Thermal Modeling of NMC811 Prismatic Battery for Fast Charging Profiles in EV Applications
Bibliographic record
Abstract
The thermal management of Li-Ion batteries is essential in battery-powered applications, especially in battery electric vehicles (BEVs), due to the high charge and discharge C rates involved. Designing robust cooling systems for battery packs in BEVs must rely on a high-fidelity thermal model that can track the cells' surface and core temperatures. This paper introduces the development of an accurate lumped thermal model for a high-power NMC811 prismatic battery, the model is based on the equivalent circuit model (ECM). Genetic algorithm optimization was used to fit the surface temperature measurements for multiple charging and discharging profiles on a two-node equivalent model to identify the thermal conductivity and capacitance of the cell. The model is able to estimate the surface temperature of the cell in multiple discharging profiles and drive cycles with a root mean square error (RMSE) as low as 0.1°C, whereas for the fast-charging profiles, that go up to 3.5 C, the RMSE achieved is 0.5°C.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".