Assessment of Simplifications to a Pseudo–2D Electrochemical Model of Li-ion Batteries
Bibliographic record
Abstract
Lithium-ion batteries are ubiquitous in modern society. Their high power and energy density compared to other forms of electrochemical energy storage make them very popular in a wide range of applications [1]. To ensure safe, prolonged, and reliable operations, significant research effort has been put into understanding, modelling, and predicting the key limiting phenomena, which has led to various battery models with different levels of complexity and prediction capabilities [2]. This work focuses on implementing the pseudo-two-dimensional (P2D) model, the most widely accepted electrochemical model on lithium-ion batteries. The unparalleled prediction abilities of the P2D model, however, are over shadowed by its high complexity. Thus, much of this work focuses on model reduction to shorten effective simulation time. In the end, four model reductions have been identified and successfully implemented. Comparisons to the full model at 1C, 2C and 5C discharge rates are reported.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".