Coupled Numerical Approach for Automotive Battery Pack Lifetime Estimates With Thermal Management
Bibliographic record
Abstract
This study combined a simple two-dimensional (2D) finite volume model (Kim model), which employs Ohm's law along with charge conservation over the electrodes and Butler–Volmer charge transfer kinetics for prismatic battery cells coupled with the single particle model (SPM) in order to model the thermal state of automotive battery packs. The objective here was to determine the effects of liquid cooling applied to the packs under standard driving cycles. A model developed by Kim provided a means for determining a nonuniform current distribution over the surface of the current collectors. The Kim model is based on the application of Ohm's law over a conducting medium, with empirical source terms representing current flowing into or out of an adjacent electrode layer. Here, a modeling advance is presented where empirical source terms in the Kim model were replaced with ones based on the chemistry and physics occurring inside the battery. As such, fundamental battery function was imparted to the model by integrating the SPM into the 2D finite volume Kim model. The 2D procedure described above was carried out on electrode sheets at different positions inside the cell, and determined thermal generation values that were mapped volumetrically into a heat transfer simulation, which, in turn, updated the electrochemical simulation. Capacity fade kinetics were determined by fitting experimental data to simulated results. With time-temperature profiles produced as described above for different pack cooling levels and varying degrees of cell degradation, a basic SPM simulation was then used with thermal overlays to estimate automotive cell life under various driving scenarios and various cooling levels. With these simulations, scenarios representing different thermal management regimes along with driving behavior were able to show the combined impact on automotive battery pack lifetimes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".