Electrochemical-thermal Analysis of High Capacity Li-ion Pouch Cell for Automotive Applications
Bibliographic record
Abstract
Latest regulatory trends implemented in order to limit emissions combined with research advances in alternative fuels have paved the road toward vehicle electrification. Major original equipment manufacturers (\\acrshort{OEM}s) have already marketed electric vehicles in large scale but apart from business strategies and policies, the real engineering problems must be addressed. Lithium-ion batteries are a promising technology for energy storage; however, their low energy density and complex electro-chemical nature, compared to fossil fuels, presents additional challenges. Their complex nature and strong temperature dependence during operation must be studied with additional accuracy, capable to predict their behavior. In this research, a pseudo two dimensional (\\acrshort{P2D}) electro-chemical model, coupled with a 3D thermal energy balance for a recent high capacity \\acrshort{NMC} pouch cell for automotive applications is developed. The electrochemical model with its temperature dependent parameters is validated at different temperatures and various discharge C-rates to accurately replicate the battery cell operational conditions. The sources of heat are distinguished and characterized via advanced electrochemical-modelling approach, in various battery operations and different thermal boundary conditions. For example, it was determined that the temperature rise during discharge at high C-rates, under natural convection, could result in thermal runaway, if managed incorrectly. Ohmic heat generation of current collectors and cell tabs is investigated and included. Hence, the thermal analysis provides insights on the current and voltage profiles causing the minimum thermal stress on the cell and the location of heat generation spatially and temporally during the battery discharge. Different modelling approximation of the cell are studied starting from the cell fundamental unit. This provides effective design considerations for the battery thermal management system (\\acrshort{BTMS}) to enhance performance, cycle life and safety of future electrified vehicle energy storage systems.
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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 | 0.000 |
| Open science | 0.000 | 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".