Thermal Profiling of Next-Generation Solid-State Batteries for Advanced Automotive Battery Management Systems
Bibliographic record
Abstract
Solid-state batteries (SSBs) are emerging as a promising alternative to conventional lithium-ion batteries due to their superior safety, energy density, and lifespan. However, understanding their thermal behavior under dynamic operating conditions is crucial for ensuring safety and performance, especially in e-mobility applications. This study presents a comparative thermal analysis of SSB and lithium nickel cobalt aluminum oxide (NCA) 21700 cells during charging under various ambient temperatures (0 °C, 25 °C, and 40 °C). Key metrics such as temperature gradients (ΔT/Δt) and differential temperature rise (ΔT) are evaluated to identify critical thermal behaviors. The results reveal that SSBs exhibit significantly higher ΔT and ΔT/Δt. While battery management systems (BMS) typically regulate absolute temperature rise (ΔT), this study highlights the importance of monitoring ΔT/Δt as a critical parameter for mitigating accelerated degradation and preventing thermal runaway. The findings contribute valuable insights toward developing robust thermal management strategies for next-generation battery 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".