Optimal Discretization Approach to the Enhanced Single-Particle Model for Li-Ion Batteries
Bibliographic record
Abstract
Enhanced single-particle models (eSPMs) have been extensively studied in the development of advanced battery management systems for their accuracy and capability of tracking physical quantities, as well as for the reduced computational load. This article proposes an optimal discretization approach to model reduction for the eSPM using a particle swarm optimization algorithm. The battery diffusion dynamics were solved using different finite difference approaches, that is, an even discretization approach (baseline model) and an uneven discretization approach (optimized model). Because of the structure of the eSPM, internal nodes locations of the solid phase and the electrolyte phase are separately optimized. For the solid phase, a weighted multiobjective cost function is considered for achieving accurate surface and bulk concentration, aiming for accurate terminal-voltage and state-of-charge prediction. For the electrolyte phase, the optimization aims for accurate concentration prediction at the boundary of the electrolyte. The optimally reduced uneven discretization model can predict the battery dynamics accurately and with an improved computational cost: 1) the maximum voltage and SOC prediction errors demonstrated under dynamic current profiles are less than 2.73 mV and 0.37%, respectively, and 2) the number of states reduces by at least 11 times, leading to about a 64% reduction in the computation time.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".