two identification methods - direct optimization method and analytical method and (3) identification with pulse and drive cycle tests. The results suggest that optimization with an application-specific test profile (drive cycle tests for example) can provide the best accuracy. Parameters identified from the pulse test with short rests using the analytical method provided comparable accuracy, suggesting that the commonly used 30 to 120 minutes rest lengths may be unnecessary. Finally, to obtain a continuous relationship between the open-circuit voltage (OCV) and the cell’s state of charge (SOC), a polynomial is fitted to the OCV curve. Polynomials with orders from 5thto 21stare tested and it was found that 11thorder polynomial provided a good compromise between the complexity and model accuracy.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".