Physics-Based and Control-Oriented Modeling of Diffusion-Induced Stress in Li-Ion Batteries
Bibliographic record
Abstract
Advanced battery management systems must take into account all key factors that contribute to significant degradation in order to ensure safe operation and to prolong battery useful life. One of the most important battery degradation mechanisms is the stress induced fracture. This has been observed on cathode electrodes during experiments, and may lead to partial or complete electrical isolation of the particles from the electrode. A physics-based stress model considering the nonlinear variation of lattice constant is presented and validated against experimental data. The effect of stress on diffusion dynamics is investigated. Stresses under the linear lattice constant and nonlinear lattice constant are compared. Stresses at different SOCs are also studied. SOC operating window which avoids the maximum stresses are determined. A physics-based, control-oriented, and highly computationally efficient model is also developed to predict the particle level stress in lithium ion batteries. This physics-based control-oriented model enables accurate estimation of stress levels inside the cathode particles while being computationally efficient enough to be implemented on an inexpensive micro-controller. With the accurate prediction of the stress level in the electrode particles, vehicles can be optimized to operate more efficiently with the aim of protecting battery health and prolonging battery life.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".